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The dynamics of a liquid droplet impacting a liquid film of different compositions is critical for many industrial processes, including additive manufacturing and bio-printing. In this work we present an exposition of droplet impact on liquid films investigating the effects of mismatch in their properties on bouncing-to-merging transitions. Experiments are conducted for two sets of liquid combinations, namely, alkanes and silicon oils. The regime maps for impact outcomes (bouncing vs merging) are created from detailed experiments with various single- and two-liquid systems. The results highlight that the two-liquid systems exhibit an additional merging regime, which is not observed for single-liquid systems. Subsequently, the scaling analyses for transitional boundaries between various regimes are revisited, and new scaling laws are proposed to include the effects of asymmetry in the droplet and film properties. Finally, the experimental results are used to assess the performance of the proposed scaling laws. 

Abstract. Future changes in the El Niño–Southern Oscillation (ENSO) are uncertain, both because future projections differ between climate models and because the large internal variability of ENSO clouds the diagnosis of forced changes in observations and individual climate model simulations. By leveraging 14 single model initial-condition large ensembles (SMILEs), we robustly isolate the time-evolving response of ENSO sea surface temperature (SST) variability to anthropogenic forcing from internal variability in each SMILE. We find nonlinear changes in time in many models and considerable inter-model differences in projected changes in ENSO and the mean-state tropical Pacific zonal SST gradient. We demonstrate a linear relationship between the change in ENSO SST variability and the tropical Pacific zonal SST gradient, although forced changes in the tropical Pacific SST gradient often occur later in the 21st century than changes in ENSO SST variability, which can lead to departures from the linear relationship. Single-forcing SMILEs show a potential contribution of anthropogenic forcing (aerosols and greenhouse gases) to historical changes in ENSO SST variability, while the observed historical strengthening of the tropical Pacific SST gradient sits on the edge of the model spread for those models for which single-forcing SMILEs are available. Our results highlight the value of SMILEs for investigating time-dependent forced responses and inter-model differences in ENSO projections. The nonlinear changes in ENSO SST variability found in many models demonstrate the importance of characterizing this time-dependent behavior, as it implies that ENSO impacts may vary dramatically throughout the 21st century. 

Arabidopsis RESISTANCE TO POWDERY MILDEW 8.2 (RPW8.2) is specifically induced by the powdery mildew (PM) fungus (Golovinomyces cichoracearum) in the infected epidermal cells to activate immunity. However, the mechanism of RPW8.2-induction is not well understood. Here, we identify a G. cichoracearum effector that interacts with RPW8.2, named Gc-RPW8.2 interacting protein 1 (GcR8IP1), by a yeast two-hybrid screen of an Arabidopsis cDNA library. GcR8IP1 physically associated with RPW8.2 with its RING finger domain that is essential and sufficient for the association. GcR8IP1 was secreted and translocated into the nucleus of host cell infected with PM. Association of GcR8IP1 with RPW8.2 led to an increase of RPW8.2 in the nucleus. In turn, the nucleus-localised RPW8.2 promoted the activity of the RPW8.2 promoter, resulting in transcriptional self-amplification of RPW8.2 to boost immunity at infection sites. Additionally, ectopic expression or host-induced gene silencing of GcR8IP1 supported its role as a virulence factor in PM. Altogether, our results reveal a mechanism of RPW8.2-dependent defense strengthening via altered partitioning of RPW8.2 and transcriptional self-amplification triggered by a PM fungal effector, which exemplifies an atypical form of effector-triggered immunity. 

Abstract El Niño and La Niña events show a wide range of durations over the historical record. The predictability of event duration has remained largely unknown, although multiyear events could prolong their climate impacts. To explore the predictability of El Niño and La Niña event duration, multiyear ensemble forecasts are conducted with the Community Earth System Model, version 1 (CESM1). The 10–40-member forecasts are initialized with observed oceanic conditions on 1 March, 1 June, and 1 November of each year during 1954–2015; ensemble spread is created through slight perturbations to the atmospheric initial conditions. The CESM1 predicts the duration of individual El Niño and La Niña events with lead times ranging from 6 to 25 months. In particular, forecasts initialized in November, near the first peak of El Niño or La Niña, can skillfully predict whether the event continues through the second year with 1-yr lead time. The occurrence of multiyear La Niña events can be predicted even earlier with lead times up to 25 months, especially when they are preceded by strong El Niño. The predictability of event duration arises from initial thermocline depth anomalies in the equatorial Pacific, as well as sea surface temperature anomalies within and outside the tropical Pacific. The forecast error growth, on the other hand, originates mainly from atmospheric variability over the North Pacific in boreal winter. The high predictability of event duration indicates the potential for extending 12-month operational forecasts of El Niño and La Niña events by one additional year. 

Abstract Analysis of observational data and a long control simulation of the Community Earth System Model, version 1 (CESM1), shows that El Niño events developing in boreal spring to early summer usually terminate after peaking in winter, whereas those developing after summer tend to persist into the second year. To test the predictability of El Niño duration based on the onset timing, perfect model predictions were conducted for three El Niño events developing in April or September in the CESM1 control simulation. For each event, 30-member ensemble simulations are initialized with the same oceanic conditions in the onset month but with slightly different atmospheric conditions and integrated for 2 years. The CESM1 successfully predicts the termination of El Niño after the peak in 95% of the April-initialized simulations and the continuation of El Niño into the second year in 83% of the September-initialized simulations. The predictable component of El Niño duration arises from the initial oceanic conditions that affect the timing and magnitude of negative feedback within the equatorial Pacific, as well as from the Indian and Atlantic Oceans. The ensemble spread of El Niño duration, on the other hand, originates from surface wind variability over the western equatorial Pacific in spring following the peak. The wind variability causes a larger spread in the September-initialized than the April-initialized ensemble simulations due to weaker negative feedback in spring. These results indicate potential predictability of El Niño events beyond the current operational forecasts by 1 year.