In an attempt to develop a better simulation of the climatology of monsoon precipitation in climate models, this paper investigates the impacts of different convective closures on systematic biases of an Indian monsoon precipitation climatology in a high-resolution regional climate model. For this purpose, the Weather Research Forecast (WRF) model is run at 45-and 15-km (two-way nested) resolution with three convective parameterization schemes, namely the Grell-Devenyi (GD), the Betts-Miller-Janjić (BMJ), and the Kain-Fritsch (KF), for the period 1 May-31 October 2001-07. The model is forced with the NCEP-NCAR reanalysis data as the initial and boundary conditions. The simulated June-September (JJAS) mean monsoon rainfall with the three convective schemes is compared with the observations. KF is found to have a high moist bias over the central and western coastal Indian region while GD shows the opposite. Among the three, BMJ is able to produce a reasonable mean monsoon pattern. In an attempt to get further insight into the seasonal bias and its evolution, the probability distribution function (PDF) of different rain-rate categories and their percentage contribution to the seasonal total are computed. BMJ and KF underestimate the observations for lighter rain rates and overestimate for rain-rate categories of more than 10 mm day 21 . GD shows an overestimation for lighter rain and an underestimation of PDF for moderate categories. The seasonal patterns of evolution of PDF plots of three rain-rate categories are analyzed to determine whether the convective schemes show any systematic bias throughout the season or if they have problems during certain phases of the monsoon. This shows that the GD systematically overestimates the lighter rain rate and underestimates the moderate rain rate throughout the season, whereas BMJ and KF have problems in the initial stages. The heavy rain category is systematically overestimated by the KF compared to the other two. To further evaluate the proportionate contribution of each rain-rate bin to the total rain, the percentage contribution of each rain rate to the seasonal total is computed. Analyzing all the rain-rate simulations produced by the three schemes, it is found that KF has a moist bias and GD has a dry bias in the spatiotemporal distribution of the monsoon precipitation. Further, this paper investigates the causes behind the mean monsoon precipitation bias. It is shown that GD produces a model climate where the vertical velocity is less than that of the observations up to 500 hPa and the vertically integrated moist instability is also weaker. KF, on the other hand, shows a higher than the observed vertical velocity and a stronger moist instability. Along with this, the vertical profile of heating suggests a warmer middle level in the KF case and significantly reduced midlevel heating for GD. Thus, KF (GD) has produced a model atmosphere that has a stronger (weaker) convective instability to produce the observed bias in the model precipitation. BMJ is found to s...

The postmonsoon (October–November) tropical cyclone (TC) season in the Bay of Bengal (BoB) has spawned many of the deadliest storms in recorded history. Here it is shown that the intensity of major TCs (wind speed > 49 m s−1) in the postmonsoon BoB increased during 1981–2010. It is found that changes in environmental parameters are responsible for the observed increases in TC intensity. Increases in sea surface temperature and upper ocean heat content made the ocean more conducive to TC intensification, while enhanced convective instability made the atmosphere more favorable for the growth of TCs. The largest changes in the atmosphere and ocean occurred in the eastern BoB, where nearly all major TCs form. These changes are part of positive linear trends, suggesting that the intensity of postmonsoon BoB TCs may continue to increase in the future.

This study examines the linkage between the Madden-Julian oscillation (MJO) and the South Asian summer monsoon onset through spatiotemporal wave-filtering and composite analyses of observational data sets during 1979-2016. We identify two major factors in determining the summer monsoon onset timing: the background conditions associated with seasonal transitions and the intraseasonal variations associated with an active MJO. The background conditions undergo sharp seasonal transition over the western Indian Ocean two to three pentads before the onset dates, while a typical monsoon onset is often associated with the arrival of the wet phase of the tropical MJO over the Indian Ocean, likely due to the promotion by the MJO-initiated eastward propagating westerly wind anomaly. Conversely, the atmospheric circulation associated with the leading dry phase of a strong MJO during the climatological mean onset dates may lead to a delayed monsoon onset.Plain Language Summary The Madden-Julian oscillation (MJO) is one of the dominant modes of intraseasonal variabilities in the tropical atmosphere, while the Asian summer monsoon represents one of the most significant transitions in the large-scale atmospheric and oceanic circulations in the Indo-Pacific regions and beyond. This study examines the linkage between MJO and the South Asian summer monsoon onset through composite analysis of decades of observational data sets along with a global space-time wave-filtering technique. We identify two major factors in determining the summer monsoon onset timing: the background conditions associated with seasonal transitions and the intraseasonal variations associated with an active MJO. The onset of the Indian summer monsoon is often associated with the arrival of the wet phase of the tropical MJO over the Indian Ocean, while a delayed monsoon onset is likely caused by the dry phase of a strong MJO that suppresses background changes associated with seasonal transition during the climatological mean onset dates.

The intense sea surface temperature cooling caused by tropical cyclone‐induced mixing lasts several weeks and may thus influence a later cyclone passing over it. Using a 28 year analysis spanning the North Atlantic, eastern Pacific, and Northwest Pacific, we systematically demonstrate that, on average, when tropical cyclones encounter lingering wakes, they experience sea surface temperatures that are ∼0.25–0.5°C colder. Consequently, the intensification rates are ∼0.4−0.7normalm1emnormals−1360.3emnormalh lower for cyclones when they interact with wakes, consistent with the maximum potential intensity theory. The probability for cyclones to encounter lingering wakes varies positively with cyclone frequency, is ∼10% on average, and has been as high as 27%–37% in the past. These large interaction probabilities reduce the mean intensification rates for cyclones by 3%–6% on average and by ∼12%–15% during the most active years. “Cyclone‐cyclone interactions” may therefore represent a mechanism through which tropical cyclones self‐regulate their activity to an extent on intraseasonal time scales.