Arindam Chakraborty

Moist Static Energy (MSE) represents the combined effects of moist enthalpy and potential energy in an atmospheric column. Energy balance demands that the net import of radiant and diffusive energy to an atmospheric column is balanced by the divergence of MSE through circulation. Thus, to understand tropical deep convection, such as monsoons, one must investigate, in addition to circulations, the three-dimensional gradient of MSE. Here, using reanalysis data, we decompose a vertical profile of MSE into four terms: its minimum value (h0), and the corresponding pressure level (p0), a component that is symmetric (β), and that is asymmetric (α) about the minimum value. These four degrees of freedom enable us to explain the spatiotemporal variations of the MSE and its divergence in a simplified fashion. The vertically integrated horizontal divergence of MSE flux quantifies the net export of MSE from the atmospheric column, while vertical advection characterizes the upward transport of MSE to upper levels. We show that the climatological MSE transport diagnosed from reanalysis closely resembles that derived from the reconstructed MSE profiles, demonstrating the effectiveness of the proposed framework. Furthermore, alongside the dynamics, we examine the relative contributions of the asymmetric and symmetric components of the MSE profile in modulating the interannual variations of the energy transport, and convection over South Asia and West Africa during the boreal summer, and relate them to climate modes such as the El Niño Southern Oscillation (ENSO), the Tropical Atlantic sea surface temperature (SST) variability, and the Indian Ocean Dipole (IOD).

Monsoons, in spite of being parts of the global-scale Intertropical Convergence Zone (ITCZ), have well-defined regional characteristics. The Indian summer monsoon and the Sahelian summer monsoon are two important components of this global monsoon system that show substantial interannual variations impacting the livelihoods of hundreds of millions of people. Thus, knowledge of how these systems coevolve and interact would help us understand and predict them better. Here, using a century-long raingauge-based rainfall data set, we show that interannual co-variability between the Indian monsoon rainfall (IMR) and Sahelian monsoon rainfall (SMR) has increased in the recent decades (1985–2020; r = 0.58) as compared to the past decades (1950–1984; r = 0.33). We show that intraseasonal waves generated over central India propagate westward and reach West Africa about a couple of weeks later, triggering moist convection over the Sahel. The African Easterly Jet (AEJ), which peaks in the mid-troposphere, plays a crucial role in this wave propagation. A strong AEJ in recent decades, driven by the increased meridional gradient of tropospheric temperature, helps propagate the intraseasonal waves from the Indian to the Sahelian region. Further, an increase in the total column water vapour during the active phases of the Madden-Julian Oscillation over Africa and the Indian Ocean facilitates such propagation. Our results have important implications for understanding interannual variations of the Indian and Sahelian monsoons and should help interpret seasonal and intraseasonal predictions by numerical models.

One of the most prominent weather and climate variations occurs at diurnal time scale, synchronously or asynchronously with the top of the atmosphere insolation. The corresponding variations in energy budget, specifically concerning moist convection, interacts with the mean climate. Using station‐based hourly observations over India, we show that phase of rainfall diurnal cycle during summer monsoon season experienced spatially homogeneous shifts in recent epoch (1992–2014) compared to the past (1969–1991). While the central, north and south peninsular India, comprising about 55% of the stations, experience a delayed phase in recent epoch, an advancement in phase is observed over relatively smaller regions like northeast and northwest India. Further analysis showed that enhanced frequency (intensity) of heavy rainfall events later (earlier) in the afternoon is responsible for the delay (advance) in the mean diurnal phase over central (northwest) India. Our study indicates large‐scale shift in the monsoon diurnal cycle with climate change.

The ENSO Transition Mode (ETM) is a distinct driver of ENSO multidecadal climate variability. The ETM plays an important role in influencing ENSO's seasonal transition from boreal winter to the following summer by affecting zonal winds in the central‐eastern equatorial Pacific Ocean during boreal spring. In this study, we show that ETM's spatial pattern and seasonality are robustly captured by the CMIP6 models' historical simulations. However, they struggle to capture its multidecadal variability. Nonetheless, these models effectively depict ETM's influence on equatorial winds, thereby affecting the seasonal transition of ENSO from boreal winter to the following summer (ΔT). We further demonstrate that the models that more accurately represent the relationship between ETM and ΔT (referred to as “Good ETM models”) are those in which ΔT is less influenced by the preceding winter Niño3.4 sea‐surface temperature anomalies (N34DJF) and largely influenced by phases of ETM. Additionally, these models better capture the 2–7‐year spectral peak observed in N34DJF. On the contrary, Bad ETM models are characterized by a dominant higher‐frequency, 2–3‐year quasi‐biennial peak. Consequently, Good ETM models render ENSO states that align more closely with observations compared with Bad ETM models. This study underscores, through CMIP6 models, the significance of boreal winter ENSO amplitude in influencing how extratropical climate affects the seasonal transitions of ENSO.

El Niño Southern Oscillation (ENSO) is the leading interannual coupled climate mode in the tropical Pacific. The seasonal transition of ENSO from boreal winter to the following summer can significantly affect the global climate. One of the major hurdles in understanding the seasonal transition of ENSO is the spring predictability barrier. Here, we show that ENSO’s seasonal transition is modulated by a multidecadal climate mode of boreal spring sea-level pressure (SLP) in the extratropical Southern Hemisphere. This ENSO transition mode (ETM), when characterised by a decrease in SLP and associated clockwise circulation of the surface winds centred over the southeastern subtropical Pacific Ocean, produces westerly anomalies at the equator. These wind anomalies in the equatorial eastern Pacific aid the seasonal warming of Niño3.4 sea surface temperature anomalies (N34SST) from boreal winter to the following summer. The ETM time series shows prominent multidecadal variations at around 50 years. This creates a conducive environment for alternate cold and warm seasonal transitions leading to multidecadal variations in boreal summer N34SST. Thus, ETM provides a physical insight into the seasonal transition of ENSO and leads to a new paradigm for ENSO evolution beyond its peak. This has implications for seasonal ENSO forecasts and decadal climate predictions.

An ensemble of forecasts is necessary to identify the uncertainty in predicting a non-linear system like climate. While ensemble averages are often used to represent the mean state and diagnose physical mechanisms, they can lead to information loss and inaccurate assessment of the model’s characteristics. We highlight an intriguing case in the seasonal hindcasts of the Climate Forecast System version-2. While all ensemble members often agree on the sign of predicted El Nino Southern Oscillation (ENSO) for a particular season, non-ENSO climate forcings, although present in individual members, are disparate. As a result, an ensemble mean retains ENSO anomalies while diminishing non-ENSO signals. This difference between ENSO and non-ENSO predictions and a more decisive impact of ENSO on seasonal climate increases the ensemble mean ENSO-Indian Summer Monsoon Rainfall correlation. Thus, a model’s teleconnection skills, which often help interpret physical mechanisms, should be studied using individual members rather than ensemble averages.

Soil water‐groundwater interactions are important in determining the moisture profile in a soil column. However, the Noah Land Surface Model (Noah LSM), the land surface component of several general circulation models, does not consider groundwater effects. The present study investigates the impact of integrating a groundwater model into the Noah LSM. By performing experiments forced by observations at the surface, insights into the characteristics of soil moisture evolution with and without the aquifer are investigated. In the absence of surface precipitation, soil moisture is shown to exhibit an exponential decay rate without the aquifer (termed as Noah‐Cntl). This is because evapotranspiration dominates other processes governing the soil moisture evolution. Based on scale analysis, we derived an analytical equation that could well represent the aforementioned exponential variation in soil moisture. Presence of the aquifer (termed as Noah‐GW) makes the capillary processes important as well. The result is a slower decay of soil moisture as compared to Noah‐Cntl without surface precipitation. Soil moisture evolution in both the models is a function of vegetation and soil types. As a result, the aforementioned decay timescale displays significant heterogeneity over the Indian region, with Central India exhibiting relatively faster decay rates. We also performed 24‐year long experiments with observed interannually varying forcing that indicates an enhancement in the annual mean soil moisture at all levels due to groundwater effects. This enhancement is particularly prominent during the post‐monsoon season, reaffirming the results of the drain‐out experiments mentioned above.

Land surface utilization in the Indian subcontinent has undergone dramatic transformations over the years, altering the region’s surface energy flux partitioning. The resulting changes in moisture availability and atmospheric stability can be critical in determining the season’s monsoon rainfall. This study uses fully coupled global climate model simulations with idealized land cover to elucidate the consequences of land surface alterations. We find that an increase in forest cover, in general, increases precipitation in India. However, precipitation is not a linear function of forest-covered-area due to the spatially heterogeneous nature of the impact. A fully forest-covered India receives less precipitation than when the forest covers only the eastern side of India, occupying just about half the area. This signifies the importance of the east-west gradient in vegetation cover observed over India. Using an energy balance model, we diagnose that the diverse nature of this precipitation response results from three different pathways: evaporation from the surface, the net energy input into the atmosphere, and moist stability. Evaporation exhibits a linear relationship with forest-covered-area and reveals minimal spatial heterogeneity. On the contrary, the influence through the other two pathways is found to be region specific. Rainfall modulation via changes in net energy input is dominant in the head Bay of Bengal region, which is susceptible to convective systems. Whereas impact through stability changes is particularly significant south of 20^∘ N. In addition, we find that moisture advection modulates the significance of these pathways over northwest India. Thus, the impact of land cover changes act via three effective mechanisms and are region dependent. The findings in this study have broader ramifications since the dominant region-specific mechanisms identified are expected to be valid for other forcings and are not just limited to the scenarios considered here.