Juliana Dias

Several different time periods of the Atlantic Tradewind Ocean‐Atmosphere Mesoscale Interaction Campaign (ATOMIC) are isolated for examining how the depiction of tradewind marine shallow cumuli in single‐column models (SCMs) is affected by choices about model physics. The periods of interest are times when the NOAA Research Vessel Ronald H. Brown and research aircraft WP‐3D Orion were collocated, enabling verification of initial conditions and large‐scale forcing (advective) tendencies constructed using gridded data from the fifth generation ECMWF atmospheric reanalysis (ERA5). To demonstrate how this new ATOMIC test case can be used to guide model development, three parameterization suites of the NOAA Unified Forecast System are evaluated within the Common Community Physics Package Single Column Model (CCPP SCM). Calculations are also performed using a large‐eddy simulation (LES) to further bridge the gap between observations and SCM output, all of which are separated into regimes of either relatively active (“cloudy”) or inactive (“clear”) marine shallow cumuli. In both regimes tested, the parameterization suites tend to: (a) generate an unrealistic skewed or bimodal distribution of cloud fraction, (b) overestimate light to moderate rain rates, (c) produce an erroneously cold and dry boundary layer, and (d) produce higher‐than‐observed cloud tops. Results show that modifying the treatment of cloud fraction as well as increasing spatial and temporal resolution help bring the SCM more in line with observations. In addition, evidence is found to suggest that some of the remaining model biases may stem from intrinsic differences in the spatio‐temporal sampling properties of the observations versus SCM output.

A data‐driven dynamical filter is developed to characterize Madden‐Julian Oscillation (MJO) variability, by representing tropical variability with nonorthogonal empirical‐dynamical modes that allow for constructive and destructive interference. We find that two intraseasonal atmospheric modes, an “MJO‐fast” mode (45 day period) and a newly identified “MJO‐slow” mode (70 day period), alongside El Niño‐Southern Oscillation modes that are not entirely removed by temporal filtering, explain nearly all observed Real‐time Multivariate MJO (RMM) index‐based variability. The fastest growing, and most predictable, MJO events are initiated primarily by the MJO‐fast mode over the Indian Ocean, with subsequent progression across the Maritime Continent resulting from destructive and then constructive interference of the MJO‐fast and MJO‐slow modes. These events, which we demonstrate can be identified at forecast initialization time, are shown to be forecasts of opportunity in the ECMWF operational forecast model, with MJO skill extended by roughly a week compared to all other forecasts.

This study investigates the local impact of weak sea surface temperature (SST) warm anomalies on trade cumulus cloudiness in an idealized and ensemble framework with large‐eddy simulations. The control experiment uses a spatially uniform, time‐invariant SST and mean large‐scale conditions and atmospheric forcings derived from the Atlantic Tradewind Ocean‐Atmosphere Mesoscale Interaction Campaign. The perturbed experiment adds a Gaussian warm SST anomaly (SSTA) with a 12.5 km radius and 0.5 K magnitude. The ensemble‐averaged differences between perturbation and control experiments show that cloud fraction is enhanced over the downwind half of the prescribed warm SSTA, with the enhancement peaking slightly above the environmental lifting condensation level (LCL) and then decaying with height. Compared to the upper‐level (1.5 km) cloud response, the low‐level cloud response (1 km) to the warm SSTA is stronger and occurs more systematically across different ensemble members. This near‐LCL cloud response is driven by enhanced surface buoyancy flux and buoyancy‐driven turbulence over the warm SSTA as opposed to SSTA‐induced anomalous surface convergence and mesoscale ascent. Process denial experiments indicate that the locally enhanced surface sensible and latent heat fluxes contribute almost equally to increase the near‐LCL cloudiness, even though the locally enhanced surface sensible heat flux plays a dominant role in enhancing surface buoyancy flux. These results corroborate recent satellite composite results (Chen et al., 2023, https://doi.org/10.1175/jas‐d‐23‐0075.1 ), suggesting that the observed increase of daily cloud fraction above warm SSTAs is due to more frequent turbulence‐driven formation of shallow cumuli near the cloud base.

The Madden Julian Oscillation (MJO) is a key driver of intraseasonal predictability. Accurate prediction of the MJO is challenging partly due to its complex interactions with the background state and sensitivity to initial conditions and unresolved processes. Using NOAA's Unified Forecast System, we explore the impact of subtle differences in initial conditions on MJO forecasts over the Indo‐Pacific. Model runs initialized with two independent reanalyses show significant and systematic differences in MJO circulation amplitude that emerge rapidly and persist over the 15‐day forecast period. Additional analysis suggests the reason is due to differences in the initial atmospheric static stability. Specifically, the reanalysis that is initially less stable is associated with enhanced large‐scale vertical motion and divergent winds throughout the forecast. Notably, a convection‐based MJO index shows less sensitivity to initial conditions, suggesting that the differences in MJO circulation amplitude arise through mechanisms other than changes in convection or diabatic heating.

Variability of the Madden‐Julian Oscillation (MJO) inferred from tropical in‐situ observations during 1940–2023 is examined, and compared to that derived from reanalyzes. MJO assessments are challenging because characterizing MJO behavior outside of the satellite era suffers from a lack of detailed information about its observed state, remote signals and often relies on climate models' typically poor MJO representation. This study shows that, while tropical soundings are spatially and temporally sparse, they are still useful for better understanding multi‐decadal MJO amplitude variability. The similarities between variability of the MJO derived from (imperfect) reanalysis products and (sparse) tropical station observations suggest that decadal changes during the last 60 years are physical and not necessarily related to changes in the observational network. The larger differences in MJO amplitude between station and reanalysis prior to 1960 suggest that sparse tropical observations are a barrier to fully characterizing long‐term MJO evolution during the century.

Convectively coupled equatorial waves (CCEWs) are modes of tropical variability that are often seen as potentially beneficial for sub‐seasonal predictions. Quantifying this potential has been proven difficult in operational forecast systems because tropical variability tends to be poorly represented in such models. The European Center for Medium‐Range Weather Forecasts (ECMWF) is a potential exception because it has been shown to have much improved representation of the tropics. Here, ECMWF reforecasts are used to investigate the predicted evolution of CCEWs and how they might impact sub‐seasonal predictions. It is shown that the ECMWF system is able to generate variability consistent with various modes of CCEWs, but CCEWs tend to be weaker in amplitude and their propagation characteristics often deviate from observations. This study suggests that model advancements aimed at improving the deterministic representation of CCEWs are still needed in order to better understand and utilize tropical sources of sub‐seasonal predictive skill.

Mesoscale convective systems (MCSs) produce over 50% of tropical precipitation and account for the majority of extreme rainfall and flooding events. MCSs are considered the building blocks of larger‐scale convectively coupled equatorial waves (CCEWs). While CCEWs can provide favorable environments for convection, how CCEWs can systematically impact organized convection and thereby MCS characteristics is less clear. We examine this question by analyzing a global MCS tracking data set. During the active phase of CCEWs, MCS frequency increases and MCSs rain harder, produce more lifetime total rain, and grow larger in size. The probability of extreme MCSs also elevates. These changes are most pronounced when MCSs are associated with Kelvin waves and tropical depression‐type waves while less so with the Madden‐Julian Oscillation. These results can be benchmarks to improve model representation of MCS interactions with large‐scale circulations and can be leveraged for operational forecasts of high‐impact MCSs at extended lead times.

Tropical precipitation and circulation are often coupled and span a vast spectrum of scales from a few to several thousands of kilometers and from hours to weeks. Current operational numerical weather prediction (NWP) models struggle with representing the full range of scales of tropical phenomena. Synoptic to planetary scales are of particular importance because improved skill in the representation of tropical larger-scale features such as convectively coupled equatorial waves (CCEWs) has the potential to reduce forecast error propagation from the tropics to the midlatitudes. Here we introduce diagnostics from a recently developed tropical variability diagnostics toolbox, where we focus on two recent versions of NOAA’s Unified Forecast System (UFS): operational GFSv15 forecasts and experimental GFSv16 forecasts from April to October 2020. The diagnostics include space–time coherence spectra to identify preferred scales of coupling between circulation and precipitation, pattern correlations of Hovmöller diagrams to assess model skill in zonal propagation of precipitating features, CCEW skill assessment, plus a diagnostic aimed at evaluating moisture–convection coupling in the tropics. Results show that the GFSv16 forecasts are slightly more realistic than GFSv15 in their coherence between precipitation and model dynamics at synoptic to planetary scales, with modest improvements in moisture convection coupling. However, this slightly improved performance does not necessarily translate to improvements in traditional precipitation skill scores. The results highlight the utility of these diagnostics in the pursuit of better understanding of NWP model performance in the tropics, while also demonstrating the challenges in translating model advancements into improved skill.

The circulation associated with convection of the Madden‐Julian Oscillation (MJO) has been suggested to have an impact on its propagation by a number of previous studies. This circulation contains both flanking Rossby waves to the rear and a Kelvin wave leading the convective center. In this study, individual MJO convective envelopes from a 40‐year database are tracked, a technique to scale the MJO by its zonal wavelength is employed, and statistical methods are used to assess how the MJO circulation pattern might impact its eastward propagation downstream. Results suggest that continuous eastward propagation of the MJO is favored when a strong Kelvin wave circulation is present east of MJO convection, indicated by both an easterly zonal wind anomaly and negative geopotential height anomaly. In addition to the known significance of having Kelvin wave easterly wind anomalies, the results of this study highlight that the existence of negative geopotential height is important to supporting moistening and MJO propagation. It is found that importance of the Kelvin wave signal to MJO propagation depends on the region that the MJO is located over. Kelvin wave circulation east of MJO convection enhances moistening to support continuous eastward propagation of the MJO, mainly through meridional moisture advection due to the coupling between the Kelvin wave and Rossby‐like disturbances east of the active convection. The roles of boundary layer convergence and vertical moistening are also discussed.

Boreal‐wintertime hindcasts in the Unified Forecast System with the tropics nudged toward reanalysis improve United States (US) West Coast precipitation forecasts at Weeks 3–4 lead times when compared to those without nudging. To diagnose the origin of these improvements, a multivariate k‐means clustering method is used to group hindcasts into subsets by their initial conditions. One cluster characterized by an initially strong Aleutian Low demonstrates larger improvements at Weeks 3–4 with nudging compared to the others. The greater improvements with nudging for this cluster are related to model errors in simulating the interaction between the Aleutian Low and the teleconnection patterns associated with the Madden‐Julian oscillation (MJO) and El Niño‐Southern Oscillation (ENSO). Improving forecasts of tropical intraseasonal precipitation, especially during early MJO phases under non‐cold ENSO, may be important for producing better Weeks 3–4 precipitation forecasts for the US West Coast.