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In this study, we demonstrate a new mechanism, on how the warm phase of El Niño and Southern Oscillation (ENSO) delays the Indian Summer Monsoon onset through coupled ocean-atmospheric processes. Strong basin-wide warming is prominent over North Indian Ocean (NIO) during the El Niño years. The warming is intense over the South West Indian Ocean (SWIO) due to the westward propagation of the equatorial Rossby waves from the Pacific Ocean. It increases the convective activities over the southern tropical Indian Ocean (0–10° S), adjacent to the SWIO region. The warming over the SWIO and the NIO strengthens the divergent wind from the Indian Ocean to the sub-tropics via a wind-evaporation-SST feedback mechanism, which causes more upper-level convergence within 30° N latitudes. Besides, a warmer Indian Ocean enhances the upper-level diabatic heating over the southern Arabian Peninsula and Eastern Indian landmass. These factors strengthen but shift the local Hadley circulation over the Indian Ocean more southward, with an ascending branch centered over the SWIO region. The shifting of the local Hadley circulation during the El Niño years causes the Sub Tropical Jet (STJ) to migrate more southward and centered more over the Indian subcontinent. This southward movement of the STJ over the Indian subcontinent in response to the El Niño condition inhibits the establishment and propagation of the tropical easterly jet during the monsoon season, which subsequently hinders the monsoon circulation, thus delays its onset.

In this study, we investigate the spatiotemporal characteristics of drought in India and its impact on agriculture during the summer season （April-September）. In the analysis, we use Standardized Precipitation Evapotranspiration Index （SPEI） datasets between 1982 and 2012 at the six-monthly timescale. Based on the criterion SPEI 〈 -1, we obtain a map of the number of occurrences of drought and find that the humid subtropical Upper Middle Gangetic Plain （UMGP） region is highly drought-prone, with an occurrence frequency of 40%-45%. This UMGP region contributes at least 18%-20% of India＇s annual cereal production. Not only the probability of drought, but the UMGP region has become increasingly drought-prone in recent decades. Moreover, cereal production in the UMGP region has experienced a gradual declining trend from 2000 onwards, which is consistent with the increase in drought-affected areas from 20%-25% to 50%-60%, before and after 2000, respectively. A higher correlation coefficient （-0.69） between the cereal production changes and drought-affected areas confirms that at least 50% of the agricultural （cereal） losses are associated with drought. While analyzing the individual impact of precipitation and surface temperature on SPEI at 6 month timescale [SPEI （6）] we find that, in the UMGP region, surface temperature plays the primary role in the lowering of the SPEI. The linkage is further confirmed by correlation analysis between SPEI （6） and surface temperature, which exhibits strong negative values in the UMGP region. Higher temperatures may have caused more evaporation and drying, which therefore increased the area affected by drought in recent decades.

Community Earth System Model-Large Ensemble (CESM-LE) simulations are used to partition the Surface Air Temperature (SAT) trends over East Asia into the contribution of external forcing factors and internal variability. In the historical period (1966–2005), the summer SAT trends display a considerable diversity (≤−2 °C to ≥2 °C) across the 35 member ensembles, while under the RCP8.5 scenario, the region is mostly dominated by a strong warming trend (~1.5–2.5 °C/51 years) and touches the ~4 °C mark by the end of the 21st century. In the historical period, the warming is prominent over the Yangtze River basin of China, while under the RCP8.5 scenario, the warming pattern shifts northward towards Mongolia. In the historical period, the Signal-to-Noise Ratio (SNR) is less than 1, while it is higher than 4 under the RCP8.5 scenario, which indicates that, in the early period, internal variability overrides the forced response and vice versa under the RCP8.5 scenario. In addition, over much of the East Asian region, the chances of cooling are relatively high in the historical period, which partially counteracted the warming trend due to external forcing factors. In contrast, under the RCP8.5 scenario, the chances of warming reach ~100% over East Asia due to contributions from the external forcing factors. The novel aspect of the current study is that, in the negative phase (from the mid-1960s to ~2000), the Atlantic Multidecadal Oscillation (AMO) accounts for ~70–80% of the cooling trend or the SAT variability over East Asia, and thereafter, natural variability exhibits a slow increasing trend in the future. However, the contribution of external forcing factors increases from ~55% in 2000 to 95% in 2075 at a rate much faster than natural variability, which is primarily due to increasing downward solar radiation fluxes and albedo feedback on SAT over East Asia.

A correction to this paper has been published: https://doi.org/10.1007/s00382-021-05721-z

Here we investigate the key features of early and late South China Sea summer monsoon (SCSSM) onset. To carry out the investigation, we select five extreme early and nine extreme late onset years (14 days or more, before and after the normal onset date are considered “late” and “early” onset years, respectively). Since the mean SCSSM onset occurs in May, we analyze the changes in composite mean meteorological fields during May. Next, we examine the differences in daily evolution (from 10 to 2 days before the onset) of various vital variables between the early and late SCSSM onset years. We demonstrate that during the early SCSSM onset years, surface air temperature (SAT) is warmer over the North Pacific Ocean, South China Sea (SCS), and Northern China, which gradually extend towards East Asia. These SAT anomalies over Northern China, East Asia, and SCS are due to the advection of warm anomalies from the North Pacific Ocean. Consequently, a dipole-like sea level pressure (SLP) pattern had developed, with lower SLP over East Asia, Northern China, SCS, and North Pacific Ocean, and higher SLP over the Indian Ocean. It subsequently brought moisture-laden wind from the Indian Ocean towards SCS and East Asian landmass and is responsible for early SCSSM onset.

The second empirical orthogonal function mode (EOF2) of winter surface air temperature (SAT) over 0°–180° E, 40°–90° N during 1979–2005 is defined as warm Arctic-cold Eurasia (WACE) pattern. The present study evaluates the performance of 25 Coupled Model Inter-comparison Project Phase 5 (CMIP5) models in simulating the WACE pattern based on historical runs. There exist large inter-model spreads in the simulation of the WACE pattern. Analyses show that the ability of a CMIP5 model in capturing the WACE pattern is connected with the model’s performance in representing the observed atmospheric circulation anomalies related to the winter sea ice concentration (SIC) variation over Barents–Kara Seas. Sea ice loss over Barents–Kara Seas can induce significant positive geopotential height anomalies over Arctic region and negative geopotential height anomalies around the Baikal Lake, resulting in warm anomalies over Barents–Kara Seas and cold anomalies over Eurasia. Further analysis shows that CMIP5 model’s performance in representing the SAT anomalies related to the WACE pattern is partly due to simulation of the amplitude of winter SIC variability over Barents–Kara Seas. Larger standard deviations of winter SIC over Barents–Kara Seas can instigate stationary wave-train more easily, which further induces the SAT anomalies.

The Kalahari high-pressure system that forms the Kalahari and Namib Deserts of Southern Africa is maintained by the sinking motion of the Hadley circulation. Despite projected desertification under the climate change, beginning in the early 21st century the Kalahari Desert and grasslands of South Africa, Lesotho–Drakensberg highland and Eswatini has experienced a trend of greening/Savannisation. Here, we find that the disparity is likely due to strong multidecadal variability. A positive phase of the Atlantic multidecadal oscillation during this period has intensified the Hadley circulation and widened the Kalahari anticyclone, thereby facilitating moisture transport from the warm Angola–Benguela front to southern African landmasses. The advected moisture brought wetness at its periphery, which satisfied the water demand for cropland expansion (∼10%–15%) in southern Africa. A water-food-economy nexus is increasing Africa’s crop yields by approximately 25%, reducing food and economic insecurity through a nearly fourfold increase in agricultural exports and contributing approximately 26% to the continent’s total agricultural output. However, in the opposite phase of multidecadal variability, the superposition of greenhouse warming would exacerbate the drying trend that will amplify aridity.

This study investigates the reproducibility of the spatial structure and amplitude of the observed Pacific–Japan (PJ) pattern in the phase 5 of the Coupled Model Intercomparison Project (CMIP5) models. In particular, the role of sea surface temperature anomalies (SSTAs) and atmospheric mean flow in the diverse reproducibility of the PJ pattern among models is investigated. Based on the pattern correlation between simulated and observed PJ patterns, models are categorized into high and low correlation groups, referred to as HCG and LCG, respectively. The observed cold SSTAs in the western North Pacific (WNP) and equatorial central Pacific, organized convection and precipitation anomalies, and Rossby wave response are reproduced well in HCG models, whereas these features are not present in LCG models. The summer SSTAs are closely tied to the preceding El Niño–Southern Oscillation and its temporal evolution in the tropical Indo-Pacific Ocean in both observations and models, but the SSTAs in the Indian Ocean are weak in both HCG and LCG, implying a weak Indian Ocean capacitor effect. As a result, the reproducibility of the amplitude of the WNP center of the PJ pattern is mainly modulated by the SSTAs and local air–sea feedback over the WNP in the models. On the other hand, a model with stronger climatological southerly along the coast of East Asia tends to produce more realistic amplitude of the midlatitude center of the PJ pattern with clearer poleward wave-activity fluxes due to more efficient local barotropic energy conversion from the mean flow.

Carbon sinks in the tropical rainforests are restricting the global warming to attain unprecedented heights. However, deforestation and climate change is switching them to a net carbon source at some of the deforested patches. Using machine learning algorithm we predict that more than 50% of the tropical rainforests will undergo rapid “Savannisation”/transformation by the end of 21 st century under high emission scenarios. Climate change projects ‘El Niño-like’ warming condition, which decreases precipitation in the rainforests and favors atmospheric dryness. In Central Amazonia vegetation degradation saturates the carbon sink and more than 25% of the rainforests will transform into a net carbon source due to increase in soil microbial respiration. This transition will accelerate if Eastern Pacific/Global temperature warms beyond 1.5 ◦ K/2.3 ◦ K (by 2050’s) and will undergo a steeper transit by ~2075 (2.45 ◦ K/3.8 ◦ K warming). This alteration will exacerbate global warming and has consequences for policies that are intended to stabilize Earth’s climate.

40% of global population, who resides in Asian monsoon region is at high risk from extreme hot summer events, which is expected to increase by 25%/30 years under RCP8.5 scenario. Using Community Earth System Model (CESM) Large-ensemble simulations we assess the relative contribution of external forcings and internal variability on hot extremes over South and East Asia. Climate change projects surface mean temperature to reach 2.0 °C and 5.0 °C by ~2050 and ~2100, respectively, making the region uninhabitable under exposed conditions. Internal variability will partly obscure anthropogenic warming over South and Southeast Asia; however, East Asia will experience a 4–6 fold rise in record breaking hot events in later periods. Nevertheless, beyond 2.35 °C warming internal variability will decrease over South Asia due to weaker albedo feedback on unforced internal variability. Our results contradict the existing hypothesis that warming will increase volatility in weather patterns everywhere, particularly the Asian monsoon regions.