Recent studies suggest that springtime moisture transport into the Arctic can initiate sea ice melt that extends to a large area in the following summer and fall, which can help explain Arctic sea ice interannual variability. Yet the impact from an individual moisture transport event, especially the extreme ones, is unclear on synoptic to intraseasonal time scales and this is the focus of the current study. Springtime extreme moisture transport into the Arctic from a daily data set is found to be dominant over Atlantic longitudes. Lag composite analysis shows that these extreme events are accompanied by a substantial sea ice concentration reduction over the Greenland-Barents-Kara Seas that lasts around a week. Surface air temperature also becomes anomalously high over these seas and cold to the west of Greenland as well as over the interior Eurasian continent. The blocking weather regime over the North Atlantic is mainly responsible for the extreme moisture transport, occupying more than 60% of the total extreme days, while the negative North Atlantic Oscillation regime is hardly observed at all during the extreme transport days. These extreme moisture transport events appear to be preceded by eastward propagating large-scale tropical convective forcing by as long as 2 weeks but with great uncertainty due to lack of statistical significance.

Arctic sea ice extent (SIE) achieves its minimum in September each year and this value has been observed to decline steeply over the satellite era of the past three decades. Yet large year-to-year fluctuations are also present in the September SIE and the mechanisms for this variability are still not clear. Here we address this issue by examining the preconditions in meteorological fields in the previous spring and summer from observations and a large ensemble of historical climate model simulations. The focus of this study is on the impact of anomalous moisture transport into the Arctic and the associated surface energy fluxes on the September SIE. We find that the below-normal September SIE is associated with enhanced moisture transport into the Arctic in spring, which induces downward thermal radiation at the surface. However, in summer, the anomalous moisture transport over the Arctic is divergent due to an anticyclonic atmospheric flow pattern and the ice albedo feedback plays a leading role in sea ice loss.

The intertropical convergence zone (ITCZ) in the east Pacific is located north of the equator during most of the year. In daily data it is most variable in March-April when it may be located north of the equator (nITCZ), on both sides of the equator (dITCZ), or south of the equator (sITCZ), or it may be absent (when convection does not take on a zonally elongated form). Additionally, in strong El Niño years it is located on the equator during the boreal winter half-year. Here the focus is on conditions when the ITCZ has a presence south of the equator (dITCZ, sITCZ) and composites of various fields are compared to "normal conditions" [i.e., when the ITCZ is north of the equator (nITCZ)]. Composites of sea surface temperature (SST), precipitation, outgoing longwave radiation, and the upper-level circulation show very similar patterns for dITCZ and sITCZ days, where the latter cases have almost double the amplitude of the former. The sITCZ state is viewed as an extreme case of the dITCZ state. Both are found to be related to the central Pacific (CP) La Niña with anomalous positive SST and atmospheric heating over the western tropical Pacific and anomalous negative SST and cooling over the central tropical Pacific. Ocean-atmosphere interaction plays an important role in developing the dITCZ and sITCZ anomalies. These daily composite patterns can be reproduced by the regression of monthly fields on the cold CP El Niño-Southern Oscillation mode, suggesting that the interannual rather than day-to-day variability dominates in contributing to the patterns of the composites.

A key question for infectious disease dynamics is the impact of the climate on future burden. Here, we evaluate the climate drivers of respiratory syncytial virus (RSV), an important determinant of disease in young children. We combine a dataset of county-level observations from the US with state-level observations from Mexico, spanning much of the global range of climatological conditions. Using a combination of nonlinear epidemic models with statistical techniques, we find consistent patterns of climate drivers at a continental scale explaining latitudinal differences in the dynamics and timing of local epidemics. Strikingly, estimated effects of precipitation and humidity on transmission mirror prior results for influenza. We couple our model with projections for future climate, to show that temperature-driven increases to humidity may lead to a northward shift in the dynamic patterns observed and that the likelihood of severe outbreaks of RSV hinges on projections for extreme rainfall.

Towards the end of June 2021, temperature records were broken by several degrees Celsius in several cities in the Pacific Northwest areas of the US and Canada, leading to spikes in sudden deaths and sharp increases in emergency calls and hospital visits for heat-related illnesses. Here we present a multi-model, multi-method attribution analysis to investigate the extent to which human-induced climate change has influenced the probability and intensity of extreme heat waves in this region. Based on observations, modelling and a classical statistical approach, the occurrence of a heat wave defined as the maximum daily temperature (TXx) observed in the area 45-52N, 119-123W, was found to be virtually impossible without human-caused climate change. The observed temperatures were so extreme that they lay far outside the range of historical temperature observations. This makes it hard to state with confidence how rare the event was. Using a statistical analysis that assumes that the heat wave is part of the same distribution as previous heat waves in this region led to a first-order estimation of the event frequency of the order of once in 1000 years under current climate conditions. Using this assumption and combining the results from the analysis of climate models and weather observations, we found that such a heat wave event would be at least 150 times less common without human-induced climate change. Also, this heat wave was about 2 C hotter than a 1-in-1000-year heat wave would have been in 1850-1900, when global mean temperatures were 1.2 C cooler than today. Looking into the future, in a world with 2 C of global warming (0.8 C warmer than today), a 1000-year event would be another degree hotter. Our results provide a strong warning: our rapidly warming climate is bringing us into uncharted territory with significant consequences for health, well-being and livelihoods. Adaptation and mitigation are urgently needed to prepare societies for a very different future.