No abstract

Realistic simulation of different modes of atmospheric variability ranging from diurnal cycle to interannual variation in global climate models (GCMs) depends crucially on the convection trigger criteria. In this study, using the data from constrained variational analysis by the Atmospheric System Research program for single-column models (SCM), the performance of the commonly used convective trigger functions in GCMs is evaluated based on the equitable threat score (ETS) value, a widely used forecast verification metric. From the ETS score, three consistently better-performing trigger functions were identified. They are based on the dilute and undilute convective available potential energy (CAPE) generation rate from large-scale forcing in the free troposphere (hereafter dCAPE) and parcel buoyancy at the lifting condensation level (Bechtold scheme). The key variables used to define these trigger functions are examined in detail. It is found that the dilute dCAPE trigger function performs the best consistently in both the tropical and midlatitude convective environment. Analysis of the composite fields of key variables of the trigger functions, based on the correct prediction, overprediction and underprediction of convection, and correct prediction of no-convection cases for convective onset, brings to light some critical factors responsible for the performance of the trigger functions. The lower-tropospheric advective forcing in dilute dCAPE trigger and vertical velocity in Bechtold trigger are identified to be the most importance ones. Suggestions are offered for further improvements.

Monsoon low‐pressure systems (LPSs) contribute to more than half of the total summer monsoon rainfall over central India. As their genesis mechanism is not well understood, the LPS‐related precipitation contribution is ill represented in climate models, which has contributed to the underestimation of rainfall over central India in climate model simulations. Two hundred fifty‐six cases of LPS initiations over the Bay of Bengal during 1979 to 2017summer seasons were analyzed, and it was found that 68% of the systems were initiated in situ, while the remaining 32% were initiated by downstream amplification of wave disturbances from the western Pacific. Detailed analysis reveals that the LPS generated by the two mechanisms have similar dynamic and thermodynamic features. A declining trend is also observed in the number of downstream generated cases indicating that it would become increasingly difficult to predict the initiation of LPS in the future.

Closure is an important component of a mass flux-based convective parameterization scheme, and it determines the amount of convection with the aid of a large-scale variable (closure variable) that is sensitive to convection. In this study, we have evaluated and quantified the relationship between commonly used closure variables and convection for a range of global climate model (GCM) horizontal resolutions, taking convective precipitation and mass flux at 600 hPa as measures for deep convection. We have used cloud-resolving model simulation data to create domain averages representing GCM horizontal resolutions of 128 km, 64 km, 32 km, 16 km, 8 km, and 4 km. Lead-lag correlation analysis shows that except moisture convergence and turbulent kinetic energy, none of the other closure variables evaluated in this study show any relationship with convection for the six subdomain sizes. It is found that the correlation between moisture convergence and convective precipitation is largest when moisture convergence leads convection. This correlation weakens as the subdomain size decreases to 8 km or smaller. Although convective precipitation and mass flux increase with moisture convergence at a given subdomain size, as the subdomain size increases, the rate at which they increase becomes smaller. This suggests that moisture convergence-based closure should scale down the predicted mass flux for a given moisture convergence as GCM resolution increases.

[1] Understanding the underlying dynamics of the Indian summer monsoon (ISM) extremes such as severe droughts is key to improving seasonal prediction of the ISM rainfall. A large number of ISM droughts over the past century occurred unrelated to external forcing like the El Niño-Southern Oscillation (ENSO). In this study, we challenge the perception that the 2009 ISM drought was driven by ENSO and show that it was caused by internally driven processes. The 2009 drought of ISM was the result of two very long breaks, one in the month of June and the other in July-August (JA). While some studies provide strong evidence that the June break was caused by dry air intrusion associated with extratropical waves, a mechanism for the equally important JA break has not been elucidated so far. In this study, we unravel a new process in which westward propagating convectively coupled planetary-scale equatorial Rossby (PSER) waves emanating from the eastern Pacific as a remnant of Madden-Julian Oscillation (MJO), interact with the monsoon intraseasonal oscillation (MISO), modulate the active/break spells, and thereby influence the seasonal mean. It was found that during JA 2009 the arrival of the divergent phase of this PSER mode over the ISM domain reinforced and extended the break condition initiated by the northward propagating MISO, thereby creating a long break. Nonlinear kinetic energy exchanges between the PSER mode and the northward propagating MISO were found to be at the heart of such interactions. Evidence of such interactions can be seen during different active/break events in other monsoon seasons as well. As both long breaks were primarily driven by internal dynamical processes of the atmosphere, the study underscores the major role played by internal dynamics in causing the 2009 ISM drought. Our discovery that interactions between PSER waves and MISO can lead to significant modulations of the active/break spells opens up a new unexplored mechanism for understanding monsoon variability.

A significant fraction of interannual variability (IAV) of the Indian summer monsoon (ISM) is known to be governed by ''internal'' dynamics arising from interactions between high-frequency fluctuations and the annual cycle. While several studies indicate that monsoon intraseasonal oscillations (MISOs) are at the heart of such internal IAV of the monsoon, the exact mechanism through which MISOs influence the seasonal mean monsoon IAV has remained elusive so far. Here it is proposed that exchange of kinetic energy (KE) between the seasonal mean and MISOs provides a conceptual framework for understanding the role of intraseasonal oscillations (ISOs) in causing IAV and interdecadal variability (IDV) of the ISM. The rate of KE exchange between seasonal mean and ISOs is calculated in frequency domain for each Northern Hemispheric summer season over the ISM domain, using 44 yr of the 40-yr ECMWF Re-Analysis (ERA-40) data. The seasonal mean KE and the rate of KE exchange between seasonal mean and ISO shows a significant relationship at both the 850-and 200-hPa pressure levels. Since the rate of KE exchange between seasonal mean and ISO is found to be independent of known external forcing, the variability in seasonal mean KE arising from this exchange process can be considered as an internal component explaining about 20% of IAV and about 50% of IDV. Contrary to the many modeling studies attributing the weakening of tropical circulation to the stabilization of the atmosphere by global warming, this paper provides an alternative view that internal dynamics arising from scale interactions might be playing a significant role in determining the decreasing strength of the monsoon circulation.

[1] Reliable medium range prediction of monsoon weather is crucial for disaster preparedness. Weather in tropics, controlled by fast growing convective instabilities is, however, intrinsically less predictable than that in extratropics. Increased frequency and intensity of extreme rain events in the tropics in the backdrop of global warming has a potential for further decreasing the potential predictability of the tropical weather. Using nonlinear dynamical techniques on gridded daily rainfall data over India for 104 years , here we show that the deterministic predictability of monsoon weather over central India in the latest quarter of the period has indeed decreased significantly compared to that in the earlier three quarters. The decrease of initial error doubling time from approximately 3.0 days to 1.5 days is consistent with higher frequency of extreme events and increased potential instability of the atmosphere in the recent quarter. To overcome the increased difficulty in predicting monsoon weather, significant increase in efforts to improve models, observations and enhancement of computing power would be required. Citation: Mani, N. J., E. Suhas, and B. N.Goswami (2009), Can global warming make Indian monsoon weather less predictable?, Geophys. Res. Lett., 36, L08811,

Most of the global climate models (GCMs) in the Coupled Model Intercomparison Project, phase 5 do not include precipitating ice (aka falling snow) in their radiation calculations. We examine the importance of the radiative effects of precipitating ice on simulated surface wind stress and sea surface temperatures (SSTs) in terms of seasonal variation and in the evolution of central Pacific El Niño (CP‐El Niño) events. Using controlled simulations with the CESM1 model, we show that the exclusion of precipitating ice radiative effects generates a persistent excessive upper‐level radiative cooling and an increasingly unstable atmosphere over convective regions such as the western Pacific and tropical convergence zones. The invigorated convection leads to persistent anomalous low‐level outflows which weaken the easterly trade winds, reducing upper‐ocean mixing and leading to a positive SST bias in the model mean state. In CP‐El Niño events, this means that outflow from the modeled convection in the central Pacific reduces winds to the east, allowing unrealistic eastward propagation of warm SST anomalies following the peak in CP‐El Niño activity. Including the radiative effects of precipitating ice reduces these model biases and improves the simulated life cycle of the CP‐El Niño. Improved simulations of present‐day tropical seasonal variations and CP‐El Niño events would increase the confidence in simulating their future behavior.