PUBLICATIONS. Journal of Geophysical Research: Atmospheres. Diagnosing MJO hindcast biases in NCAR CAM3 using nudging during the DYNAMO field campaign

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1 PUBLICATIONS Journal of Geophysical Research: Atmospheres RESEARCH ARTICLE Key Points: Evaluate MJO hindcast performance Relate MJO hindcast biases to model physics parameterizations Understand MJO mechanisms Diagnosing MJO hindcast biases in NCAR CAM3 using nudging during the DYNAMO field campaign Aneesh C. Subramanian 1 and Guang J. Zhang 1 1 Scripps Institution of Oceanography, University of California, San Diego, California, USA Correspondence to: G. J. Zhang, gzhang@ucsd.edu Citation: Subramanian, A. C., and G. J. Zhang (2014), Diagnosing MJO hindcast biases in NCAR CAM3 using nudging during the DYNAMO field campaign, J. Geophys. Res. Atmos., 119, , doi:. Received 16 DEC 2013 Accepted 28 MAY 2014 Accepted article online 2 JUN 2014 Published online 26 JUN 2014 Abstract This study evaluates the Madden Julian Oscillation (MJO) hindcast skill and investigates the hindcast biases in the dynamic and thermodynamic fields of the National Center for Atmospheric Research Community Atmosphere Model version 3. The analysis is based on the October 2011 MJO event observed during the Dynamics of the Madden Julian Oscillation field campaign. The model captures the MJO initiation but, compared to the observations, the hindcast has a faster MJO phase speed, a dry relative humidity bias, a stronger zonal wind shear, and a weaker MJO peak amplitude. The MJO hindcast is then nudged toward the European Centre for Medium-Range Weather Forecast Reanalysis fields of temperature, specific humidity, horizontal winds, and surface pressure. The nudging tendencies highlight the model physics parameterization biases, such as not enough convective diabatic heating during the MJO initiation, not enough upper tropospheric stratiform condensation, and lower tropospheric reevaporation during the mature and decay phases and a strong zonal wind shear during the MJO evolution. To determine the role of temperature, specific humidity, and horizontal winds in the model physics parameterization errors, six additional nudging experiments are carried out, with either one or two of the fields allowed to evolve freely while the othersare nudged. Resultsshowthatconvection andprecipitation increase when temperature or specific humidity are unconstrained and decrease when horizontal winds evolve freely or temperature alone is constrained to reanalysis. Budget analysis of moist static energy shows that the nudging tendency compensates for different process biases during different MJO phases. The diagnosis of such nudging tendencies provides a unique objective way to identify model physics biases, which usefully guides the model physics parameterization development. 1. Introduction The Madden Julian Oscillation (MJO) is the dominant mode of intraseasonal variability in the tropical atmosphere [Madden and Julian, 1994; Zhang, 2005]. It interacts with many other climate phenomena across different temporal and spatial scales [Waliser and Lau, 2005] and has a wide range of impacts on the Earth s climate system. The MJO interacts with El Niño [e.g., Marshall et al., 2009; Hendon et al., 2007; Zavala-Garay et al., 2005; Bergman et al., 2001; Kessler, 2001; Takayabu et al., 1999] and North Atlantic Oscillation [Cassou, 2008] and impacts the onset and break of the Indian and Australian summer monsoons [e.g., Yasunari, 1979; Wheeler and McBride, 2005]. Practically, it would be difficult to simulate these various climate modes well and predict their evolution along with the Earth s climate without simulating the MJO accurately in models. Improving MJO simulations in climate models over the past few decades has necessitated and helped in improved understanding of the MJO dynamics and its interaction with other climate modes. The MJO has a time scale that is in between the classically studied time scales of weather and climate. It plays a prominent role in global climate and is a critical component of extended-range (15 20 days) forecasts by operational weather and climate providers [Lau and Waliser, 2012]. Real-time forecasts of the MJO are becoming increasingly important for potential improvements in forecasting for this time range. While MJO prediction studies using operational dynamical models have increased in recent years [Hendon, 2000; Savage and Milton, 2007; Vitart et al., 2007; Gottschalck et al., 2010], the limitations in forecast skill of various climate models and insufficient tools to appropriately diagnose and correct them have complicated the evaluation of the utility of the forecasts. Hence, simulating and predicting the MJO have received increased interest recently [Sperber and Waliser, 2008; Kim et al., 2009; Gottschalck et al., 2010]. Although significant progress has been made in understanding the MJO predictability and MJO simulations in global climate models (GCMs) SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7231

2 [Waliser et al., 2003; Zhang et al., 2006; Subramanian et al., 2011; Hirons et al., 2012; Benedict et al., 2013], fundamental problems remain to puzzle the community as to what processes are responsible for realistic or poor MJO simulations in a GCM [Zhang et al., 2013]. A number of possible causes have been suggested for poor MJO simulations. These include incomplete modeling of shallow to deep convection transition [Zhang and Song, 2009; Cai et al., 2013], incorrect vertical heating profiles [Lappen and Schumacher, 2012, 2014], lack of self-suppression processes [Zhou et al., 2012], and inadequate closure assumptions in parameterized deep convection [Maloney and Hartmann, 2001; Zhang and Mu, 2005; Lin et al., 2004, 2006]. For example, Lin et al. [2004] show that the observed top-heavy diabatic heating structure corresponding to stratiform precipitation is important to MJO development, and it is largely missing in GCMs. A recent field campaign, Cooperative Indian Ocean Experiment on Intraseasonal Variability in Year/Dynamics of the MJO (CINDY/DYNAMO), was designed to collect in situ observations in the tropical Indian Ocean region, during the period from October 2011 to February 2012, to aid the development of numerical models and advance understanding of the structure and statistics of convective clouds and their interaction with the large-scale environment during MJO initiation. Yoneyama et al. [2013] give a detailed discussion on the background, scientific rationale, experimental design, operation, and preliminary results of the field campaign. The overarching goal of the CINDY/DYNAMO field campaign is to expedite our understanding of processes key to MJO initiation over the Indian Ocean and to improve simulation and prediction of the MJO. Four intraseasonal and eastward propagating large-scale convective events occurred over the tropical Indian Ocean during the period of the DYNAMO field campaign. They were in late October, late November, late December of 2011, and March 2012, respectively. The October and November events were mainly manifest in the Indian Ocean and Maritime Continent and barely had an anomalous convective signal in the Pacific Ocean, partially because of the La Niña conditions there. Both events had a strong propagating convective signal over the Indian Ocean, and the November event has been shown to be a continuation of the circumnavigating signal of the October MJO event [Gottschalck et al., 2013; Yoneyama et al., 2013]. The goal of the present paper is twofold. First, we evaluate the MJO forecast skill and biases in the dynamic and thermodynamic fields of the Community Atmosphere Model 3.0 with a revised Zhang McFarlane deep convection scheme [Zhang, 2002] for the October MJO event observed during the DYNAMO campaign. Second, we diagnose the model bias in terms of deficiencies in model physics parameterization by nudging the model fields toward the ECMWF Reanalysis and analyzing the nudging tendencies in the dynamic and thermodynamic fields. The experiment setup is described in section 2. The MJO forecast skill assessment is described in section 3. The results from the nudging experiments in diagnosing biases in model physics parameterization are presented in section 4. Section 5 summarizes the paper. 2. Model Experiments and Validation Data 2.1. Model and Experimental Setup A modified National Center for Atmospheric Research (NCAR) CAM3 model is used in this study. It has a horizontal resolution of T42, which is equivalent to in Gaussian grid, and a vertical resolution of 26 levels from the surface to 4 mb. In the standard version of the CAM3, deep convection is parameterized by the Zhang McFarlane scheme [Zhang and McFarlane, 1995] and shallow convection by the Hack et al. [1994] scheme. However, the standard CAM3 does not simulate the MJO well [Hartmann and Maloney, 2001]. Thus, in this study we use a revised Zhang McFarlane scheme, which produces realistic MJOs [Zhang and Mu, 2005] in climate simulations. The modifications to the Zhang McFarlane scheme and their effect on the simulation of the tropical precipitation climatology are described by Zhang and Mu [2005]. Here we will outline the main changes. Three changes are made to the Zhang McFarlane scheme in the modification. First, a new closure based on the work of Zhang [2002] is used to replace the convective available potential energy-based closure in the original Zhang McFarlane scheme. Second, a relative humidity threshold of 80% is included in the scheme for the convection trigger. The third change to the Zhang McFarlane scheme is the removal of the restriction in the code that convection only originates from below the planetary boundary layer (PBL) top. This allows midlevel convection with a cloud base above the PBL top to be included in the Zhang McFarlane scheme. The first set of model experiments analyzed in this study is set up as initialized hindcasts for a period of 30 days. Two model hindcasts are performed, one initialized on 6 October 2011 and one on 16 October SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7232

3 Atmospheric initial conditions for the CAM3 are obtained by regridding the European Centre for Medium- Range Weather Forecast (ECMWF) Reanalysis (ERA Interim or ERAI) [Dee et al., 2011] data. The hindcast experiments were set up for the October MJO event with the surface forcing from daily sea surface temperature, which is also obtained from ERAI for the period of the model run. The second set of experiments involves nudging CAM3 forecast runs toward the ERAI field of temperature, humidity, and horizontal wind fields on every grid point in the model. The ERAI data have assimilated the DYNAMO field observations. We design the experiments to either nudge all the fields simultaneously or let one of them evolve freely or nudge one field only in the model. These experiments are done with the model initialized on 6 October 2011, 9 days prior to the first convective activity of the October MJO event. The rationale for this experimental design is to separate the MJO simulation biases due to model dynamics from those due to model physics. In an interactive system such as the global climate model, model dynamics respond to forcing from model physics, and model physics in turn feeds back to model dynamics. The deficiencies in the model physics tendencies can be identified from the nudging tendencies needed to keep the model state in check by holding the dynamic and thermodynamic states close to observed fields. For this purpose, a nudging term is added to the prognostic equations of temperature, moisture, and winds. It represents a Newtonian relaxation technique, as used in early numerical forecast and data assimilation models [Hoke and Anthes, 1976]. For a model predicted variable X, the nudging term is given by X t ¼ X X a ; (1) τ where X a is the observed or ECMWF reanalysis field. Parameter τ is the relaxation time scale representing the e-folding time over which the model bias will be reduced in the absence of any other forcing and is set to 1 h in this study. The nudging technique has been used as an evaluation technique for GCMs [Jeuken et al., 1996] and as a diagnostic tool for model tendency errors [Machenhauer and Kirchner, 2000]. More recently, Hagos et al. [2011] analyze the effect of moisture nudging in a regional model to identify limitations of the model convective parameterization. Our implementation technique for CAM3 is adapted from an earlier version (CAM2) used to initialize forecast simulations for evaluating the growth of model errors on short time scales [Boyle, 2005] Observational Data Six-hourly data from the ERAI global reanalysis for the period of the MJO hindcast (6 October to 3 November 2011) is used to nudge the model. The reanalysis data have a horizontal resolution of and is regridded onto the model grid, and the daily means are computed for comparison to the model daily mean fields. The nudging experiments use the global zonal and meridional winds, humidity, and temperature at all vertical levels and surface pressure from the ERAI reanalysis regridded to the model grid to compute the nudging tendencies. The daily rainfall data ( ) from the Tropical Rainfall Measuring Mission (TRMM) multisatellite precipitation analysis [Huffman et al., 2007] for the same period are used for model forecast validation and for identifying the MJO signal in the observed precipitation variable. Previous studies have shown that TRMM data can be used to diagnose MJO characteristics [Morita et al., 2006]. The all-season real-time multivariate MJO (RMM) index of Wheeler and Hendon [2004] is used to evaluate the model MJO forecast. Most of the diagnostic analysis for the model runs is done over the northern DYNAMO array region, which extends from 72 E to 80 E and 0 N to 7.5 N (Figure 1). The DYNAMO field campaign had six observation sites forming a northern array (north of the equator) and a southern array (south of the equator). The northern array was formed by R/V Revelle at 0, 80.5 E and three land sites at Gan Island (0.7 S, 73.2 E), Hulhule Island of Male Atoll (4.2 N, 73.5 E), and Colombo (6.9 N, 79.8 E). 3. DYNAMO October MJO Hindcast A series of hindcasts are performed for the October MJO event observed during the DYNAMO field campaign, and the model hindcasts are compared to the observations. For comparison, we identify the MJO using both the precipitation signal in TRMM rainfall observations and the RMM indices described in Wheeler and Hendon [2004] (WH04 henceforth). To identify the propagating MJO signal using precipitation, we take the SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7233

4 Figure 1. The DYNAMO northern sounding array for the period October December 2011 consisted of the four stations indicated here: Malé, Gan, Revelle, and Colombo. The model fields are averaged over this domain for comparison with observations. The colors here show the surface temperature on 6 October 2011 from the ECMWF Reanalysis. approach of Ling et al. [2014]. In a time-longitude Hovmöller diagram of precipitation averaged over a latitudinal belt (15 S 15 N in this study), a set of straight lines are drawn between two longitudinal extremes (55 95 E in this study, which is the farthest longitude of the MJO precipitation signal on 4 November 2011), each with a different slope, and the average precipitation along each line is calculated. This is repeated for different dates. Among this set of lines with different slopes and starting dates, the first one with the largest averaged precipitation represents the MJO event (dashed line in Figure 2b points to the average slope or phase speed and average precipitation for this line). This line is referred to in literature as the MJO track. The slope of this MJO track line measures the eastward propagation phase speed, while the average precipitation of this track measures the strength of precipitation for this MJO event. Using this method, the phase speed and Figure 2. Precipitation (color, mm/d) and 850 hpa zonal wind anomalies (contours, m/s) averaged from 15 S to 15 N for the period from 6 October to 4 November. (a) From observations (precipitation from TRMM data set, zonal wind anomalies from ECMWF Reanalysis). (c and e) From CAM3 hindcasts initialized on 6 October and 16 October, respectively. The contour interval is 2 m/s, and the dashed contours indicate negative values. (b, d, and f) Time-phase speed diagram of the daily precipitation (mm/d) from TRMM observations (Figure 2a) and the two CAM3 hindcasts, respectively. SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7234

5 RMM MJO Phase Diagram RMM Oct06 Oct16 Figure 3. Wheeler and Hendon [2004] real-time multivariate MJO (RMM) index for the period 6 October to 4 November 2011: from observations (black dashed line) and from CAM3 hindcast runs initialized on 6 October (red line) and 16 October (blue line). The black squares indicate the CAM3 initial MJO phase and amplitude for each of the hindcast runs. The triangles indicate the observed MJO phase and amplitude on 6 October (red) and 16 October (blue). amplitude of the MJO event can be quantified objectively even for a short-term forecast. As can be seen in the observed precipitation (Figure 2a), the October 2011 MJO convection started on 15 October in the West Indian Ocean (60 E 65 E), but did not propagate until 20 October. Afterward, it propagated eastward at about 5 m/s (Figure 2b), with the maximum precipitation occurring on 30 October 2011 at 90 E. The precipitation of the model hindcasts for two different initial dates is shown in Figures 2c and 2e. The first hindcast starts on 6 October (Figure 2c) and the second one on 16 October (Figure 2e). Both hindcasts show two eastward propagating bands of precipitation that initiate in the Indian Ocean and propagate at an approximate phase speed of 6 7 m/s. The first event initiates on 14 October, approximately on the same day as the beginning of precipitation in the observations for this region, but it propagates eastward immediately. This propagating band of precipitation in the model is accompanied by convergent 850 hpa zonal wind anomalies (shown as contours in Figures 2c and 2e), with easterly wind ahead of convection and westerly wind behind it. The second event initiates around 21 October and appears to correspond to the increased eastward propagation of the observed MJO. These eastward propagating signals in the model appear to be more akin to moist Kelvin waves. Nevertheless, the initiation of the event is captured well by the model hindcasts initialized even 9 days prior to the initiation of MJO convection. The second hindcast started after the initiation of MJO convection in the region. Again, it shows two eastward propagating bands of precipitation very similar to the first experiment. The MJO initiation and propagation can also be described using the RMM indices (referred to as RMM1 and RMM2) from WH04. The RMM indices together with their associated spatial structures capture the key broadscale features of the MJO and are thus a suitable starting point for assessment of forecast skill. RMM1 and RMM2 vary mostly on the intraseasonal time scale of the MJO only and are approximately in quadrature with peak correlation at a lag of about 12 days. Figure 3 shows the evolution of the observed and hindcast October 2011 MJOs on the RMM phase diagram. The model forecasts of zonal winds at 200 hpa, 850 hpa, and outgoing longwave radiation (OLR) averaged between 15 S and 15 N are projected onto the observed empirical orthogonal functions (EOFs) to compute the model RMM1 and RMM2. The observed MJO evolution is plotted as the black line with dates indicated by the numbers, which shows that the MJO had its peak amplitude on 18 October. The RMM index weighs more on circulation fields [Straub, 2013; Kiladis et al., 2013] than the OLR. This could be a reason why the index peaks on 18 October even though the precipitation does not peak on this day. The model events for two of the hindcast experiments are plotted in red and blue with the corresponding initial dates of the hindcasts marked as black squares on the phase diagram. The triangles on the black line indicate the state of the observed MJO at the beginning of the model hindcasts. Note that the differences in the positions between the squares and the corresponding triangles for the initial dates are due to the fact that the RMM1 and RMM2 indices for the observed MJO are computed using the National Centers for Environmental Prediction Reanalysis data, while the model hindcasts use ERAI. In the hindcasts, the MJO event initiates at about the right date and propagates eastward (counterclockwise in the phase diagram), but the peak amplitude in the model is significantly weaker than the observed maximum amplitude. This can also be related to the fast propagation speed of the hindcast MJO across the Indian Ocean region (as seen in Figure 2), which corresponds to the bottom left quadrant of the phase diagram. The SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7235

6 MJO event did not last long enough to amplify in the Indian Ocean to reproduce the RMM index amplitude, as seen in the observations, either due to the high-phase speed in the model hindcasts or possibly because one of the fields used in computing the RMM values briefly projected strongly onto one of the observed EOF modes and then rapidly decayed. To further evaluate the model hindcasts of the observed MJO, we compare the vertical cross sections of humidity, winds, and tendencies of moist processes. The time-height cross section of relative humidity (RH) from the ERAI averaged over the northern DYNAMO array is shown in Figure 4a. Prior to the initiation of MJO convection in the observations (before 13 October), the free troposphere above the boundary layer is very dry, with the RH as low as 15% at 400 hpa on 9 October. There is a gradual increase of the depth of the moist layer (RH > 70%) over the MJO initiation period from 13 October to 17 October in the Indian Ocean region. This then transitions to the entire atmospheric column being moistened and remains so for the MJO active phase until 31 October. Afterward, the atmospheric column experiences abrupt drying (with RH going below 65% in the midtroposphere by 3 November). The model hindcast shows a similar general slow transition from shallow to deep moistening (Figure 4b), when the MJO becomes active in the region. However, there are several important differences. First, before the MJO initiation, the moist layer in the lower troposphere is much deeper than in the ERAI data. Second, after the initiation of MJO convection, the atmosphere is not as moist as in the ERAI data, particularly in the lower troposphere below 600 hpa, and the middle troposphere remains dry until 22 October. Third, in the decay stage of the MJO, the drying in the upper and middle troposphere starts too early. The moistening of the deep atmospheric column (Figure 4b) corresponding to the MJO event in the model lasts for a much shorter period than that in the observed MJO. This is consistent with the event propagating at a faster phase speed than the observed MJO and hence lasts only a few days in the Indian Ocean region (as seen in Figure 2c). For temperature and moisture fields, since there is a large vertical variation, we show the anomalies from the 30 day (6 October to 4 November) average ERAI data in Figures 4c and 4e, respectively. The atmosphere is anomalously dry and warm in the low levels prior to 16 October, when the convection initiates in the region. The upper atmosphere is cold during this period. After 16 October, the entire atmospheric column becomes anomalously moist and the lower atmosphere is colder, while the upper atmosphere is warmer during the peak convection period. Figures 4d and 4f show the difference between the specific humidity and temperature field of CAM3 hindcast and ERAI, respectively. The free troposphere in CAM3 has a moist bias from 10 October until 16 October, which corresponds to the period when the model s relative humidity in the lower atmosphere is higher than that in the ERAI. After this period, the model has a dry bias especially in the lower atmosphere during the peak convection period. The temperature bias overall is negative above the boundary layer, especially in the upper atmosphere during the convective period in the model from 16 October to the end of the simulation. The vertical profile of the observed zonal and meridional wind from radiosonde measurements in the same region is shown in Figures 4g and 4i, respectively. The zonal wind profile shows enhanced easterlies developing aloft during the initiation and mature phase of the MJO event accompanied by a downward shift in the wind-speed maxima with time. The westerly wind in this period decreases, and its vertical extent also decreases from 400 hpa on 12 October to below 800 hpa after 17 October. This is followed by an increase in westerlies and their vertical extent, as seen near the end of October and beyond in Figure 4g. The drying in the lower troposphere along with an increase in westerlies toward the end of the active phase of the MJO was also observed during Tropical Ocean Global Atmosphere Coupled Ocean-Atmosphere Response Experiment (TOGA COARE) [Lin and Johnson, 1996]. The meridional winds show alternating patterns of northerlies and southerlies with a period of 2 3 days during the initiation and mature phase of the MJO, consistent with the 2 day cycle of the observed precipitation during this period [Johnson and Ciesielski, 2013]. The model hindcast vertical profiles of the zonal and meridional winds are shown in Figures 4h and 4j, respectively. The zonal wind shear in the model is too strong during both the initiation (15 18 October) and the peak of the model event (21 24 October), with strong westerlies below 500 hpa and easterlies aloft. The easterlies aloft descend as the model event evolves similar to that in the observations, but they are not sustained as long as seen in the observations due to the faster phase speed of the event in the model. The strong westerly in a deep layer from the surface to 600 hpa on 24 October corresponds to the start of the second precipitation band. The meridional velocities in the model also show some alternating patterns of northerlies and southerlies above 800 hpa SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7236

7 Figure 4. Height-time cross section of (a) relative humidity (%) from ECMWF Reanalysis data set, (b) relative humidity (%) from the CAM3 hindcast initialized on 6 October, (c and e) specific humidity and temperature anomalies from the mean over the hindcast period of ECMWF Reanalysis data set, (d and f) specific humidity and temperature differences of the CAM3 hindcast from the ECMWF Reanalysis fields, (g and i) zonal and meridional winds (m/s) from the radiosonde data collected during the DYNAMO field campaign, and (h and j) zonal and meridional winds (m/s) from the CAM3 hindcast. All the fields (Figures 4a 4j) are averaged over the northern sounding array (NSA, 72 E 80 o E, 0 N 7.5 N) region. during the event in the model. The low-level meridional winds tend to remain southerly and strong throughout the period, unlike the oscillating patterns in the observations. To further analyze the convective signature of the MJO in the CAM3 forecasts, the diabatic heating of the model in the Indian Ocean region is shown in Figure 5. Figure 5d shows the mean OLR over the northern array region for the period 10 October to 24 October. The OLR begins to dip with the onset of the MJO convection starting on 16 October, and the minima is reached on 21 October. Figure 5a shows the diabatic heating due SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7237

8 Figure 5. Height-time cross section of (a) large-scale condensational heating (K/d), (b) parameterized convective heating (K/d), and (c) convective mass flux ( 10 2 kg m 2 s 1 ). (d) Mean outgoing longwave radiation. All the fields (Figures 5a 5d) are from the CAM3 hindcast initialized on 6 October, and they are averaged over the NSA region (72 E 80 E, 0 N 7.5 N). to large-scale condensation, which depicts a first baroclinic structure with heating above due to large-scale condensation in the upper levels and cooling below 600 hpa, indicative of large-scale reevaporation of precipitation. Figure 5b shows the heating due to parameterized convection. The heating in the low levels due to shallow convection remains for over a week before the deep heating of the entire atmospheric column occurs with the onset of deep convection on 18 October. The convective heating is top heavy when the MJO is active. The transition from shallow to deep convection is also revealed in the convective mass flux shown in Figure 5c before the initiation of the MJO. During October, there is a deepening in convective mass flux. The transition of shallow to deep convection over a few days is also consistent with the gradual moistening of the deeper atmospheric column from 15 October to 20 October as seen in the RH plots in Figure 4b. The transition from shallow to deep convective heating in the model occurs more abruptly on 18 October than seen in the observations and modeling studies [Benedict and Randall, 2007;Zhu et al., 2009]. After 20 October, the maximum convective mass flux is located in the upper troposphere, producing a top-heavy diabatic heating. This feature then dissipates within 2 days (Figure 5c) before the next event begins on 24 October. To summarize, the CAM3 is able to simulate the initiation of the October DYNAMO MJO event. However, there are important biases; they are the fast phase speed of the event, weak moistening of the deep troposphere compared to observations, quick transition from deep to no convection in the mature phase, and biases in precipitation amplitude and winds during the event. 4. Nudging Experiments In this section, we carry out a series of nudging experiments to understand the model biases further and gain insights into biases in model physics. For this purpose, we explore the use of nudging technique, which provides an effective way of diagnosing model physics errors. To demonstrate this, we note that the model equation including nudging can be written as (using specific humidity as an example) q t model ¼ q t dyn_model þ q t þ phys_model q t ; (2) nudge where the model q change is due to large-scale advective or dynamic forcing ( q/ t) dyn _ model,model physics parameterizations ( q/ t) phys _ model, and relaxation adjustment ( q/ t) nudge. The observed humidity field q satisfies SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7238

9 30 25 TRMM ERAI ALLVAR Precipitation q ¼ t obs q t þ dyn_obs q t ; (3) phys_obs where ( q/ t) phys _ obs represents the humidity (q) tendency from the actual physical processes in the atmosphere that accompany the observed moisture 10/10 10/16 10/22 time 10/28 11/03 evolution. If the nudging time scale is small enough, the modeled q field should be very close to the observed field. Thus, the Figure 6. Rainfall from TRMM (black line), ECMWF Reanalysis (red line), advective tendency in the model should and CAM3 nudging hindcast (blue line). All the fields are averaged over also be close to its observational the NSA region (72 E 80 E, 0 N 7.5 N). counterpart, since the winds and humidity fields are held close to the observations, assuming errors in the model numerics are small [Mapes and Bacmeister, 2012]. Subtracting equation (3) from equation (2) gives q ¼ q q t t t nudge phys_obs phys_model Hence, the model s nudging tendency measures the deficiency of the model physics parameterizations (deviation of the model physical tendency from the actual physical tendency) when the dynamic forcing term of the model is equal to that in the observations. In other words, it shows what is missing in the model physics tendency that is needed in order for the model to produce the observed thermodynamic and circulation fields. The nudged variables in the present study are temperature, moisture, horizontal u and v winds, and surface pressure. The ERAI reanalysis data were available every 6 h, and these were interpolated using a cubic polynomial to the 20 min time step of the model. This degree of nonlinearity was determined sufficient to adequately drive the model, after experimenting with interpolation schemes ranging from linear to quintic. Seven sets of nudging experiments were conducted. All of them were initialized from 6 October 2011 with the same fields as in the MJO forecast experiments. In the first nudging experiment (ALLVAR), all five variables: temperature, humidity, u, v winds, and surface pressure, are nudged. In the second experiment, four fields are nudged, leaving specific humidity evolving freely (NOHUM). The third experiment lets temperature evolve freely (NOTEMP), the fourth experiment allows horizontal winds (u and v) to be unconstrained (NOVEL). We let both u and v evolve freely in the fourth experiment instead of constraining one of them, because they are related through mass continuity. In addition to the above four experiments, we carry out three more experiments, nudging only one field a time. The fifth experiment nudges humidity only (HUMONLY), the sixth experiment nudges temperature only (TEMPONLY), and the last experiment nudges the horizontal winds (u and v) only (VELONLY). Our main goal from the nudging experiments is to learn the biases in the model physics parameterization, mainly related to the free tropospheric processes. Hence, we nudge the surface pressure field toward the reanalysis to keep the surface winds and surface fluxes consistent across the experiments. The time series of precipitation averaged over the northern DYNAMO sounding array is shown in Figure 6 for the TRMM observations, ERAI, and the ALLVAR nudged simulation. Before the MJO initiation on 15 October, the TRMM observations show very little precipitation in the northern DYNAMO domain. Heavy precipitation starts after 20 October and tapers down after 31 October. On the other hand, the ERAI precipitation shows significantly more precipitation from 9 to 21 October and after 30 October. The ALLVAR run reproduces the TRMM observations very well, including the 2 day precipitation cycle, suggesting that given the correct dynamic and thermodynamic states, the revised Zhang McFarlane convective parameterization can reproduce the observed convection and precipitation realistically. Interestingly, although the CAM3 ALLVAR run nudges its temperature, moisture, and wind fields toward the ERAI fields, it produces a precipitation field closer to the TRMM observations than to the ERAI precipitation. One likely reason is that ERAI precipitation is produced with its own model physical parameterizations. The evolution of the MJO for each of these nudging experiments is shown in the precipitation fields in Figure 7, along with the observed precipitation from TRMM (Figure 7a) and precipitation from ERAI (4) SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7239

10 Figure 7. Longitude-time Hovmöller plot of precipitation (in mm/d) for (a) TRMM observations, (b) ECMWF Reanalysis, (c) ALLVAR experiment, (d) NOHUM experiment, (e) NOTEMP experiment, (f) NOVEL experiment, (e) HUMONLY experiment, (f) TEMPONLY experiment, and (g) VELONLY experiment. The precipitation fields are from 30 E to 150 E and are averaged between 15 S and 15 N. (Figure 7b) for comparison. Compared to the TRMM observations, the ERAI precipitation is of similar magnitude but has a broader spatial scale. Over the DYNAMO longitudes (72 to 80 E), there is more precipitation from 12 to 22 October, as seen in Figure 6. The total precipitation field for the ALLVAR case (Figure 7c) shows a very similar spatial pattern to the TRMM observed precipitation although with a smaller magnitude in precipitation west of the DYNAMO domain during the MJO initiation to maturing phase (16 October to 22 October). The longitudinal scale of the precipitation field is close to that of the TRMM precipitation. The phase speed of the MJO in this experiment is very close to the observed MJO phase speed of 5 m/s as computed using the method described in section 3. Figure 8a shows that the large-scale precipitation is only about 10 20% (shown in contours) of the total precipitation (Figure 7c) even during the mature phase of the MJO. Thus, convective precipitation dominates the MJO precipitation in the ALLVAR experiment. Previous studies have shown that the stratiform precipitation allows for the grid-scale precipitation heating to interact with large-scale low-frequency waves in the tropics, which is postulated to play a critical role in the development and maintenance of the MJO [Wang, 2005; Fu and Wang, 2009]. In our case, since the large-scale fields are nudged, the lack of sufficient stratiform precipitation does not seem to affect the maintenance and propagation of the MJO. This shows that on MJO scale when the model temperature, humidity, and wind fields are held close to the reanalysis fields, the convective parameterization scheme performs reasonably well in producing the MJO precipitation. When humidity in the model is allowed to evolve without nudging (NOHUM), the maximum precipitation for the MJO event is increased, especially during the MJO maturing phase (16 October to 22 October; Figure 7d). Also, the mean amplitude shows an increase, and the phase speed is about 4 m/s for this case. There is significantly more large-scale precipitation during all the phases of the MJO for the NOHUM case, yet the percentage of large scale is still about 10 20% as in the ALLVAR case (Figure 8b). The precipitation field for the HUMONLY experiment shows the maximum amplitude of precipitation among all the cases (Figure 7g) and still has a phase speed of 5 m/s similar to the observed MJO. The percentage of large-scale precipitation is also the largest in this case (about 30 40%; Figure 8c). SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7240

11 Figure 8. Longitude-time Hovmöller plot of large-scale precipitation (in color, mm/d) and percentage ratio with respect to the total precipitation (in line contours, %) for (a) ALLVAR experiment, (b) NOHUM experiment, (c) NOTEMP experiment, (d) NOVEL experiment, (e) HUMONLY experiment, (f) TEMPONLY experiment, and (g) VELONLY experiment. The large-scale precipitation fields are from 30 E to 150 E and are averaged between 15 S and 15 N. The precipitation field for the NOTEMP case (Figure 7e) shows the second strongest precipitation signal in the model MJO event among all the nudging experiments with a phase speed of about 4 m/s. The corresponding large-scale precipitation (Figure 8d) also has a higher contribution (more than 20 30%) to the total precipitation in this case compared to the other nudging experiments except HUMONLY. Comparatively, the TEMPONLY case has the weakest MJO signal (Figure 7h) among all the cases with a fast phase speed of close to 8 m/s. The large-scale precipitation is also very weak during all the phases (Figure 8e). Hence, the lack of sufficient large-scale precipitation and heating could hinder the coupling between convection and the lowfrequency large-scale atmospheric waves [Fu and Wang, 2009; Wang, 2005]. SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7241

12 Pressure (hpa) Diabatic Heating due to Moist Processes ALLVAR NOHUM NOTEMP NOVEL ONLYHUM ONLYTEMP ONLYVEL Diabatic Heating Tendency (K/day) Figure 9. Diabatic heating tendency (K/d) due to moist processes for the ALLVAR experiment (black), NOHUM experiment (green), NOTEMP experiment (blue), NOVEL experiment (magenta), HUMONLY experiment (yellow), TEMPONLY experiment (cyan), and VELONLY experiment (red). The field is averaged over the NSA region (72 E 80 E, 0 N 7.5 N) and from 15 October to 3 November. When u and v winds in the model are not nudged (NOVEL experiment), the MJO precipitation signal (Figure 7f) is much weaker during the initiation and maturing phases. The model shows a strong enough propagating MJO signal only during the fully mature phase, when the large-scale precipitation also starts to appear (Figure 8d). The phase speed of the MJO signal is similar to the ALLVAR case of about 5 m/s. In contrast, the VELONLY case has one of the best representation of the MJO signals compared to the observations, especially in terms of magnitude, as seen in Figure 7i and Figure 8i. The large-scale precipitation in this case is similar to the ALLVAR case, accounting for about 10 20% of the total precipitation (Figure 9g). Clearly, getting the atmospheric circulation or wind fields right is very critical to a realistic MJO forecast. Consistent with the precipitation variation, the diabatic heating from convection also shows increased convection in the HUMONLY, NOHUM, and NOTEMP experiments and reduced convection in the TEMPONLY and NOVEL experiments. Figure 9 shows the vertical profiles of convective heating averaged over the domain of northern DYNAMO array (as shown in Figure 1) and the time period of 15 October to 3 November for the seven nudging experiments. The heating profiles for HUMONLY show an increase of 100% in the maximum, while both NOTEMP and NOHUM show an increase of about 10 20% in the maximum, as compared to the ALLVAR case. The TEMPONLY experiment has the least convective heating as expected from the weak precipitation signal, and the NOVEL experiment also has a weaker maximum heating compared to the ALLVAR. Note that all the experiments have maximum diabatic heating between 400 and 500 hpa, and there is no sign of top-heavy structure. This is consistent with the levels of maximum heating from observations as noted in the MJO diabatic heating during the TOGA COARE [Lin et al., 2004]. Analyzing the model biases as compared to the reanalyses can provide more insights into the model simulation deficiencies of the dynamic and thermodynamic features. From equation (4), the model bias in physical parameterization tendency, ( q/ t) phys _ model ( q/ t) phys _ obs, is just the negative of the nudging tendencies ( q/ t) nudge in the ALLVAR experiment. Since the simulation period is over an MJO life cycle and is convectively active, we expect the model physics biases to be mostly associated with convective parameterization. Figure 10 shows the model biases from the ALLVAR experiment for the four nudged variables (temperature, humidity, zonal wind, and meridional wind) along with the convective and advective heating tendencies, all averaged over the northern DYNAMO array region. The convective and advective heating tendencies are the two largest terms in the temperature budget and are analyzed to help interpret the nudging tendencies. The temperature and moisture tendency biases (Figures 10a and 10b, respectively) show that there are cooling and moistening biases beginning in the lower troposphere on 12 October and expand to the deep troposphere a few days later. This suggests that before the start of the MJO convection (13 October to 15 October), there is not enough shallow/congestus convective heating and drying, and during the MJO initiation (15 October to 20 October), there is not enough deep convective heating and drying. This is supported by the convective heating plot (Figure 10c), which shows little convective activity during this time period. By 21 October, there are mostly cooling and moistening biases above 700 hpa and heating and drying biases below (Figures 10a and 10b). In the MJO mature phase (21 October to 30 October), the vertical structure of the temperature and moisture tendency biases is similar to a first baroclinic structure. Except a few isolated heating tendency biases (from 600 hpa to 300 hpa) during the days of strong SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7242

13 Figure 10. ALLVAR nudging experiment where horizontal velocities, temperature, and humidity fields are nudged to the ECMWF Reanalysis fields. (a) Temperature model bias (K/d). (b) Humidity model bias (g/kg/d). (c) Convective temperature tendency (K/d). (d) Advective temperature tendency (K/d). (e) Zonal wind model bias (m/s/d). (f) Meridional wind model bias (m/s/d). The model biases shown in Figures 10a, 10b, 10e, and 10f are the negative of nudging tendencies. All the fields (Figures 10a 10f) are averaged over the NSA region (72 E 80 E, 0 N 7.5 N). Nudging time scale = 1 h toward six-hourly ECMWF fields. convection (22 October to 31 October, as seen in Figure 10c), there is an overall cooling and moistening tendency bias in the upper troposphere and heating and drying tendency bias in the lower troposphere (below 700 hpa). This suggests the model s deficiency in representing the observed top-heavy vertical diabatic heating structure of the MJO during DYNAMO (Ciesielski, personal communication, 2013). A vertical dipole heating contributes to the top-heavy heating profile observed in the areas of stratiform precipitation, with heating and drying in the upper troposphere from large-scale condensation accompanied by cooling and moistening in the lower troposphere from rain reevaporation, as also seen in previous studies of the MJO [Houze, 1982, 1997; Johnson, 1984; Mapes and Houze, 1995]. Thus, the baroclinic structure in the model biases after 21 October indicates that the model lacks both sufficient stratiform precipitation, consistent with the weak large-scale precipitation seen in Figure 8, and sufficient rain reevaporation in the lower troposphere, consistent with the warming and drying biases seen during this period. The zonal wind tendency in Figure 10e shows a negative (easterly tendency) bias above 600 hpa and a positive (westerly tendency) bias below 600 hpa during the MJO initiation and developing phases, indicating that the model has a tendency to produce stronger vertical wind shear than it should. In all the model experiments conducted in this study, convective momentum transport (CMT) is included using the parameterization scheme of Zhang and Cho [1991]. The CMT scheme considers the vertical redistribution of horizontal momentum by convection and accounts for the role of perturbation pressure field generated by the interaction of convection with large-scale circulation in vertical momentum transport [Wu and Yanai, 1994; Zhang and Wu, 2003]. The negative tendency bias in the upper troposphere and positive tendency bias in the lower troposphere during the MJO initiation and developing phases (Figure 10e) indicate that there is SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7243

14 not enough vertical transport of momentum by convection. Again, this is consistent with the lack of convection during this period. In the mature to decay phase after 27 October, the biases are in general reversed, with negative momentum tendency in the lower troposphere and positive tendency in the upper troposphere. From Figure 6, this is a period of active convection, and the model simulation produces more precipitation than the TRMM observations and the ERAI reanalyses, suggesting that there may be too much convection, which transports more momentum vertically than needed. Comparing the observed and model simulated zonal winds in the free-running simulation (Figure 4), this period corresponds to the enhancement of westerly winds that gradually deepen from the surface to midtroposphere. On the other hand, the model simulation shows deepening of easterly winds in the lower troposphere due to the negative momentum tendency biases. The meridional momentum tendency bias shows positive tendency biases in the lower to middle troposphere and negative biases above from 13 to 27 October and reverses afterward. It is not straightforward to relate them to convective activity. Nudging all the four fields simultaneously allows one to estimate the biases in model physics tendencies, because the dynamical tendencies from advection are constrained by observations/reanalyses data. As seen earlier (Figure 7), leaving any of the fields unnudged leads to significant changes in the MJO precipitation. In order to examine how errors in an individual field can affect the performance of other model physics in the MJO hindcast, we also analyze the nudging tendencies and misfit biases for the unnudged model fields from the other nudging experiments. Figure 11 presents the model biases for the NOHUM and HUMONLY experiments, along with the convective and advective temperature tendencies. Since specific humidity is allowed to evolve unconstrained in the NOHUM experiment, we plot its deviation from the reanalysis field in the figure. The temperature tendency bias shows that the negative diabatic heating bias from 12 to 21 October is reduced considerably throughout the troposphere compared to Figure 10a, which corresponds to the increase in precipitation (Figure 7). The humidity misfit in Figure 11c shows a net dry bias in the lower troposphere prior to the MJO initiation as a combined effect of the advective and convective processes. This is in contrast to the moist bias in the free-running hindcast during this period. Nudging temperature and velocities have made the model drier in this region. Afterward, the dry bias gives way to moist bias in the entire troposphere, which is again in contrast to the free-running CAM3 hindcast that exhibited a moist bias during the convective phase of the MJO. Also, the positive temperature tendency biases are increased in the middle troposphere and decreased in the lower troposphere, corresponding to increased large-scale precipitation during this period, as seen in Figure 8. The wind tendency biases are very similar to those in the ALLVAR hindcast, with somewhat larger magnitudes. This suggests that the moisture field in the model is reasonably well simulated; nudging it or not does not significantly change the model simulation results. Figure 11 (right column) shows the temperature, specific humidity, and wind biases in the HUMONLY case, which has the strongest precipitation amplitude among all the nudging experiments presented. The atmospheric column is cooler than the reanalysis, similar to the free-running hindcast temperature bias, with increased convective heating tendency during the mature phase being balanced by the strong advective cooling. This is also evident in the much stronger precipitation signal for this case compared to the ALLVAR case. The specific humidity tendency bias also shows a dry bias in the lower atmosphere during the initiation and mature phases of the MJO along with a moistening bias in the PBL indicating increased diffusion in the PBL. The zonal wind bias shows a strong westerly (or weaker easterly) bias in the upper atmosphere and strong easterly (or weaker westerly) bias in the lower atmosphere during the initiation and mature phases of the MJO, indicative of too weak a shear. This is likely a result of increased vertical mixing by excessively strong convection. When the model temperature is allowed to evolve freely (Figure 12, left column), the atmosphere becomes significantly cooler similar to the HUMONLY case and the free-running hindcast, especially in the middle and upper troposphere during the MJO initiation phase. The cooler temperature bias in the troposphere during the MJO initiation phase is due to the combined cooling effect of the convective and advective temperature tendencies. During the maturing and mature phases of the MJO (22 October to 31 October), convection (as shown in Figure 12e) is significantly stronger than that in the ALLVAR case, which is also reflected in the enhanced precipitation in the NOTEMP case (Figure 7). However, the moisture tendency biases are much smaller than in the ALLVAR run, with noticeable drying bias in the lower troposphere only in the mature to decaying stage of the MJO. Also, there are moistening biases in the PBL, suggesting an increased activity of PBL diffusion. The zonal momentum tendency biases are mostly positive in the lower SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7244

15 Figure 11. (left column) NOHUM and (right column) HUMONLY nudging experiments where horizontal velocities and temperature fields or only humidity fields, respectively, are nudged to the ECMWF Reanalysis fields. (a) NOHUM temperature model bias (negative of nudging tendencies, K/d) and (b) HUMONLY temperature model misfit between CAM3 and ERAI interpolated temperature fields. (c) NOHUM humidity misfit (g/kg) between CAM3 humidity field and ERAI interpolated fields and (d) HUMONLY humidity model bias (negative of nudging tendencies, K/d). (e and f) Convective temperature tendency (K/d). (g and h) Advective temperature tendency (K/d). (i) NOHUM zonal wind model bias (m/s/d) and (j) HUMONLY zonal wind misfit between CAM3 and ERAI interpolated zonal wind fields. The model biases shown in Figures 11a, 11d, and 11i are the negative of nudging tendencies. All the fields (Figures 11a 11f) are averaged over the NSA region (72 E 80 E, 0 N 7.5 N). Nudging timescale = 1 h toward six-hourly ECMWF fields. SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7245

16 Figure 12. (left column) NOTEMP and (right column) TEMPONLY nudging experiments where horizontal velocities and humidity fields or only temperature fields, respectively, are nudged to the ECMWF Reanalysis fields. (a) NOTEMP temperature model misfit between CAM3 and ERAI interpolated temperature fields and (b) TEMPONLY temperature model bias (negative of nudging tendencies, K/d). (c) NOTEMP humidity model bias (negative of nudging tendencies, K/d) and (d) TEMPONLY humidity misfit (g/kg) between CAM3 humidity field and ERAI interpolated fields. (e and f) Convective temperature tendency (K/d). (g and h) Advective temperature tendency (K/d). (i) NOTEMP zonal wind model bias (m/s/d) and (j) TEMPONLY zonal wind misfit between CAM3 and ERAI interpolated zonal wind fields. The model biases shown in Figures 12b, 12c, and 12i are the negative of nudging tendencies. All the fields (Figures 12a 12f) are averaged over the NSA region (72 E 80 E, 0 N 7.5 N). Nudging timescale = 1 h toward six-hourly ECMWF fields. SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7246

17 and middle troposphere and negative in the upper troposphere before the MJO initiation. Since the ERAI u wind during this period is westerly in the lower troposphere and easterly above, this implies that convective vertical mixing is too weak, same as in other cases, due to lack of convection. During the MJO mature phase (22 October to 31 October), increased convection corresponds to increased vertical mixing, leading to negative u-momentum tendency biases (weaker westerlies) in the lower troposphere and positive biases above (weaker easterlies). The negative biases gradually moves upward as the MJO decays. During the peak convection period of the MJO, along with increased zonal momentum biases, the NOTEMP experiment also reveals increased meridional wind tendency biases. However, they cannot be related to the alternating meridional wind patterns as seen in Figure 4e. Figure 12 (right column) shows the biases in temperature tendency, humidity, and winds for the TEMPONLY case, which has the weakest MJO signal among the experiments. The atmosphere has a cooling tendency bias prior to the MJO initiation (before 16 October) and then has a net warming bias until 300 hpa throughout the period of the MJO propagation through this domain. The humidity misfit is small and mostly positive throughout the period except for a small negative bias in the lower atmosphere during the evolution of the MJO. Corresponding to the weak precipitation signal in this case, the convective tendencies are also less than the ALLVAR cases. This weak convective tendency is mostly balanced by the weak advective tendency as in other cases. The zonal wind bias is weak and does not show a particular pattern, due to the lack of organized convection in the domain for this experiment. Since the humidity misfit is small and the temperature is nudged in this experiment, the biases in the winds seem to play a critical role in both triggering convection earlier in this experiment and also in the absence of organized convection during the mature phase of the MJO. Figure 13 (left column) show the temperature and specific humidity tendency biases and the wind biases for the NOVEL experiment. The overall patterns of temperature and moisture tendency biases are similar to the ALLVAR run except with larger magnitudes, with cooling and moistening biases during MJO initiation and mature phases. This indicates that the model undersimulates convection, consistent with the precipitation field in Figure 7 and the convective heating field in Figure 13e as compared to the ALLVAR case. In the decaying phase, there are heating and drying biases in the lower troposphere and cooling and moistening biases in the upper troposphere, indicating insufficient stratiform condensation in the upper troposphere and rain reevaporation in the lower troposphere. The zonal wind misfit shows an easterly wind bias in the middle troposphere at the MJO initiation. During the mature phase, the westerly wind is too strong in the lower troposphere. Near the end of the MJO life cycle, the westerly wind extends too much into the upper troposphere. Figure 13 (right column) shows the temperature, specific humidity, and velocity biases for the VELONLY case, which has a very good representation of the MJO signal. The temperature misfit shows a much cooler atmosphere in the vertical with enhanced cool bias in the upper atmosphere. The net cool bias in the upper troposphere is greater than the free-running hindcast bias. This could be indicative of reduced stratiform condensational heating compared to reanalysis. This is consistent with large-scale precipitation in this case only accounting for about 10 20% of the total precipitation. The humidity misfit shows a moist bias during the preinitiation phase comparable to the free-running hindcast bias and then becomes small negative during the mature phase of the MJO. The magnitude of convective heating and advective cooling is of the same order as the ALLVAR case, indicating that the cooler upper troposphere with similar amount of moisture to the reanalysis, produces equivalent amount of convection and precipitation as observed in this MJO event. The zonal wind tendency biases reveal a westerly tendency bias in the lower atmosphere and in the upper atmosphere but a easterly bias in the middle atmosphere. During the MJO preinitiation phase, this could indicate the insufficient momentum mixing from the lower levels to the upper level leading to strong westerlies, which have to be reduced by nudging. To further interpret the processes contributing and compensated for by nudging during the MJO evolution, we study the vertically integrated moist static energy budget in the troposphere in the following section. 5. Column-Integrated Moist Static Energy Budget Analysis To further elucidate the processes relating the nudging tendency terms to the intensity of MJO convection via moistening of the troposphere during the initiation, mature, and decay phases of the SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7247

18 Figure 13. (left column) NOVEL and (right column) VELONLY nudging experiments where temperature and humidity fields or only horizontal velocity fields, respectively, are nudged to the ECMWF Reanalysis fields. (a) NOVEL temperature model bias (negative of nudging tendencies, K/d) and (b) VELONLY temperature model misfit between CAM3 and ERAI interpolated temperature fields. (c) NOVEL humidity model bias (negative of nudging tendencies, K/d) and (d) VELONLY humidity misfit (g/kg) between CAM3 humidity fields and ERAI interpolated fields. (e and f) Convective temperature tendency (K/d). (g and h) Advective temperature tendency (K/d). (i) NOVEL zonal wind misfit between CAM3 and ERAI interpolated zonal wind fields and (j) VELONLY zonal wind model bias (m/s/d). The model biases shown in Figures 13b, 13c, and 13i are the negative of nudging tendencies. All the fields (Figures 13a 13f) are averaged over the NSA region (72 E 80 E, 0 N 7.5 N). Nudging time scale = 1 h toward six-hourly ECMWF fields. SUBRAMANIAN AND ZHANG American Geophysical Union. All Rights Reserved. 7248