The Indian summer monsoon in MetUM-GOML2.0: effects of air–sea coupling and resolution

The fidelity of the simulated Indian summer monsoon is analysed in the UK Met Office Unified Model Global Ocean Mixed Layer configuration (MetUM-GOML2.0) in terms of its boreal summer mean state and propagation of the boreal summer intraseasonal oscillation (BSISO). The model produces substantial biases in mean June–September precipitation, especially over India, in common with other MetUM configurations. Using a correction technique to constrain the mean seasonal cycle of ocean temperature and salinity, the effects of regional air–sea coupling and atmospheric horizontal resolution are investigated. Introducing coupling in the Indian Ocean degrades the atmospheric basic state compared with prescribing the observed seasonal cycle of sea surface temperature (SST). This degradation of the mean state is attributable to small errors (±0.5°C) in mean SST. Coupling slightly improves some aspects of the simulation of northward BSISO propagation over the Indian Ocean, Bay of Bengal, and India, but degrades others. Increasing resolution from 200 to 90km grid spacing (approximate value at the Equator) improves the atmospheric mean state, but increasing resolution again to 40km offers no substantial improvement. The improvement to intraseasonal propagation at finer resolution is similar to that due to coupling

Isolating the effects of moisture entrainment on convectively coupled equatorial waves in an aquaplanet GCM

The rate of humidity entrainment in the convective parametrization scheme in a general circulation model affects the simulation of convectively-coupled waves. However, it is unclear whether this is caused directly by the effects of entrainment on waves or indirectly through associated impacts such as on the basic state. Therefore, using an aquaplanet model, we employ a novel framework in which we entrain a weighted average of the resolved humidity field and a prescribed zonally symmetric field, with the weighting controlled by a decoupling parameter. Hence, we can vary the entrainment rate of basic state humidity independently of the entrainment of humidity perturbations, simultaneously minimizing changes in basic state. Thus we isolate the effect of moisture entrainment on the waves. Enhancing entrainment rate increases spectral power over all zonal wavenumbers and frequencies, with an increase in the ratio of eastward-to-westward power. The Kelvin wave speed decreases as entrainment increases, which can be partially accounted for by an associated change in basic state humidity. Increasing the decoupling parameter reduces spectral power in Kelvin waves relative to the background, with only long waves still prominent when entrainment is almost fully decoupled from the resolved moisture field, suggesting the wave structure in humidity is required for convection to organize into short wave structures. For long waves the increase in the ratio of eastward-to-westward power as entrainment rate increases cannot be explained by the changes in the coupling with the wave structure in humidity, but is consistent with the changes in the basic state

The influence of air‐sea coupling on forecasts of the 2016 Indian summer monsoon and its intraseasonal variability

Daily initialized coupled and uncoupled numerical weather prediction (NWP) forecasts from the global Met Office Unified Model (MetUM) are compared for the 2016 Indian summer monsoon. Three MetUM configurations are used: atmosphere‐only (ATM), coupled to a mixed‐layer ocean model (KPP), and coupled to a dynamical ocean model (NEMO). The analysis focuses on the impact of air‐sea coupling, particularly in the Bay of Bengal (BoB), on NWP for monsoon rainfall. Seasonal‐mean biases in all three configurations are highly consistent and driven by errors in atmospheric processes. Rainfall is initially overestimated over India, but underestimated over the BoB, the latter associated with too much shortwave radiation and too little cloud cover in MetUM. The excess shortwave radiation (>40 Wm‐2 over the northwest BoB) is partially compensated by additional latent cooling, primarily due to overestimated surface wind speeds. In NEMO and KPP, coupling improves the timing of intraseasonal active and break phases over India, primarily the end of these phases, which are systematically too late in ATM. NEMO and KPP show a more realistic intraseasonal local phase relationship between sea surface temperature (SST) and rainfall throughout the BoB, but no configuration reproduces the observed significant lagged relationship between BoB SST and Indian rainfall. The lack of this relationship may be partly attributed to weak heat flux feedbacks to northern BoB SST, with the forecast shortwave feedback having systematically the wrong sign (positive) compared to satellite radiation, and thus contributing to SST warming at all lead times. Based on these MetUM forecasts, there is a limited impact of coupling on NWP for monsoon rainfall, both for the mean rainfall and intraseasonal variability. Further research to improve NWP for monsoon rainfall should focus on reducing MetUM atmospheric systematical biases

Intraseasonal variability of air-sea fluxes over the Bay of Bengal during the southwest monsoon

In the Bay of Bengal (BoB), surface heat fluxes play a key role in monsoon dynamics and prediction. The accurate representation of large-scale surface fluxes is dependent on the quality of gridded reanalysis products. Meteorological and surface flux variables from five reanalysis products are compared and evaluated against in situ data from the RAMA moored array in the BoB. The reanalysis products: ERA-Interim (ERA-I), TropFlux, MERRA-2, JRA-55 and CFSR are assessed for their characterisation of air-sea fluxes during the southwest monsoon season (JJAS). ERA-I captured radiative fluxes best while TropFlux captured turbulent and net heat fluxes (Qnet) best, and both products outperformed JRA-55, MERRA-2 and CFSR, showing highest correlations and smallest biases when compared to the in situ data. In all five products, the largest errors were in shortwave radiation (QSW) and latent heat flux (QLH), with non-negligible biases up to ∼75 W m−2. The QSW and QLH are the largest drivers of the observed Qnet variability, thus highlighting the importance of the results from the buoy comparison. There are also spatially coherent differences in the mean basin-wide fields of surface flux variables from the reanalysis products, indicating that the biases at the buoy position are not localized. Biases of this magnitude have severe implications on reanalysis products ability to capture the variability of monsoon processes. Hence, the representation of intraseasonal variability was investigated through the boreal summer intraseasonal oscillation and we found that TropFlux and ERA-I perform best at capturing intraseasonal climate variability during the southwest monsoon season

A local-to-large scale view of Maritime Continent rainfall: control by ENSO, MJO, and equatorial waves

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

Evaluation of multi-season convection permitting atmosphere - mixed layer ocean simulations of the Maritime Continent

A multi-season convection permitting regional climate simulation of the Maritime Continent using the Met Office Unified Model with 2.2-km grid spacing is presented and evaluated. The simulations pioneer the use of atmosphere-ocean coupling with the multi-column K profile parametrisation (KPP) mixed layer ocean model in atmospheric convection permitting climate simulations. Comparisons are made against a convection parametrised simulation in which it is nested, and which in turn derives boundary conditions from ERA5 reanalysis. This paper describes the configuration, performance of the mean state and variability of the two simulations compared against observational datasets. The models both have minor sea surface temperature (SST) and wet precipitation biases. The diurnal cycle, representation of equatorial waves and relationship between SST and precipitation are all improved in the convection permitting model compared to the convection parametrised model. The MJO is present in both models with a faster than observed propagation speed. However, it is unclear whether fidelity of the MJO simulation is inherent to the model or whether it predominantly arises from the forcing at the boundaries

Impact of the Madden–Julian oscillation and equatorial waves on tracked mesoscale convective systems over southeast Asia

Southeast Asia is a region dominated by high-impact weather, but numerical weather prediction here is a challenge owing to the complex orography and interactions between small- and large-scale phenomena. Localised mesoscale convective systems (MCSs) can produce intense precipitation. Here, we track MCSs over a 5-year period in Himawari satellite data, characterise the distribution of MCSs in the region, and investigate how they are modulated by the Madden–Julian oscillation (MJO) and equatorial waves. Between 10°S and 10°N in southeast Asia, MCSs account for 45–70% of the precipitation during boreal extended winter (November–April). Over most of the region, the fractional MCS contribution to rainfall is higher than average on days with extreme rainfall (>55%). Long-lived (>12 hr) MCSs contribute disproportionately, providing 85% of the rainfall despite comprising only 34% of all MCSs. Variability in MCS rainfall accounts for >50% of the total rainfall variability during an MJO cycle, mostly due to larger numbers of MCSs in convectively active MJO phases. Variations in MCS size and mean rain rate due to shifts in the stratiform proportion provide compensating effects. In the west of the region, a shift to faster moving MCSs in active MJO phases and slower moving MCSs in inactive phases resulted in fast-moving MCSs having the greatest impact on the MJO-associated variability. Variability is larger in the west than in the east. Equatorial Kelvin waves modulate MCS rainfall, with MCSs accounting for 20–50% of local rainfall anomalies. This variability is again enhanced in the west. By contrast, rainfall anomalies due to westward-propagating mixed Rossby–gravity waves and Rossby-1 waves are dominated by tropical-cyclone-related rainfall. Skill at local scales may be extracted from forecasts of subseasonal drivers such as the MJO and Kelvin waves, by understanding how these modulate the number and characteristics of MCSs

Propagation of the Madden–Julian Oscillation and scale interaction with the diurnal cycle in a high-resolution GCM

The Madden–Julian Oscillation (MJO) is the chief source of tropical intra-seasonal variability, but is simulated poorly by most state-of-the-art GCMs. Common errors include a lack of eastward propagation at the correct frequency and zonal extent, and too small a ratio of eastward- to westward-propagating variability. Here it is shown that HiGEM, a high-resolution GCM, simulates a very realistic MJO with approximately the correct spatial and temporal scale. Many MJO studies in GCMs are limited to diagnostics which average over a latitude band around the equator, allowing an analysis of the MJO’s structure in time and longitude only. In this study a wider range of diagnostics is applied. It is argued that such an approach is necessary for a comprehensive analysis of a model’s MJO. The standard analysis of Wheeler and Hendon (Mon Wea Rev 132(8):1917–1932, 2004; WH04) is applied to produce composites, which show a realistic spatial structure in the MJO envelopes but for the timing of the peak precipitation in the inter-tropical convergence zone, which bifurcates the MJO signal. Further diagnostics are developed to analyse the MJO’s episodic nature and the “MJO inertia” (the tendency to remain in the same WH04 phase from one day to the next). HiGEM favours phases 2, 3, 6 and 7; has too much MJO inertia; and dies out too frequently in phase 3. Recent research has shown that a key feature of the MJO is its interaction with the diurnal cycle over the Maritime Continent. This interaction is present in HiGEM but is unrealistically weak

BoBBLE: ocean-atmosphere interaction and its impact on the South Asian monsoon

The Bay of Bengal (BoB) plays a fundamental role in controlling the weather systems that make up the South Asian summer monsoon system. In particular,the southern BoB has cooler sea surface temperature (SST) that influence ocean-atmosphere interaction and impact on the monsoon. Compared to the southeast, the southwestern BoB is cooler, more saline, receives much less rain, and is influenced by the Summer Monsoon Current(SMC). To examine the impact of these features on the monsoon, the BoB Boundary Layer Experiment (BoBBLE) was jointly undertaken by India and the UK during June–July 2016. Physical and bio-geochemical observations were made using a CTD, five ocean gliders, a uCTD, a VMP, two ADCPs, Argo floats, drifting buoys, meteorological sensors and upper air radiosonde balloons. The observations were made along a zonal section at 8◦N between 85.3◦E and 89◦E with a 10-day time series at 89◦E, 8◦N. This paper presents the new observed features of the southern BoB from the BoBBLE field program, supported by satellite data. Key results from the BoBBLE field campaign show the Sri Lanka Dome and the SMC in different stages of their seasonal evolution and two freshening events during which salinity decreased in the upper layer leading to the formation of thick barrier layers. BoBBLE observations were taken during a suppressed phase of the intraseasonal oscillation; they captured in detail the warming of the ocean mixed layer and preconditioning of the atmosphere to convection

Current and emerging developments in subseasonal to decadal prediction

Weather and climate variations of subseasonal to decadal timescales can have enormous social, economic and environmental impacts, making skillful predictions on these timescales a valuable tool for decision makers. As such, there is a growing interest in the scientific, operational and applications communities in developing forecasts to improve our foreknowledge of extreme events. On subseasonal to seasonal (S2S) timescales, these include high-impact meteorological events such as tropical cyclones, extratropical storms, floods, droughts, and heat and cold waves. On seasonal to decadal (S2D) timescales, while the focus remains broadly similar (e.g., on precipitation, surface and upper ocean temperatures and their effects on the probabilities of high-impact meteorological events), understanding the roles of internal and externally-forced variability such as anthropogenic warming in forecasts also becomes important. The S2S and S2D communities share common scientific and technical challenges. These include forecast initialization and ensemble generation; initialization shock and drift; understanding the onset of model systematic errors; bias correct, calibration and forecast quality assessment; model resolution; atmosphere-ocean coupling; sources and expectations for predictability; and linking research, operational forecasting, and end user needs. In September 2018 a coordinated pair of international conferences, framed by the above challenges, was organized jointly by the World Climate Research Programme (WCRP) and the World Weather Research Prograame (WWRP). These conferences surveyed the state of S2S and S2D prediction, ongoing research, and future needs, providing an ideal basis for synthesizing current and emerging developments in these areas that promise to enhance future operational services. This article provides such a synthesis