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Diurnal warm layers develop in the upper ocean on sunny days with low surface wind speeds. They rectify intraseasonal sea‐surface temperatures (SSTs), potentially impacting intraseasonal weather patterns such as the Madden–Julian Oscillation (MJO). Here we analyse 15‐lead‐day forecast composites of coupled ocean–atmosphere and atmosphere‐only numerical weather prediction (NWP) models of the UK Met Office to reveal that the presence of diurnal warming of SST (dSST) leads to a faster MJO propagation in the coupled model compared with the atmosphere‐only model. To test the feedback between the MJO and the dSST, we designed a set of experiments with instantaneous vertical mixing over the top 5 or 10 m$$ 10\;\mathrm{m} $$ of the ocean component of the coupled model. Weaker dSST in the mixing experiments leads to a slower MJO over 15 lead days. The dSST produces a 3%$$ 3\% $$ increase in the MJO phase speed between the coupled and the atmosphere‐only model. An additional 5%$$ 5\% $$ increase is found for other coupling effects, unrelated to the dSST. A two‐way feedback manifests in the coupled model over the 15 lead days of the forecast between the MJO and the dSST. The MJO regime dictates the strength of the dSST and the dSST rectifies the intraseasonal anomalies of SST in the coupled model. Stronger dSST in the coupled model leads to stronger intraseasonal anomalies of SST. The MJO convection responds to these SSTs on a seven‐lead‐day timescale, and feeds back into the SST anomalies within the next three lead days. Overall, this study demonstrates the importance of high vertical resolution in the upper ocean for predicting the eastward propagation of the MJO in an NWP setting, which is potentially impactful for seasonal predictions and climate projections, should this feedback be unrepresented in the models. [ABSTRACT FROM AUTHOR]

The Madden–Julian Oscillation (MJO) is the leading mode of sub‐seasonal variability in the tropical atmosphere and is a source of predictability for extratropical weather through its teleconnections. MJO teleconnection patterns can be modulated by the El Niño–Southern Oscillation (ENSO) on seasonal to interannual time scales. However, changes over decadal time scales are less well understood. ERA5 reanalysis data are used to show that the boreal winter MJO teleconnection pattern in the Northern Hemisphere has changed in recent decades in line with changes in the Pacific Decadal Oscillation and Atlantic Multidecadal Variability. Changes are seen in the circulation, temperature and precipitation responses. In particular, from 1997, intraseasonal cold anomalies appear over Europe and the eastern United States due to MJO convection over the western Pacific; these were not present 20 years previously. The decadal variability observed is not the product of aliasing of ENSO modulation of the teleconnection. Plain Language Summary: Weather in different regions of the globe can be linked by planetary‐scale atmospheric waves, and these links can help forecasters to predict the weather. One such link, or teleconnection pattern, connects changes in rainfall over Indonesia and the tropical Pacific (from a weather system called the Madden–Julian Oscillation or MJO) to changes in the weather in North America and Europe. This study assesses this teleconnection pattern in two separate time periods (roughly the mid‐1970s to mid‐1990s and mid‐1990s to late 2010s) to analyze if and how it has changed. We find that the pattern has changed, and that this is due to large‐scale changes in the background state of the atmosphere. These changes in the link between the tropics and extratropics will have implications for weather forecasts on weekly to monthly time scales. Key Points: The extratropical response to the Madden‐Julian Oscillation has changed on decadal time scalesThis decadal variability coincides with changes in low‐frequency oceanic modes in both the Pacific and Atlantic basinsChanges on decadal time scales are different to those modulated by the El Niño‐Southern Oscillation on interannual scales [ABSTRACT FROM AUTHOR]

The canonical view of the Maritime Continent (MC) diurnal cycle is deep convection occurring over land during the afternoon and evening, tending to propagate offshore overnight. However, there is considerable day-to-day variability in the convection, and the mechanism of the offshore propagation is not well understood. We test the hypothesis that large-scale drivers such as ENSO, the MJO, and equatorial waves, through their modification of the local circulation, can modify the direction or strength of the propagation, or prevent the deep convection from triggering in the first place. Taking a local-to-large scale approach, we use in situ observations, satellite data, and reanalyses for five MC coastal regions, and show that the occurrence of the diurnal convection and its offshore propagation is closely tied to coastal wind regimes that we define using the k-means cluster algorithm. Strong prevailing onshore winds are associated with a suppressed diurnal cycle of precipitation, while prevailing offshore winds are associated with an active diurnal cycle, offshore propagation of convection, and a greater risk of extreme rainfall. ENSO, the MJO, equatorial Rossby waves, and westward mixed Rossby–gravity waves have varying levels of control over which coastal wind regime occurs, and therefore on precipitation, depending on the MC coastline in question. The large-scale drivers associated with dry and wet regimes are summarized for each location as a reference for forecasters. Significance Statement: Extreme precipitation can be life-threatening in the Maritime Continent region, for example, due to flash floods and landslides. The main form of variability of convective storms is the diurnal cycle, but this can be modulated by large-scale weather drivers. By quantifying the effect of these drivers on local-scale weather regimes for a range of Maritime Continent locations, we identify which drivers are most important (and in which phase) to consider when understanding the local risk of extreme rainfall. Given that these large-scale drivers may be forecast with greater skill than is possible for quantitative precipitation forecasts, this study provides crucial extra information for forecasters to aid prediction of life-threatening weather conditions. [ABSTRACT FROM AUTHOR]

The Maritime Continent (MC) is a region subject to high impact weather (HIW) events, which are still poorly predicted by numerical weather prediction (NWP) models. To improve predictability of such events, NWP needs to be evaluated against accurate measures of extreme precipitation across the whole MC. With its global spatial coverage at high spatio‐temporal resolution, the Global Precipitation Measurement (GPM) data set is a suitable candidate. Here we evaluate extreme precipitation in the Integrated Multi‐Satellite Retrieval for GPM (IMERG) V06B product against station data from the Global Historical Climatology Network in Malaysia and the Philippines. We find that the high intragrid spatial variability of precipitation extremes results in large spatial sampling errors when each IMERG grid box is compared with individual co‐located precipitation measurements, a result that may explain discrepancies found in earlier studies in the MC. Overall, IMERG daily precipitation is similar to station precipitation between the 85th and 95th percentile, but tends to overestimate above the 95th. IMERG data were also compared with radar data in western Peninsular Malaysia for sub‐daily timescales. Allowing for uncertainties in radar data, the analysis suggests that the 95th percentile is still suitable for NWP evaluation of extreme sub‐daily precipitation, but that the rainfall rates diverge at higher percentiles. Hence, our overall recommendation is that the 95th percentile be used to evaluate NWP forecasts of HIW on daily and sub‐daily time scales against IMERG data, but that higher percentiles (i.e., more extreme precipitation) be treated with caution. Plain Language Summary: Extreme rainfall is a major hazard in many parts of the tropics, leading to flooding and social and economic impacts. Accurate weather forecasting of extreme rainfall events is needed by national and regional government planners and disaster relief organizations, as well as by agriculture and industry. The skill of weather forecast computer models needs to be tested against a reliable data set of observed rainfall, so that scientists can improve the models to give better forecasts of extreme rainfall. Observed rainfall data sets need to be evaluated prior to their use for testing models. Here, we evaluate the reliability of the Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) rainfall data set for this purpose. IMERG is based on satellite and rain gauge measurements of rainfall from across the planet. We focus on the area known as the western Maritime Continent. After comparing IMERG rainfall against local measurements of rainfall from weather radar in Malaysia, and weather station data across the region, the recommendation is that IMERG can be used as a reliable measure of fairly extreme rainfall (the top 5% of daily rainfall totals), but tends to overestimate and therefore should be used with caution for very extreme rainfall (the top 1% of daily rainfall totals). Key Points: Spatial sampling error severely affects the comparison of Integrated Multi‐Satellite Retrieval for Global Precipitation Measurement (IMERG) data with pointwise precipitationThe 95th percentile is the optimum choice for comparison of numerical weather prediction precipitation extremes against IMERGAbove the 95th percentile, IMERG overestimates daily precipitation rates compared with rain gauges [ABSTRACT FROM AUTHOR]

Intense rainfall over active volcanoes is known to trigger dangerous volcanic hazards, from remobilizing loose volcanic surface material into lahars or mudflows to initiating explosive activity including pyroclastic flows at certain dome-forming volcanoes. However, the effect of the heated volcanic surface on the atmospheric circulation, including any feedback with precipitation, is unknown. This is investigated here, using the Weather Research and Forecasting (WRF) Model. The recent activity at the Soufrière Hills Volcano (SHV), Montserrat, is a well-documented case of such rainfall-volcano interaction and is used as a template for these experiments. The volcano is represented in the model by an idealized Gaussian mountain, with an imposed realistic surface temperature anomaly on the volcano summit. A robust increase in precipitation over the volcano is simulated for surface temperature anomalies above approximately 40°C, an area-average value that is exceeded at the SHV. For wind speeds less than 4 m s−1 and a range of realistic atmospheric conditions, the precipitation increase is well above the threshold required to trigger volcanic hazards (5-10 mm h−1). Hence, the thermal atmospheric forcing due to an active, but nonerupting, volcano appears to be an important factor in rainfall-volcano interactions and should be taken account of in future hazard studies. [ABSTRACT FROM AUTHOR]