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On the Relationship between Mean Monsoon Precipitation and Low Pressure Systems in Climate Model Simulations V. PRAVEEN, S. SANDEEP, AND R. S. AJAYAMOHAN Center for Prototype Climate Modeling, New York University Abu Dhabi, Abu Dhabi, United Arab Emirates (Manuscript received 12 June 2014, in final form 9 March 2015) ABSTRACT The north-northwest-propagating low pressure systems (LPS) are an important component of the Indian summer monsoon (ISM). The objective detection and tracking of LPS in reanalysis products and climate model simulations are challenging because of the weak structure of the LPS compared to tropical cyclones. Therefore, the skill of reanalyses and climate models in simulating the monsoon LPS is unknown. A robust method is presented here to objectively identify and track LPS, which mimics the conventional identification and tracking algorithm based on detecting closed isobars on surface pressure charts. The new LPS tracking technique allows a fair comparison between the observed and simulated LPS. The analysis based on the new tracking algorithm shows that the reanalyses from ERA-Interim and MERRA were able to reproduce the observed climatology and interannual variability of the monsoon LPS with a fair degree of accuracy. Further, the newly developed LPS detection and tracking algorithm is also applied to the climate model simulations of phase 5 of the Coupled Model Intercomparison Project (CMIP5). The CMIP5 models show considerable spread in terms of their skill in LPS simulation. About 60% of the observed total summer monsoon precipitation over east-central India is found to be associated with LPS activities, while in model simulations this ratio varies between 5% and 60%. Those models that simulate synoptic activity realistically are found to have better skill in simulating seasonal mean monsoon precipitation. The model-to-model variability in the simulated synoptic activity is found to be linked to the intermodel spread in zonal wind shear over the Indian region, which is further linked to inadequate representation of the tropical easterly jet in climate models. These findings elucidate the mechanisms behind the model simulation of ISM precipitation, synoptic activity, and their interdependence.
1. Introduction Tropical cyclones are high-impact weather systems that form during the summer season and often bring havoc to coastal regions. A distinct feature of the northern Indian Ocean, as compared to the rest of the tropical oceans, is that the strong vertical shear of the monsoon winds prevents tropical cyclone development during the summer monsoon season. Nevertheless relatively weak cyclonic storms form during the Indian summer monsoon (ISM) season, and they play a crucial role in determining the amount and distribution of the summer rainfall over India, as they penetrate deep inland. These synoptic-scale
Corresponding author address: R. S. Ajayamohan, Center for Prototype Climate Modeling, New York University Abu Dhabi, Saadiyat Island, P.O. Box 129188, Abu Dhabi, United Arab Emirates. E-mail:
[email protected] DOI: 10.1175/JCLI-D-14-00415.1 Ó 2015 American Meteorological Society
systems of varying strength mainly form over the Bay of Bengal and generally follow a north-northwestward track (Mooley 1973; Sikka 1977; Krishnamurthy and Ajayamohan 2010). These cyclonic systems are collectively called low pressure systems (LPS; Mooley and Shukla 1989; Ajayamohan et al. 2010). The LPS have a life cycle of 3–6 days and a horizontal dimension of about 1000–2000 km (Mooley 1973; Krishnamurti et al. 1975), and they bring copious rainfall to the central and northwestern Indian subcontinent during the June–September (JJAS) season (Krishnamurthy and Ajayamohan 2010). Further, Krishnamurthy and Ajayamohan (2010) noted that the LPS tracks reach farther into northwestern India during flood years compared to drought years, during which they are mainly confined to central India. The increasing trend in extreme rainfall events during the ISM season in recent decades is also found to be linked with LPS (Ajayamohan et al. 2010). Despite their important role in the ISM, the dynamics and thermodynamics of LPS are not
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as well studied as tropical cyclones. Moreover, the skill of present-day state-of-the-art climate models in simulating LPS has not been reviewed. One hurdle for such studies to be carried out is the difficulty of detection and tracking of LPS in reanalysis and climate model simulations. The objective detection and tracking of tropical cyclones is relatively easy, as their severe strength and well-defined structure makes them distinguishable from the mean atmospheric flow pattern. However, this is not the case with relatively weak cyclonic storms, such as extratropical cyclones (Neu et al. 2013). Tropical cyclones are also vividly clear in satellite pictures to the extent that their dimensions can be measured in contrast to relatively weak monsoon low pressure systems. The objective tracking of rather weak cyclonic systems that form over the Indian region during the summer monsoon season can be equally challenging as the tracking of extratropical cyclones. The presence of a monsoon trough, which is a semipermanent low pressure region over the Indian landmass during summer, makes the detection of LPS complicated for the objective algorithms that rely on minimum sea level pressure (SLP) criteria. Various methods are developed for the objective identification and tracking of rotating weather systems [e.g., Murray and Simmonds (1991); Hodges (1994); Sinclair (1997); see Neu et al. (2013) for a detailed review]. However, the monsoon LPS are weaker than tropical cyclones and often midlatitude storms. Hence, the methods developed primarily to detect tropical cyclones or midlatitude storms may not be optimal for the identification of monsoon LPS both from reanalysis data products and climate model simulations. Few previous studies (Sabre et al. 2000; Stowasser et al. 2009) used known tropical cyclone tracking algorithms to detect LPS in coupled general circulation models (CGCMs) and regional climate models. However, none of them validated their LPS tracking technique by applying it to observationally constrained reanalysis data and cross comparing it with actual LPS observations. Hence, the reliability of those techniques in detecting monsoon LPS is unknown. A comparison of storm frequency (mainly depressions) in ERA-40 with observations during the ISM season shows that the reanalysis data overestimate the storm events by about two storms in an year, although the linear trend (decrease of depressions in recent decades) is somewhat reasonably reproduced (Stowasser et al. 2009). The reason for overestimation of storm events in ERA-40 data cannot be ascertained without a proper validation of the technique used to track storms. If the reanalysis data are able to reliably capture the observed LPS tracks and intensity, it would open up the possibility of exploring the dynamics of LPS using three-dimensional meteorological fields from the reanalysis.
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The impacts of climate change on monsoon LPS are not identified, especially in the future climate. The intensity of postmonsoon tropical cyclones over the Bay of Bengal is found to be increasing in recent decades, with no significant changes in the number of systems (Balaguru et al. 2014). In contrast, the frequency of the stronger (weaker) monsoon LPS is decreasing (increasing) in recent decades during the boreal summer season (Rajeevan et al. 2000; Dash et al. 2004; Ajayamohan et al. 2010; Krishnamurti et al. 2013; Prajeesh et al. 2013). The exact reason for this contrasting trend in monsoon LPS compared to tropical cyclones needs to be identified in order to make a reliable projection of natural hazards over the Indian region in a warming climate. CGCMs are the main tool for making future projections of the global climate. Most of the state-of-the-art current-generation climate models are not successful in simulating a reasonable mean climate of the ISM (Sperber et al. 2013; Ramesh and Goswami 2014; Sandeep and Ajayamohan 2015, 2014), which may be linked to the models’ inability to simulate monsoon internal dynamics (Ajayamohan and Goswami 2007; Xavier et al. 2010). In a recent study, Sabin et al. (2013) note that improved LPS activity in a high-resolution atmospheric GCM (AGCM) results in realistic mean ISM precipitation. This suggests that understanding the simulation of monsoon synoptic activity by the climate models is imperative for improved mean monsoon simulation and to make reliable projections of the future climate. However, the current understanding of the climate models’ fidelity in simulating synoptic features of ISMs is limited. Development of a robust algorithm to track LPS in climate model simulations can provide new insights to the model simulation of monsoons. The primary goal of this study is to assess the skill of climate models and reanalysis products in simulating monsoon LPS that form over the Bay of Bengal. An objective technique based on closed isobar identification is developed to identify and track LPS from climate model simulations and reanalysis data. The relationship of mean monsoon rainfall and synoptic activity in climate model simulations is examined. In addition, we are making the tracks of the LPS dataset in the ECMWF interim reanalysis (hereinafter ERAI; Dee et al. 2011) and the Modern-Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al. 2011) data publicly available in electronic form. This will help the monsoon research community to use an LPS dataset that is very well compared with the observed data. Note that an inventory of Indian monsoon LPS databased on reanalysis data products like ERAI and MERRA is nonexistent today because of different identification criteria employed for specific research purposes in various studies.
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The next section of this paper describes the data and methodology used, the LPS tracking algorithm, and the diagnostics used for comparing simulated synoptic activity. Section 3 validates our results with the observed LPS dataset and compares them with a few CGCM outputs from the CMIP5 archive. The systematic biases seen in the CGCMs in simulating synoptic activity and the dynamics associated with it are discussed in section 4. A brief summary of results and concluding remarks are presented in section 5.
2. Data and methods a. Data We used LPS data compiled by Mooley and Shukla (1987) and Sikka (2006) by a careful examination of the India Meteorological Department (IMD) surface pressure charts [see Ajayamohan et al. (2010) for details]. Since the observed LPS data are derived from surface pressure charts, we also use pressure data to retrieve LPS information from reanalyses and climate model simulations. For stronger systems, such as tropical cyclones, vorticity field or precipitation observation from satellites may serve as better parameters for tracking the vortex trajectory. A weaker vortex combined with a lack of eyewall structure and organized precipitation band makes it difficult for tracking of LPS using precipitation/ vorticity fields. We used 6-hourly SLP data from ERAI and daily mean SLP from CMIP5 models. MERRA provides high-frequency data output, and we used hourly SLP data from it. The ERAI uses a four-dimensional variational data assimilation system to ingest satellite and conventional observations in its model at every 12-h cycle (Dee et al. 2011). The MERRA assimilates a substantial amount of satellite observations in addition to conventional data sources using a three-dimensional variational analysis at every 6-h cycle (Rienecker et al. 2011). Thus, ERAI and MERRA can be considered good choices in studying synoptic-scale systems, such as LPS. Daily precipitation from IMD observations (Rajeevan et al. 2006) and CMIP5 simulations are also used. The lengths of data archives vary between different datasets. We choose data during 1979–2003 for this study, as all products have data during this time span. Daily temperature and meridional winds from ERAI and MERRA, as well as historical all-forcing (AF) simulations of CMIP5 models during 1990–99, are used to construct storm-centered composites of LPS vertical structure. Monthly mean specific humidity and winds during 1979–2003 from ERAI and AF simulations are used to calculate moisture convergence, geostrophic vorticity, and zonal wind shear. (For a list of CMIP5 coupled models used in this study, see Table 3).
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Only the first ensemble (r1i1p1) of the CMIP5 experiments is used. All analyses are done for the JJAS season.
b. LPS tracking technique As explained earlier, the objective tracking of LPS is challenging. Nevertheless, a rich set of data spanning the whole twentieth century has been constructed by the careful manual evaluation of the India Meteorological Department’s daily surface pressure charts (Mooley and Shukla 1987; Sikka 2006; herein referred to as the Sikka archive). These observed LPS data can be used as a benchmark to evaluate LPS simulated by reanalysis. We aim to devise a technique that is as close as possible to the manual detection and tracking criteria used by Sikka (2006). The manual tracking of LPS in the Sikka archive is based on the identification of closed contours in the interval of 2 hPa on daily surface pressure charts. Such a closed contour identification technique has been objectively applied in reanalysis data mainly to detect extratropical storms (Wernli and Schwierz 2006; Hanley and Caballero 2012). The advantages of the technique of Hanley and Caballero (2012) are that it can detect multicenter cyclones, and it does not rely on ellipsoidal best fit to identify the closed contours, as in the case of earlier algorithms (Murray and Simmonds 1991). The proposed LPS tracking technique imbibes the basic principles of contour detection from Hanley and Caballero (2012). However, the unique regional features, such as heat lows over the land and the presence of a semipermanent low pressure area called the monsoon trough, can have annulling effects on automated algorithms that are successful in tracking extratropical storms. This is evident from the spatial distribution of cyclone frequency in Wernli and Schwierz (2006), where the pattern over South Asia resembles heat low over the deserts. The monsoon trough in their study extends from a heat low over northwestern India to the Indo-Gangetic plain aligned parallel to the Himalayas (see their Fig. 4c). To overcome these hurdles in objectively detecting monsoon LPS, a new detection and tracking technique is designed, as explained in the following steps.
1) CONTOUR DETECTION (i) At each grid point, search for the local minima from the surrounding eight grid points. (ii) In the above step, local minima that do not satisfy the criteria of a threshold pressure gradient are considered as heat lows and removed. The heat lows are identified in the following substeps: 1) Mean pressure gradient of central minima ($SLP) with respect to surrounding eight grid points is calculated.
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FIG. 1. Schematic diagram showing the identification of the LPS center and calculation of the pressure gradient for (a) a single-center system and (b) a multicenter system.
2) If the numerical value of $SLP is less than 1/10th of grid resolution of the dataset (e.g., 0.15 hPa per degree of grid resolution for ERAI, which has a grid resolution of 1.58), then it is considered as a heat low. A systematic sensitivity analysis is carried out by varying $SLP as a function of data grid resolution to arrive at an optimal value. This procedure is similar to that of Hanley and Caballero (2012), but with a modified threshold pressure gradient to account for summer heat lows over India. The data are then regridded to a 0.58 3 0.58 resolution before proceeding to the next step, in order to get smoother contours. (iii) Identify a closed contour around the local minimum with an increment of 1-hPa interval. (iv) The pressure depth (DSLP) is calculated as the pressure difference between the outermost closed contour and the local minimum. If there are more than two local minima inside the outermost closed contour, take the lowest among them (see Fig. 1). For both ERAI and MERRA datasets, contours are identified for two sampling times that are closer to the IMD sampling time (0230 UTC). The detection is performed for ERAI at 0000 and 0600 UTC and MERRA at 0200 and 0300 UTC, respectively. Since CMIP5 model SLPs are available as daily means, only one time slice per day is used.
2) TRACKING (i) A first guess position of the track is taken as the first member in the first time slice (e.g., for MERRA 0200 UTC data). If the search returns none, then the search is extended to the next time slice (e.g., for MERRA 0300 UTC). (ii) The second position in the track is determined by searching in a radius of 38 from the first guess position after 24 h. The search radius for the subsequent positions is taken as the distance traveled by the
system in the previous 24 h (R). Since the systems slow down over the land, the search radius is also reduced as 0.75R. The land–sea separation is identified using a 15-m isobath extracted from the ETOPO2v2 dataset (NOAA/National Geophysical Data Center 2006). The local minima identified outside the search radius are considered as independent systems and are tracked simultaneously to account for multiple systems. (iii) If the nearest neighbor search for the next LPS position does not find an LPS in the ensuing 24 h, then those tracks are treated as terminated. (iv) The systems with a life cycle less than 2 days are not considered. (v) The algorithm runs for 122 days of the monsoon season, starting 1 June. Since CMIP5 models have only daily means, step (i) is performed only for one time slice per day. The above objective tracking algorithm is performed over the eastern half of the Indian subcontinent, spanning the Bay of Bengal (58–278N and 708–1008E). Note that systems forming over the Arabian Sea are not included in this study, as our goal is to study the systems originating over the Bay of Bengal and adjoining land regions. The different categories of LPS based on the intensity of the storm are shown in Table 1. The original classification used by Mooley and Shukla (1987) is used in the present work to avoid ambiguity in determining the category of the LPS by the objective detection and tracking algorithm.
c. Comparison of LPS tracks with observations LPS have a considerable range of intensities and, hence, for the purpose of comparison, we derive an aggregate synoptic activity index (SAI) by summing the number of LPS days in each 38 3 38 grid cell after weighting with LPS intensity for each JJAS season [see
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TABLE 1. Classification of monsoon low pressure systems based on pressure depth adapted in this study. This classification is similar to the one used in Sikka (2006) and Ajayamohan et al. (2010). DSLP (hPa)
LPS category
Estimated wind speed (m s21)
SAI weighting
#2 .2 and #4 .4 and #10 .10 and #16 .16
Low Depression Deep depression Cyclonic storm Severe cyclonic storm
,8.5 8.5–13.4 13.5–16.4 16.5–23.4 $23.5
4.25 11 15 20 27.5
the appendix of Krishnamurthy and Ajayamohan (2010) for details]. LPS days are defined as days in the monsoon season in which an LPS is present. If N systems occur in the same day, then it will be counted as N LPS days. The spatial patterns of SAI computed from ERAI and MERRA and CMIP5 model simulations are quantitatively compared with those of the Sikka archive by calculating spatial correlation and the root-mean-square error (RMSE). The cyclone track intercomparison procedure (Blender and Schubert 2000; Neu et al. 2013) is employed to compare LPS tracks derived from ERAI and MERRA with the observed LPS tracks (see the appendix for details).
d. Diagnostics of CGCM-simulated synoptic activity The diagnostics that involve spatial maps of SAI are done for a subset of five CMIP5 models for the sake of brevity. The subset of CMIP5 CGCMs and their standalone version (atmospheric) are chosen in such a way that good, bad, and moderately performing ones are represented in an unbiased manner. The model performance in simulating ISM is evaluated using a Taylor diagram [see Fig. 13 of Sandeep and Ajayamohan (2015)]. The scatterplots and regression maps that show the model spread in synoptic activity use all the available 17 models, which provide daily data for the variables considered for this study. The JJAS mean wind shear is calculated as the difference between 200- and 850-hPa mean zonal winds (U200 and U850, respectively). To examine to what extent the model-to-model spread in wind shear affects the intermodel variance in synoptic activity, we regressed the area-averaged SAI climatology of 17 models on their zonal wind shear climatology. This regression technique is identical to the one used by Sandeep and Ajayamohan (2014), which can be explained with the regression coefficient
1 T b5 åU i U i n
21
1 åU i hSi i , n
where U denotes JJAS climatological zonal wind shear, hSi is the area-averaged SAI climatology over 158–258N
and 778–908E , the subscript i stands for the index of CMIP5 models (spans from 1 to 17), and n is for the total number of models used in the calculation (in this case n 5 17). For easier understanding, one may imagine the regression of the ‘‘model series’’ of area-averaged SAI climatology S(model) regressed on the model series of two-dimensional climatological maps of zonal wind shear U(model, latitude, longitude). The map of regression slopes is expected to reveal the model-to-model covariance in SAI and zonal wind shear. In the case of regression of SAI on vertical profiles of zonal winds, the model series of SAI climatology S(model) is regressed on the model series of zonal wind profiles U(model, level). The statistical significance of the regression slopes is estimated using a two-tailed t test.
3. Synoptic variability in reanalyses and climate models a. Interannual variability of observed and reconstructed LPS tracks The success of LPS track retrieval from the reanalysis data depends on the robustness of the tracking algorithm. The modern reanalysis products are observationally constrained by the assimilation of satellite and conventional meteorological observations using sophisticated data assimilation systems (Dee et al. 2011; Rienecker et al. 2011). Therefore, we may expect the current-generation reanalyses, such as ERAI and MERRA, to be closer to the actual observations. Nonetheless, the application of reanalysis data in climate change studies should be done with caution because of the spurious trends in some fields, mainly arising from the changes in observing systems (Bengtsson et al. 2004; Robertson et al. 2011). It is therefore essential that the storm positions and intensity derived from the reanalysis should be compared with the corresponding observations in terms of mean climatology as well as year-to-year variability. If the interannual variability of the LPS frequency and intensity are realistically reproduced by the reanalysis data, then our confidence in using the tracking algorithm for long-term climate analysis will be enhanced.
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TABLE 2. Mean number (June–September) of category-wise storms formed over the Bay of Bengal during 1979–2003. Data source/ category
Low
Depression
Deep depression
Total (std dev)
Sikka archive ERA MERRA
9.2 6.1 6.6
2.5 4.5 4.5
1.2 2 2.9
12.9 (2.7) 12.6 (2.7) 14 (2.8)
A comparison of the LPS statistics generated by the proposed technique from ERAI and MERRA data with the observed daily weather data (Sikka archive) would be helpful in a qualitative understanding of the limitations of the technique and data before venturing in to further investigation. Table 2 lists the LPS statistics generated by the new tracking technique as compared to the Sikka archive. Qualitatively, both the reanalysis products perform reasonably well in reconstructing the overall LPS numbers in a season with comparable standard deviations during the 1979–2003 period, with MERRA detecting about 1 storm more than the Sikka archive (Table 2). The tracking algorithm systematically underestimates (overestimates) the number of lows (depressions and deep depressions) in a season over the Bay of Bengal. We note here that a recent study (Hurley and Boos 2015) using an entirely different tracking algorithm detects ;16 storms (;13 in the Sikka archive) in a season over the north Indian Ocean (both the Bay of Bengal and Arabian Sea). This highlights the promise and pitfalls in tracking LPS using reanalysis data. Errors can percolate from the reanalysis data because of its coarse resolution, data assimilation, and several other related issues. On the other hand, errors in the LPS observational data (Sikka archive) due to the subjectivity involved in the manual detection technique cannot be overruled. The narrow division between various LPS categories (lows, depressions, deep depressions, and cyclonic storms) also poses challenges to an automated tracking algorithm. As indicated earlier, an accurate reproduction of interannual variability in the LPS activity by the reanalysis products is important for their usefulness in the longterm climate analysis. Thus, the next step is to evaluate the reconstructed LPS frequency and intensity against observations during the period of combined availability of both datasets. Monsoon LPS systems rarely achieve intensity above that of category 2 cyclones on the Saffir– Simpson scale (Ajayamohan et al. 2010). Therefore, we split the number of LPS systems that form in each monsoon season during 1979–2003 into two intensity categories (Table 1): namely, category 1 and 2 (lows and depressions) and category .2 (deep depressions and storms). Both ERAI and MERRA moderately
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reproduce the observed number of category 1 and 2 LPS during the analysis period (Fig. 2a). The number of stronger LPS (category .2) are better reproduced by MERRA than ERAI (Fig. 2b). During 1979–2003, 322 LPS systems formed over the Bay of Bengal, with a total of 1478 days of activity. In this period, ERAI (MERRA) has 317 (351) systems, totaling 1339 (1442) days of storm activity, which indicates robustness of the reanalysis data as well as the tracking technique. Despite the reliability of the reanalysis products in reconstructing the total number of systems and stormy days, the categorywise reproduction seems to be sketchy. During the 1999– 2003 period, there are no stronger LPS (category .2) in the observations. However, ERAI and MERRA show a moderate number of stronger LPS during this period. This hints at the uncertainty in the category-wise reproduction of LPS, especially category 3 and greater, by the reanalysis products. The total June–September LPS (figure not shown) in ERAI (MERRA) has a better correlation of 0.45 (0.6) compared to the category-wise comparison with the Sikka archive during 1979–2003. The monthwise cumulative days of LPS activity during 1979–2003 are shown as time series (Fig. 3). Both ERAI and MERRA reasonably capture the interannual variability in monthly LPS activity. The observed interannual variability in June, July, and August LPS days (days when LPS are present) are reproduced by both the reanalysis data products (Figs. 3a–c). Overall, both reanalyses have moderately strong to high correlations with the observed interannual variability in LPS days for the months of June, July, and August. It is noted that LPS days in the month of July have a significant (p , 0.05) positive trend in the observations, as well as in ERAI and MERRA. The interannual variability in LPS days derived from reanalysis data are weakly correlated with observations for the month of September, in contrast to the other three monsoon months (June, July, and August). The reason for this weak correlation in the month of September between observations and reanalysis is unclear. A more detailed analysis of daily pressure charts in the month of September is needed to uncover this ambiguity, which is beyond the scope of the present study. The cumulative seasonal (JJAS) LPS days are calculated for the Sikka archive, ERAI and MERRA, and five AGCM simulations of the AMIP experiment carried out as part of the CMIP5 exercise (Fig. 4, top). These AGCM simulations are forced with observed monthly SSTs and other anthropogenic, as well as natural, forcing agents (Taylor et al. 2012). The total number of JJAS LPS days in ERAI and MERRA has a moderate correlation with observations (r 5 0.49 and 0.54, respectively). When the LPS days during only June–August are considered, the reanalysis data have
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FIG. 2. Comparison of the observed and reconstructed annual number of low pressure systems formed over the Indian monsoon region for (a) category 1 and 2 and (b) category .2 systems. The correlations of the annual number of LPS computed from ERAI and MERRA datasets with the Sikka archive are indicated for JJAS and JJA.
stronger correlations with the observed LPS days, with r 5 0.65 (0.76) for ERAI (MERRA). These results indicate that the LPS identified using ERAI and MERRA data are not artifacts of the reanalysis data. Moreover, it highlights the skill of the tracking technique to capture the interannual variability of the observed data. It is worth noting here that similar studies to track LPS data fail to capture interannual variability of the observed synoptic activity (Hurley and Boos 2015). As the LPS information from the reanalysis products extracted by the proposed technique is similar to that of the Sikka archive, this LPS tracking technique can be applied to CMIP5 models as a means of evaluating their performance in LPS simulation. It is interesting to note that two out of five models (CNRM-CM5 and CCSM4) analyzed here simulate the seasonal sum of LPS days closer to the observed range. While MRI-CGCM3 and MIROC5 overestimate the number of LPS days,
ACCESS1.3 underestimates it. Further, this indicates that many of the current-generation climate models are able to simulate monsoon LPS. However, a comprehensive analysis of the simulated spatial structure and life cycle is required to assess the skill of climate models in successfully simulating the observed characteristics of the monsoon LPS (see section 3c).
b. Track intercomparison Probably the most difficult test for any cyclone (or LPS) tracking algorithm is to simulate the correct trajectory of the system. The probability of coincidence (Pc) of tracks (Blender and Schubert 2000; see appendix) in observations and reanalysis is a robust measure of how close the LPS track reproduced by the reanalysis is to the observed (Sikka archive) track. The Pc of LPS tracks from ERAI and MERRA with respect to observations are calculated for each monsoon season during
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FIG. 3. Frequency of LPS days for (a) June, (b) July, (c) August, and (d) September during 1979–2003 calculated from the Sikka archive, ERAI, and MERRA. The correlation of the frequency of LPS days computed using ERAI and MERRA datasets with the Sikka archive are also indicated.
1979–2003 (Fig. 5). A considerable range of coincidence probability (;7%–53%) is found for ERAI and MERRA tracks with observed tracks for individual seasons. A 100% probability means exact reproduction of all the observed tracks by the reanalysis product for one particular monsoon season. The overall agreement of all storm tracks captured by ERAI (MERRA) with respect to the observed tracks during the entire span of the analysis period is found to be 25% (29%). This means that 25% (29%) of all LPS tracks in ERAI (MERRA) during 1979–2003 have an exact spatiotemporal match with the observed tracks. The horizontal resolution of the reanalysis data, subjectivity in the observed tracks, and the temporal frequency of the observations and reanalyses are all crucial in determining the probability of track coincidence. Thus, a low value of Pc does not mean that the reanalysis data is not useful; rather, the majority of LPS tracks reproduced by the reanalysis do not show an exact match with the observed tracks. In the case of midlatitude storms, a value of Pc $ 70% is considered as a good agreement between different tracking algorithms (Neu et al. 2013). It shall be noted that Neu et al. (2013) did not compare reanalysis tracks with observed tracks, as in the present study. The value of Pc between ERAI and MERRA
tracks is found to be 70%, indicating that the trajectories of LPS in the two reanalysis products have reasonable agreement. Taking together the LPS numbers, days, and the probability of track coincidence, we can conclude that the reanalysis data are reasonably successful in reproducing the number and life cycle of the LPS systems, but with major disagreements in the trajectories of individual storms compared to the observed trajectory.
c. Spatial pattern of LPS activity The track disagreements between reanalyses and observations may not be crucial in simulating the spatial pattern associated with LPS in the reanalysis. Both ERAI and MERRA reliably reproduce the number and life cycle of the systems. As the LPS involve a considerable range of intensities, SAI, which is an index weighted by the storm intensity (Ajayamohan et al. 2010), is used to construct the spatial density maps of the LPS activity (Fig. 6). The climatological mean spatial pattern of the observed SAI shows the strongest LPS activity over the Indo-Gangetic plain region closer to the Bay of Bengal (Fig. 6a). This observed pattern in SAI is consistent with the composite structure of all LPS trajectories (Ajayamohan et al. 2010). The spatial patterns of SAI in ERAI (Fig. 6b) and MERRA (Fig. 6c) closely resemble
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FIG. 4. Frequency of LPS days during 1979–2003 calculated from the Sikka archive, ERAI and MERRA, and various AMIP model simulations from CMIP5 for the (top) June–September and (bottom) June–August seasons. Correlation of the frequency of LPS days computed using ERAI and MERRA datasets with the Sikka archive are also indicated.
those of observations, with the latter having maxima located slightly farther inland. The spatial patterns of SAI in ERAI and MERRA have spatial correlations of 0.94 and 0.92, respectively, with observed patterns. ERAI has a lower root-mean-square error (22.4) compared to MERRA (30). The spatial densities of SAI in CGCM simulations (Figs. 6d–h) are much weaker compared to observations and reanalyses. Among the CGCMs analyzed here, MIROC5 performs better than others in terms of spatial structure of SAI, with a spatial correlation value of 0.84 with observations. MIROC5 and CCSM4 also have relatively low values of RMSE (33.6 and 33.2, respectively), as compared to other models (;42 and 53). Although MIROC-ESM has a high spatial correlation (r 5 0.8), its RMSE is also very high (53.4) because of the weak amplitude of the SAI. It may be noted that MIROC5 is identified as one of the best among the CMIP5 coupled models for the simulation of the
mean monsoon precipitation (Wang et al. 2013). The present results suggest that the improved simulation of mean monsoon precipitation by the climate models may be linked to their performance in simulating LPS. The AGCM simulations are found to be better in simulating the mean spatial pattern of SAI in a few cases (Fig. 7; e.g., MIROC5 r 5 0.94), which indicates that the errors in ocean–atmosphere coupling processes may affect the simulation of LPS in CGCMs to a certain extent. However, the standalone version of MIROC5 grossly overestimates the intensity of SAI (RMSE 5 98), as compared to its coupled version (RMSE 5 33.6). All models except CCSM4 have overestimated the strength of SAI in AGCM simulations. In summary, AGCM experiments do not show an overall improvement of LPS simulation when compared with CGCM experiments. The time series of the areaaveraged JJAS mean SAI index confirms that ERAI and MERRA capture the observed interannual variability in
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FIG. 5. Probability of track coincidence (%) calculated for LPS systems in ERAI and MERRA, with respect to the Sikka archive. Probability of track coincidence calculated for all LPS during 1979–2003 are indicated.
SAI (r 5 0.69 and 0.54, respectively), consistent with LPS days during the analysis period (Fig. 8). AGCM simulations of MIROC5 and MRI-CGCM3 tend to overestimate the magnitude of SAI, the reason for which is found to be the simulation of a larger number of stronger LPS by these models (figure not shown). When the analysis is restricted to the June–August (JJA) season, ERAI and MERRA yield a better correlation (r 5 0.7) with the observed interannual variability in the SAI index, consistent with the uncertainty found in the month of September (Fig. 3).
The monsoon LPS are the main rain-bearing systems, which bring copious rains over the central Indian region (Sikka 2006; Krishnamurthy and Ajayamohan 2010). Hence, the rainfall associated with LPS days with respect to the total rainfall in a season assumes significance. Figure 9 shows the relation between total JJAS seasonal precipitation (PT) and the LPS-day precipitation (PL) over the core monsoon region. It is to be noted that the observations are available only over the land and, hence, oceanic grids are discarded in
FIG. 6. Spatial density maps of the SAI from (a) the Sikka archive, (b) ERAI, (c) MERRA, (d) MIROC5, (e) CCSM4, (f) CNRM-CM5, (g) ACCESS1.3, and (h) MRI-CGCM3. Values for (d)–(f) are from historical all-forcing experiments of CMIP5 simulations. The spatial patterns are the mean of 1979–2003 JJAS LPS days’ frequency. The spatial correlations and RMSE of the model-simulated SAI index with Sikka archive are indicated. The black box in (a) shows the region (158–258N, 778–908E) selected for calculating the area-averaged SAI in Fig. 9.
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FIG. 7. As in Fig. 6, but (d)–(f) are computed using AGCM experiments (AMIP) of CMIP5 simulations.
these calculations. Almost 60% of the observed total precipitation during 1979–2003 is found to be contributed during the LPS days. The CGCMs also show a strong dependence of PT to PL, with considerable spread in the simulation of ISM precipitation. There seems to be a linear relationship between the skill of the CGCMs in simulating mean monsoons and simulating synoptic activity. The models that realistically simulate monsoon synoptic activity also show skill in the simulation of ISM precipitation (Fig. 9). It is noted that MIROC-ESM and MIROC-ESM-CHEM simulate total seasonal precipitation closer to the observed amount, with substantially less contribution from synoptic activities, suggesting that a few CGCMs have different mechanisms for ISM precipitation simulation. MIROC5 simulates excessive precipitation over the Gangetic plain, with most of it coming from synoptic activities, consistent with the stronger values of the SAI index in that model. In general, CESM1 (BGC), CCSM4, and GFDL-ESM2G models perform better among the CGCMs analyzed here, in terms of the ISM precipitation simulation over the core monsoon region. These models also simulate the precipitation contribution associated with LPS closer to the observations. It may be noted that the models (ACCESS1.3, CSIRO Mk3.6.0, IPSL-CM5B-LR, MRICGCM3, and MRI-ESM1) that have a weaker contribution from LPS-related precipitation are already found as having a strong cold SST bias over the northern Arabian Sea and a dry bias over the Indian land region (Sandeep and Ajayamohan 2014). The weaker monsoon circulation in the models with a strong cold SST bias may affect the LPS activity in those models.
4. Potential factors contributing to intermodel spread in LPS activity It may be difficult to find one single factor that explains the deficiency in simulating synoptic activity across all models. In other words, the biases in LPS simulation may be due to different reasons in different models. However, in order to improve the model performance in future, it is important to have a deeper understanding of the probable factors that are contributing to the biases in LPS simulation. Finer model resolution may be required to resolve the dynamical features of the storms. At the same time, parameterized physics, such as cumulus convection, also plays an important role in representing the processes that are responsible for storm development. In the present study, we do not investigate the effect of subgrid-scale processes on the LPS simulation, as inferring the role of such processes from the various CMIP models is difficult. Instead, the roles of large-scale dynamical features, such as moisture convergence, geostrophic vorticity, and wind shear on the LPS simulation are examined, in addition to the effect of model resolution. Further, the thermodynamical structure of the reanalyzed and simulated LPS is analyzed.
a. Horizontal and vertical resolution of the models The horizontal (latitude 3 longitude) grid spacing of the models considered for this study varies from 1.1258 3 1.1258 to 2.88 3 2.88, and the number of vertical levels ranges between 18 and 80. While the increased horizontal resolution is found to have a positive effect on the model performance, the impact of increased
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FIG. 8. Interannual variability of synoptic activity from the Sikka archive, ERAI and MERRA, and AGCM simulations for (a) JJAS and (b) JJA seasons. Synoptic activity is represented by the SAI index (see text for details) averaged over 158–258N and 778–908E (see the box in Fig. 6a). Correlations of SAI calculated from ERAI and MERRA with the Sikka archive are indicated.
vertical levels is not always clear (Roeckner et al. 2006). A balanced choice of horizontal and vertical resolutions is needed for optimal model performance (Roeckner et al. 2006). In general, the simulation of LPS gets better with decreased grid spacing, as indicated by the correlation of 20.62 between SAI and model resolution (Table 3). However, it is difficult to attribute this correlation to horizontal resolution alone, as the model physics also plays an important role in the LPS simulation. The scale interaction between parameterized physics and model dynamics is hard to elucidate from the analysis of multimodel CMIP5 experiments. Although the increased model resolution helps in reducing numerical errors, a systematic analysis of model integrations at various resolutions is necessary to understand the improvements in the performance of a particular model (Boer et al. 1992). It may be noted that
Sabin et al. (2013) found an improved simulation of monsoon synoptic activity when the horizontal resolution of an AGCM is increased. A negative correlation (r 5 20.42) is obtained between SAI and the number of vertical levels of models, suggesting that CMIP5 models with a higher number of vertical levels are poorer in simulating LPS activity. The models that have more vertical levels are the ones with coarser horizontal resolution. The poor performance of these models may be because of the coarser horizontal resolution rather than the increased vertical levels. It is better to have a balance between the horizontal and vertical resolutions for optimal model performance.
b. Moisture convergence The moisture convergence plays an important role in the development and maintenance of synoptic-scale weather
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the model is important for the simulation of LPS activity. Stronger zg can be considered as a necessary, but not a sufficient, condition for the development of LPS.
d. Spread in wind shear and synoptic systems across model simulations
FIG. 9. Total precipitation index against LPS-day precipitation index. The PL is the sum of precipitation over all grid points in the box bounded by 158–258N and 778–908E for all LPS days during 1979–2003. Similarly, PT is the sum of precipitation for all days during 1 Jun–30 Sep in the same box. Both PL and PT are normalized using observed total precipitation. As observations are available only over land, the oceanic grids are masked for the calculations. The models (indicated by numbers from Table 3) that have a strong cold SST bias over the northern Arabian Sea (Sandeep and Ajayamohan 2014) are indicated in blue color.
systems, such as LPS. The column-integrated seasonal mean moisture convergence of each of the 17 CMIP5 Ð P5100hPa models is calculated as M 5 (1/g) P5850hPa $ (qV) dp, where q is the specific humidity, and V is the vector wind. Seasonal mean (JJAS) M and SAI are found to be moderately correlated (r 5 0.55), suggesting that the moisture convergence alone may not determine the synoptic activity in the models. The models with weaker moisture convergence, in general, tend to have poor synoptic activity. Some of the models have a net divergence of the moisture during the JJAS season, as indicated by the negative values of M (Table 3). The models that have a negative moisture convergence are also the ones already identified as having a strong cold SST bias over the Arabian Sea and weaker ISM circulation (Sandeep and Ajayamohan 2014).
c. Geostrophic vorticity The large-scale vorticity field at low levels (850 hPa) is a feature of ISM circulation. The low-level vorticity of the monsoon circulation also favors the development of LPS. The geostrophic vorticity at 850 hPa is calculated as zg 5 (1/f0)=2F; where F is the geopotential height. SAI and zg are rather strongly correlated (r 5 0.65), indicating that the mean vorticity field at lower levels of
Keshavamurty et al. (1978) suggested that the monsoon disturbances grow by drawing up on zonal kinetic energy. The roles of barotropic, baroclinic, and combined barotropic–baroclinic instability in developing LPS are examined in earlier modeling (Shukla 1978; Mishra and Salvekar 1980; Krishnakumar et al. 1992) and observational studies (Sikka 1977; Sanders 1984). Shukla (1977) argued that the barotropic instability of the mean zonal winds at 150 hPa is the primary mechanism that excites the largest unstable mode in the mean monsoon flow. The strong westward zonal wind shear over the Indian region during the summer monsoon season is vital for the development of the LPS (Goswami et al. 1980). Here we examine the mechanisms responsible for the large intermodel variability seen in the climate models in simulating the LPS activity and, hence, the seasonal mean monsoon. As outlined above, one of the most prominent dynamical features for the generation of LPS is the easterly shear seen in zonal winds over the monsoon trough region. Here we do not attempt to investigate the role of barotropic or baroclinic instability on the development of individual storms; rather, we explore how the mean state zonal winds and synoptic activity are related in CMIP5 models. The ensemble mean zonal wind shear (Fig. 10a) is comparable with that from ERAI. However, the models exhibit a considerable spread in the wind shear, with a standard deviation of about 4 m s21 over the monsoon trough region (Fig. 10b). This suggests that the model-to-model variability in the synoptic activity and wind shear over the monsoon trough region may be linked. The regression pattern of SAI on zonal wind shear shows statistically significant (p , 0.05) slopes over the Indian land region that encompass the monsoon trough (Fig. 10c). The scatterplot between area-averaged SAI and the areaaveraged zonal wind shear shows a moderate correlation of 20.62 (p , 0.05), suggesting that the models with weak wind shear simulate less synoptic activity (Fig. 10d). To unravel the large intermodel variability seen in the easterly zonal wind shear, we further analyzed the vertical structure of zonal winds. The spread in the areaaveraged vertical profiles of seasonal mean zonal wind climatology reveals that the models have a large disagreement in the zonal winds at the 300–150-hPa levels (Fig. 11a). This indicates that the models have a substantial spread in the simulation of the mean strength of the tropical easterly jet (TEJ). A comparison with the
No. of grid points in horizontal (A) 27 840 8192 8192 55 296 55 296 32 768 18 432 12 960 12 960 20 592 9216 32 768 8192 8192 51 200 51 200 13 824 115 680 0.44
Horizontal resolution (lat 3 lon)
1.248 3 1.888 2.818 3 2.818 2.818 3 2.818 0.948 3 1.258 0.948 3 1.258 1.418 3 1.418 1.888 3 1.888 2.008 3 2.508 2.008 3 2.508 1.268 3 2.508 1.888 3 3.758 1.418 3 1.418 2.818 3 2.818 2.818 3 2.818 1.138 3 1.138 1.138 3 1.138 1.888 3 2.508 0.758 3 0.758 20.62
Model/reanalysis product
1) ACCESS1.3* 2) BCC_CSM1.1 3) CanESM2 4) CCSM4 5) CESM1(BGC) 6) CNRM-CM5 7) CSIRO Mk3.6.0* 8) GFDL-ESM2G 9) GFDL-ESM2M 10) IPSL-CM5A-MR 11) IPSL-CM5B-LR* 12) MIROC5 13) MIROC-ESM-CHEM 14) MIROC-ESM 15) MRI-CGCM3* 16) MRI-ESM1* 17) NorESM1-M ERAI Correlation with SAI index
38 26 35 26 26 31 18 24 24 39 39 40 80 80 48 48 26 37 20.42
Vertical levels (B) 1.058 0.213 0.287 1.438 1.438 1.016 0.332 0.311 0.311 0.803 0.359 1.311 0.655 0.655 2.458 2.458 0.359 4.280 0.30
Total No. of grid points (3106) 732.63 315.08 234.06 2126.77 2126.77 1057.03 1024.00 540.00 540.00 528.00 236.31 819.20 102.40 102.40 1066.67 1066.67 531.69 3126.49 0.54
Grid points ratio (A/B) 22.99 21.13 20.21 2.44 2.55 20.33 20.19 1.80 1.40 0.22 22.75 3.33 1.07 1.11 22.01 21.92 1.16 3.18 0.55
Vertically integrated moisture convergence (31025 kg m22 s21) 43.96 26.87 19.78 64.76 73.47 66.58 28.00 64.93 54.45 17.37 10.77 70.45 21.30 25.24 43.50 45.44 73.99 57.00 1.00
SAI index
215.38 212.39 28.53 211.90 211.77 213.62 213.41 212.71 210.31 24.66 21.85 212.84 28.27 27.94 25.45 25.27 213.01 213.70 20.62
Wind shear (U200 2 U850, m s21)
2.78 3.90 2.09 5.30 5.67 5.63 5.61 6.82 5.25 5.49 4.29 8.37 3.39 3.50 4.52 4.38 5.76 5.90 0.65
Geostrophic vorticity (31026 s21)
TABLE 3. Various factors that contribute to the intermodel variance in LPS activity. The models with a strong cold SST bias over the northern Arabian Sea (Sandeep and Ajayamohan 2014) are indicated by an asterisk. (Expansions of model name acronyms are available at http://www.ametsoc.org/PubsAcronymList.)
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FIG. 10. (a) Ensemble mean JJAS zonal wind shear (U200 2 U850, m s21). Contours show the JJAS zonal wind shear from ERAI. (b) Standard deviation in the zonal wind shear among 17 CMIP5 models. (c) Linear regression map generated by regressing the intermodel SAI on zonal wind shear (see section 2d for details of generating this regression map); the stippling shows regression slopes significant at the 95% level. (d) Scatterplot between the area-averaged SAI and zonal wind shear. SAI is area averaged over 158–258N and 778–908E, while zonal wind shear is area averaged over the box shown in (c) (17.58–27.58N, 658–908E).
ERAI wind profile shows that almost all models analyzed here simulate a weaker TEJ. Consistent with previous analysis, the wind profile of MIROC5 is closer to ERAI, while that of IPSL-CM5A-MR (an outlier) is away from ERAI. To examine the dependence of modelto-model variance in LPS simulation on the vertical structure of the zonal wind profile, a linear regression analysis is performed by regressing the area-averaged SAI climatology of 17 models on the wind profile climatology (Fig. 11b). This regression is also done in the same way as the regression map in Fig. 10c, except that the SAI is projected on vertical profiles of area-averaged zonal winds. The vertical structure of the regression coefficient shows that the model-to-model variance in SAI is deeply associated with the spread in the zonal wind profiles between 300 and 150 hPa. This further shows that the models that simulate a weaker TEJ are also the ones with a weaker SAI.
e. Thermodynamical biases The three-dimensional dynamical and vertical structure of the simulated LPS can provide further insights
into the model skill in simulating monsoon synoptic systems. The meridionally averaged (over 10 grids on both sides of the storm center) longitude–height view of the storm-centered composite of meridional wind and potential temperature anomalies is shown in Fig. 12. Consistent with the earlier studies (Hurley and Boos 2015), both ERAI and MERRA show a vertical structure with a cold core at lower levels and a warm core at the upper levels with a southwestward tilt in the meridional winds (Figs. 12a,b, 13). Similar analysis on two CGCMS—MIROC5 and MIROC-ESM—reveals the poor vertical structure in the latter (Fig. 12d), which partly explains its failure in the simulation of LPS, consistent with the mean structure of zonal wind profiles. The MIROC5 simulation shows a dynamical structure of the LPS that is closer to ERAI, with a cold (warm) core in the lower (upper) levels. The development of a well-defined tilted vertical structure of the LPS in MIROC-ESM seems to be curtailed by a weak zonal shear. The warm-over-cold thermal structure of LPS is due to latent heating (evaporative cooling) at the upper (lower) levels. The weaker thermodynamic
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FIG. 11. (a) JJAS mean zonal wind profiles averaged over the monsoon trough (17.58–27.58N and 658–908E). Black, red, magenta, and green colors indicate ensemble mean, ERAI, IPSL-CM5A-MR, and MIROC5, respectively. The error bars (blue horizontal lines) show standard deviation of zonal wind profiles among 17 CGCMs; (b) linear regression of SAI index from 17 models on JJAS mean zonal wind profiles (units are standardized); see section 2d for details of regression technique.
structure in MIROC-ESM suggests that the low-level evaporative cooling and upper-level latent heating are not well simulated in that model. This indicates that the moist processes related to monsoons are not well represented in MIROC-ESM. To get a comprehensive view of the vertical structure of monsoon LPS, the storm-centered composite of the potential temperature anomaly is shown as a threedimensional plot in Fig. 13. The warm-over-cold structure is very clear in the three-dimensional view. When averaged over a latitude domain (10 grids on both sides of the storm center), a cold core extending up to 700 hPa beneath the warm core is visible (meridional plane, Fig. 13). The wind vectors shown in two levels (surface and 200 hPa) indicate the deep first baroclinic structure of the monsoon depressions.
5. Summary and conclusions The monsoon LPS in CGCMs and AGCMs, as well as in ERAI and MERRA, are detected and tracked using a robust tracking algorithm. The reliability of the new tracking method is verified by the fact that it succeeded in extracting LPS information from two independent reanalysis datasets that agree fairly well with observations. The rigorous comparison of the LPS tracks from the reanalysis data with observed LPS tracks, using a trajectory intercomparison protocol, strengthens the confidence in the new tracking technique. Further, the algorithm presented here mimics
the manual method used to derive the observed LPS data from daily synoptic charts. Thus, the LPS data captured by the new technique can be fairly compared with the reanalysis datasets. As the tracking results from the reanalysis products and Sikka archive are similar, the proposed technique is capable of providing useful results when applied to CMIP5 model simulations. The three-dimensional composite of potential temperature anomaly derived from ERAI reveals a finer-scale vertical structure of the LPS, which enhances confidence in using reanalysis data in further exploring the dynamics of monsoon LPS. The skill of CMIP5 coupled models in simulating monsoon LPS is assessed with the help of a newly devised LPS tracking algorithm. Although the biases in the simulation of mean monsoon precipitation are well known (Levine et al. 2013; Sperber et al. 2013; Sandeep and Ajayamohan 2014), the role of model skill in LPS simulation in such biases has not been explored hitherto. The present analysis reveals that the model skill in simulating mean monsoon precipitation is closely linked to how well the models simulate the monsoon synoptic activity. The ratio of LPS-day precipitation to total precipitation is found to be about 60% in reanalyses and a few CGCMs that are skillful in simulating LPS. The models with poor LPS simulation skills have a substantially smaller ratio of LPS-day precipitation to total precipitation, leading to poor simulation of the seasonal mean monsoon. The model-to-model variability in LPS simulation is found to be related to a number of factors,
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FIG. 12. Storm-centered composite vertical structure of anomalous potential temperature (shading; K) and meridional winds (m s21) calculated using (a) ERAI, (b) MERRA, (c) MIROC5, and (d) MIROC-ESM historical all-forcing experiments. Only 1990– 99 data were used for these calculations. The day on which each LPS event achieved maximum intensity is considered for constructing composites. Contours range between 21.5 and 1.5 m s21, with an interval of 0.3 m s21. Positive (negative) contours show southerly (northerly) winds.
such as a model’s horizontal resolution, biases in moisture convergence, geostrophic vorticity, and zonal wind shear. Increasing vertical levels at the expense of horizontal resolution can be counterproductive in realistically simulating LPS. The biases in moisture convergence may be linked to model biases in large-scale circulation features, which are often linked to issues related to parameterized convection (Hwang and Frierson 2013). The biases in zonal wind shear indicate problems related to the simulation tropical easterly jet (TEJ). This hints at the
importance of better representation of TEJ in climate models to improve the simulation of mean monsoon precipitation over India. It may be noted that the subtropical jets are also inadequately represented in climate models (Sandeep and Ajayamohan 2014). The proposed tracking technique is also promising in the context of climate change’s impact on monsoon LPS. The effects of climate change on monsoon LPS are unknown. With the help of the new algorithm, we are analyzing LPS characteristics on future projections by the
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FIG. 13. Three-dimensional thermodynamical structure of monsoon LPS in ERAI as revealed by the storm-centered composite of potential temperature anomalies (K). Blue and black arrows represent anomalous wind vectors at 850 and 200 hPa, respectively. Meridional mean of the thermodynamical structure is shown in the background (as in Fig. 12a).
CMIP5 models, which will be reported elsewhere. Since the tracking algorithm is developed based on surface pressure, there is a rationale for extending this analysis to the twentieth-century reanalysis data products (e.g., Compo et al. 2011) to analyze trends and associated dynamics. Acknowledgments. The Center for Prototype Climate Modeling is fully funded by the Government of Abu Dhabi through a New York University Abu Dhabi (NYUAD) Research Institute grant. The NYUAD high-performance computing resources are used for the computations. We thank Dr. William Boos, Dr. Kevin Walsh, and the two anonymous reviewers for their valuable comments on an earlier version of the manuscript, which led to significant improvement of this paper.
APPENDIX Track Intercomparison Algorithm We calculate the probability of coincidence Pc of tracks in the ERAI and MERRA data using the Blender and Schubert (2000) algorithm. Let the observed LPS track be represented as fF1 (t1 ), l1 (t1 )k1 (t1 )g for latitude (F), longitude (l), and time (k), with time steps t1 5 1, 2, 3, . . . . , T1 . The track in the dataset to be compared (ERAI or MERRA) with the observations may be represented as fF2 (t2 ), l2 (t2 )k2 (t2 )g for time steps t2 5 1, 2, 3, . . . . , T2 . The spatiotemporal distance between the two storm tracks can be calculated as D212 5
1 1 s212 2 (s21 1 s22 ) , T1 T2 2
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where s1 is the variance of the observed LPS tracks, and s2 is that of ERAI or MERRA tracks; s12 is
s212 5
1 T1 T2
ðT 0
1
ðT dt1
2
0
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the variance of the combined tracks, which is computed as
dt2 haf[F1 (t1 ) 2 F2 (t2 )]2 1 [l1 (t1 ) 2 l2 (t2 )]2 g 1 b[k1 (t1 ) 2 k2 (t2 )]2 i ,
where a is a spatial weighting, and b is a temporal weighting; they are related as b 5 U 2 a. In our calculations U 5 10 m s21, and a 5 1 is used. The values s1 and s2 are estimated in the same manner as s12, except that identical paths are used. The probability of coincidence of the tracks Pc is calculated as Pc 5 L/L1, with L1 being the number of LPS of the dataset with fewer LPS. The value L is the sum of identical tracks. REFERENCES Ajayamohan, R. S., and B. N. Goswami, 2007: Dependence of simulation of boreal summer tropical intraseasonal oscillations on the simulation of seasonal mean. J. Atmos. Sci., 64, 460–478, doi:10.1175/JAS3844.1. ——, W. J. Merryfield, and V. V. Kharin, 2010: Increasing trend of synoptic activity and its relationship with extreme rain events over central India. J. Climate, 23, 1004–1013, doi:10.1175/ 2009JCLI2918.1. Balaguru, K., S. Taraphdar, L. R. Leung, and G. R. Foltz, 2014: Increase in the intensity of postmonsoon Bay of Bengal tropical cyclones. Geophys. Res. Lett., 41, 3594–3601, doi:10.1002/2014GL060197. Bengtsson, L., S. Hagemann, and K. I. Hodges, 2004: Can climate trends be calculated from reanalysis data? J. Geophys. Res., 109, D11111, doi:10.1029/2004JD004536. Blender, R., and M. Schubert, 2000: Cyclone tracking in different spatial and temporal resolutions. Mon. Wea. Rev., 128, 377– 384, doi:10.1175/1520-0493(2000)128,0377:CTIDSA.2.0.CO;2. Boer, G. J., and Coauthors, 1992: Some results from an intercomparison of the climates simulated by 14 atmospheric general circulation models. J. Geophys. Res., 97, 12 771–12 786, doi:10.1029/92JD00722. Compo, G. P., and Coauthors, 2011: The Twentieth Century Reanalysis project. Quart. J. Roy. Meteor. Soc., 137, 1–28, doi:10.1002/qj.776. Dash, S. K., R. K. Jenamani, and M. S. Shekhar, 2004: On the decreasing frequency of monsoon depressions over the Indian region. Curr. Sci., 86, 1404–1411. Dee, D. P., and Coauthors, 2011: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system. Quart. J. Roy. Meteor. Soc., 137, 553–597, doi:10.1002/qj.828. Goswami, B. N., R. N. Keshavamurthy, and V. Satyan, 1980: Role of barotropic, baroclinic, and barotropic-baroclinic instability for the growth of monsoon depressions and mid-tropospheric cyclones. Proc. Indian Acad. Sci., Earth Planet. Sci., 89, 79–97, doi:10.1007/BF02841521. Hanley, J., and R. Caballero, 2012: Objective identification and tracking of multicentre cyclones in the ERA-Interim reanalysis dataset. Quart. J. Roy. Meteor. Soc., 138, 612–625, doi:10.1002/ qj.948.
Hodges, K. I., 1994: A general method for tracking analysis and its application to meteorological data. Mon. Wea. Rev., 122, 2573–2586, doi:10.1175/1520-0493(1994)122,2573: AGMFTA.2.0.CO;2. Hurley, J. V., and W. R. Boos, 2015: A global climatology of monsoon low-pressure systems. Quart. J. Roy. Meteor. Soc., doi:10.1002/qj.2447, in press. Hwang, Y.-T., and D. M. W. Frierson, 2013: Link between the double-Intertropical Convergence Zone problem and cloud biases over the Southern Ocean. Proc. Natl. Acad. Sci. USA, 110, 4935–4940, doi:10.1073/pnas.1213302110. Keshavamurty, R. N., G. C. Asnani, P. V. Pillai, and S. K. Das, 1978: Some studies of the growth of monsoon disturbances. Proc. Indian Acad. Sci., Earth Planet. Sci., A87, 61–75, doi:10.1007/BF02839386. Krishnakumar, V., R. N. Keshavamurty, and S. V. Kasture, 1992: Moist baroclinic instability and the growth of monsoon depressions—Linear and nonlinear studies. Proc. Indian Acad. Sci., Earth Planet. Sci., 101, 123–152, doi:10.1007/BF02840349. Krishnamurthy, V., and R. S. Ajayamohan, 2010: Composite structure of monsoon low pressure systems and its relation to Indian rainfall. J. Climate, 23, 4285–4305, doi:10.1175/ 2010JCLI2953.1. Krishnamurti, T. N., M. Kanamitsu, R. Godbole, C.-B. Chang, F. Carr, and J. H. Chow, 1975: Study of a monsoon depression (I): Synoptic structure. J. Meteor. Soc. Japan, 53, 227–239. ——, A. Martin, R. Krishnamurti, A. Simon, A. Thomas, and V. Kumar, 2013: Impacts of enhanced CCN on the organization of convection and recent reduced counts of monsoon depressions. Climate Dyn., 41, 117–134, doi:10.1007/s00382-012-1638-z. Levine, R. C., A. G. Turner, D. Marathayil, and G. M. Martin, 2013: The role of northern Arabian Sea surface temperature biases in CMIP5 model simulations and future projections of Indian summer monsoon rainfall. Climate Dyn., 41, 155–172, doi:10.1007/s00382-012-1656-x. Mishra, S. K., and P. S. Salvekar, 1980: Role of baroclinic instability in the development of monsoon disturbances. J. Atmos. Sci., 37, 383–394, doi:10.1175/1520-0469(1980)037,0383: ROBIIT.2.0.CO;2. Mooley, D. A., 1973: Some aspects of Indian monsoon depressions and the associated rainfall. Mon. Wea. Rev., 101, 271–280, doi:10.1175/1520-0493(1973)101,0271:SAOIMD.2.3.CO;2. ——, and J. Shukla, 1987: Characteristics of the westward-moving summer monsoon low pressure systems over the Indian region and their relationship with the monsoon rainfall. Center for Ocean–Land–Atmosphere Interactions Tech. Rep., 47 pp. [Available from COLA, IGES, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705.] ——, and ——, 1989: Main features of the westward-moving low pressure systems which form over the Indian region during the summer monsoon season and their relation to the monsoon rainfall. Mausam, 40, 137–152. Murray, R. J., and I. Simmonds, 1991: A numerical scheme for tracking cyclone centres from digital data. Part I:
5324
JOURNAL OF CLIMATE
Development and operation of the scheme. Aust. Meteor. Mag., 39, 155–166. Neu, U., and Coauthors, 2013: IMILAST: A community effort to intercompare extratropical cyclone detection and tracking algorithms. Bull. Amer. Meteor. Soc., 94, 529–547, doi:10.1175/ BAMS-D-11-00154.1. NOAA/National Geophysical Data Center, 2006: 2-minute Gridded Global Relief Data (ETOPO2v2). NOAA/National Geophysical Data Center, accessed 2 September 2013. [Available online at http://www.ngdc.noaa.gov/mgg/fliers/06mgg01.html.] Prajeesh, A. G., K. Ashok, and D. V. B. Rao, 2013: Falling monsoon depression frequency: A Gray–Sikka conditions perspective. Sci. Rep., 3, 2989, doi:10.1038/srep02989. Rajeevan, M., U. S. De, and R. K. Prasad, 2000: Decadal variation of sea surface temperatures, cloudiness and monsoon depressions in the north Indian Ocean. Curr. Sci., 79, 283–285. ——, J. Bjate, J. D. Kale, and B. Lal, 2006: High resolution daily gridded rainfall data for the Indian region: Analysis of break and active monsoon spells. Curr. Sci., 91, 296–306. Ramesh, K. V., and P. Goswami, 2014: Assessing reliability of regional climate projections: The case of Indian monsoon. Sci. Rep., 4, 4071, doi:10.1038/srep04071. Rienecker, M. M., and Coauthors, 2011: MERRA: NASA’s ModernEra Retrospective Analysis for Research and Applications. J. Climate, 24, 3624–3648, doi:10.1175/JCLI-D-11-00015.1. Robertson, F. R., M. G. Bosilovich, J. Chen, and T. L. Miller, 2011: The effect of satellite observing system changes on MERRA water and energy fluxes. J. Climate, 24, 5197–5217, doi:10.1175/ 2011JCLI4227.1. Roeckner, E., and Coauthors, 2006: Sensitivity of simulated climate to horizontal and vertical resolution in the ECHAM5 atmosphere model. J. Climate, 19, 3771–3791, doi:10.1175/JCLI3824.1. Sabin, T. P., R. Krishnan, J. Ghattas, S. Denvil, J.-L. Dufresne, F. Hourdin, and T. Pascal, 2013: High resolution simulation of the South Asian monsoon using a variable resolution global climate model. Climate Dyn., 41, 173–194, doi:10.1007/ s00382-012-1658-8. Sabre, M., K. Hodges, K. Laval, J. Polcher, and F. Désalmand, 2000: Simulation of monsoon disturbances in the LMD GCM. Mon. Wea. Rev., 128, 3752–3771, doi:10.1175/ 1520-0493(2001)129,3752:SOMDIT.2.0.CO;2. Sandeep, S., and R. S. Ajayamohan, 2014: Origin of cold bias over the Arabian Sea in climate models. Sci. Rep., 4, 6403, doi:10.1038/srep06403. ——, and ——, 2015: Poleward shift in Indian summer monsoon low level jetstream under global warming. Climate Dyn., 45, 337–351, doi:10.1007/s00382-014-2261-y.
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Sanders, F., 1984: Quasi-geostrophic diagnosis of the monsoon depression of 5–8 July 1979. J. Atmos. Sci., 41, 538–552, doi:10.1175/1520-0469(1984)041,0538:QGDOTM.2.0.CO;2. Shukla, J., 1977: Barotropic-baroclinic instability of mean zonal wind during summer monsoon. Pure Appl. Geophys., 115, 1449–1461, doi:10.1007/BF00874418. ——, 1978: CISK-Barotropic-Baroclinic instability and the growth of monsoon depressions. J. Atmos. Sci., 35, 495–508, doi:10.1175/ 1520-0469(1978)035,0495:CBBIAT.2.0.CO;2. Sikka, D. R., 1977: Some aspects of the life history, structure and movement of monsoon depressions. Pure Appl. Geophys., 115, 1501–1529, doi:10.1007/BF00874421. ——, 2006: A study on the monsoon low pressure systems over the Indian region and their relationship with drought and excess monsoon seasonal rainfall. Center for Ocean–Land– Atmosphere Studies Tech. Rep. 217, 61 pp. [Available from COLA, IGES, 4041 Powder Mill Road, Suite 302, Calverton, MD 20705.] Sinclair, M. R., 1997: Objective identification of cyclones and their circulation intensity, and climatology. Wea. Forecasting, 12, 595–612, doi:10.1175/1520-0434(1997)012,0595: OIOCAT.2.0.CO;2. Sperber, K. R., H. Annamalai, I.-S. Kang, A. Kitoh, A. Moise, A. Turner, B. Wang, and T. Zhou, 2013: The Asian summer monsoon: An intercomparison of CMIP5 vs. CMIP3 simulations of the late 20th century. Climate Dyn., 41, 2711–2744, doi:10.1007/s00382-012-1607-6. Stowasser, M., H. Annamalai, and J. Hafner, 2009: Response of the South Asian summer monsoon to global warming: Mean and synoptic systems. J. Climate, 22, 1014–1036, doi:10.1175/ 2008JCLI2218.1. Taylor, K. E., R. J. Stouffer, and G. A. Meehl, 2012: An overview of CMIP5 and the experiment design. Bull. Amer. Meteor. Soc., 93, 485–498, doi:10.1175/BAMS-D-11-00094.1. Wang, B., J. Liu, H.-J. Kim, P. J. Webster, S.-Y. Yim, and B. Xiang, 2013: Northern Hemisphere summer monsoon intensified by mega-El Niño/Southern Oscillation and Atlantic multidecadal oscillation. Proc. Natl. Acad. Sci. USA, 110, 5347–5352, doi:10.1073/pnas.1219405110. Wernli, H., and C. Schwierz, 2006: Surface cyclones in the ERA-40 dataset (1958–2001). Part I: Novel identification method and global climatology. J. Atmos. Sci., 63, 2486–2507, doi:10.1175/ JAS3766.1. Xavier, P. K., J.-P. Duvel, P. Braconnot, and F. J. Doblas-Reyes, 2010: An evaluation metric for intraseasonal variability and its application to CMIP3 twentieth-century simulations. J. Climate, 23, 3497–3508, doi:10.1175/2010JCLI3260.1.
