Lisa Bengtsson

Accurate simulation of the Madden‐Julian Oscillation (MJO) requires realistic diurnal evolution and vertical distribution of moist static energy (MSE). While prior studies documented MSE bias in simulations, vertically dependent physical processes responsible for these biases remain insufficiently understood. This study applies a hierarchical modeling and evaluation approach, combining fully coupled Unified Forecast System (UFS) forecasts with observation‐constrained single‐column model (SCM) experiments, to enable vertically and diurnally resolved assessment of MSE during two MJO events from the DYNAMO field campaign. Results reveal a diurnal loop of layer‐dependent biases: daytime errors precondition nighttime residual energy, which in turn influences MSE and convection on the following day. These biases are associated with deficiencies in the convective parameterization, in which convective latent heating is concentrated in the upper troposphere while the transition layer receives insufficient energy input, reflecting a missing shallow‐to‐deep convective transition. The convective parameterization also converts cloud condensate, especially ice, too efficiently, reinforcing an overly top‐heavy energy structure. This residual energy aloft persists overnight, suppressing the re‐establishment of low‐level instability and limiting convective development the following day. This hierarchical, vertically resolved analysis provides a process‐based pathway for diagnosing issues in tropical energetics on the diurnal timescale during the MJO preconditioning stage, with potential implications to improve MJO prediction in the UFS and related forecast systems.

Despite decades of research, the Madden‐Julian Oscillation (MJO) remains challenging for global prediction systems, partly because its representation is sensitive to convection‐environment interactions. We evaluate a new prognostic convective closure in NOAA's Unified Forecast System (UFS) that incorporates large‐scale moisture‐flux convergence (MFC) as a source alongside buoyancy. By allowing convection to respond directly to developing convergence anomalies, the scheme increases sensitivity to large‐scale moistening and produces more coherent MJO preconditioning. Using the UFS Seasonal Forecast System and composite diagnostics across nine MJO events, we show that the mixed closure generates a stronger and more realistic buildup of MFC over the central and eastern Pacific days before peak convection. This enhanced preconditioning, which is weaker in the traditional closure, is accompanied by improved MFC‐precipitation coherence and a more organized large‐scale MJO structure. These results illustrate how convective‐closure formulation can influence the large‐scale feedbacks critical for MJO representation.

Not-yet-known not-yet-known not-yet-known unknown Accurate simulation of the Madden-Julian Oscillation (MJO) requires realistic diurnal evolution and vertical distribution of moist static energy (MSE). While prior studies have documented systematic MSE deficit in global models, vertically dependent physical processes responsible for these errors remain insufficiently understood. This study applies a hierarchical modeling and evaluation framework, combining fully coupled Unified Forecast System (UFS) forecast with observation-constrained single-column model (SCM) experiments, to enable vertically and diurnally resolved assessment of MSE during two MJO events from the DYNAMO field campaign. Results reveal a coherent diurnal loop of layer-dependent, coupled physics biases, whereby daytime errors precondition nighttime residual heating and in turn influences MSE and convection the following day. These biases stem primarily from convective parameterization, which converts cloud condensates especially ice too efficiently, producing excessive precipitation, biased clouds, and radiative errors. Limited mid-level convection and a missing shallow-to-deep transition generate a top-heavy heating structure that persists overnight, suppressing vertical energy transport, MSE buildup, and the inhibit development of deep convection the next day. This hierarchical, vertically resolved analysis provides process-based pathways for improving tropical energetics and representation of the MJO initiation in UFS and other prediction systems.

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.

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.

The Madden Julian Oscillation (MJO) is a prominent mode of tropical variability and a key driver of intraseasonal predictability. Accurate prediction of the MJO is challenging partly due to 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 persists over a 15-day forecast period. Additional analysis attributes this to differences in their initial atmospheric static stability. 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 variations in convection and diabatic heating are not the main drivers for the predicted differences in MJO circulation amplitude.

This study outlines the updates made to cumulus convection parameterizations between Global Forecast System, version 16 (GFSv16), and the forthcoming GFSv17 which will be the first global forecast application to become operational under the Unified Forecast System infrastructure. The updates, addressing known systematic errors and biases, incorporate innovations such as stochasticity, three-dimensional subgrid organizational effects, and a prognostic closure evolution. The changes are shown to improve tropical temperature/humidity biases, convective available potential energy (CAPE) forecasts, CONUS precipitation, and tropical variability. By examining individual updates’ impact on temperature, humidity profiles, and precipitation power spectra, we find that the new prognostic closure and stricter precipitation evaporation criteria alleviate a dry and cold bias in the tropical boundary layer and enhance precipitation variability in the tropics. Stricter triggering criteria also allow for more CAPE buildup, particularly over the tropics. The cumulus convection updates have a modest impact on precipitation skill scores over CONUS, but overall, there is an improvement when comparing GFSv16 and the latest GFSv17 prototype configurations, in particularly for larger thresholds. The study also highlights the challenges in developing convection parameterizations suitable for both coupled and uncoupled model configurations. Evaluation of the MJO shows varying responses to cumulus convection changes depending on whether the model is coupled with a dynamic ocean model.

A prognostic closure is introduced to, and evaluated in, NOAA’s Unified Forecast System. The closure addresses aspects that are not commonly represented in traditional cumulus convection parameterizations, and it departs from the previous assumptions of a negligible subgrid area coverage and statistical quasi-equilibrium at steady state, the latter of which becomes invalid at higher resolution. The new parameterization introduces a prognostic evolution of the convective updraft area fraction based on a moisture budget, and, together with the buoyancy-driven updraft vertical velocity, it completes the cloud-base mass flux. In addition, the new closure addresses stochasticity and includes a representation of subgrid convective organization using cellular automata as well as scale-adaptive considerations. The new cumulus convection closure shows potential for improved Madden–Julian oscillation (MJO) prediction. In our simulations we observe better propagation, amplitude, and phase of the MJO in a case study relative to the control simulation. This improvement can be partly attributed to a closer coupling between low-level moisture flux convergence and precipitation as revealed by a space–time coherence spectrum. In addition, we find that enhanced organization feedback representation and stochastic effects, represented using cellular automata, further enhance the amplitude and propagation of the MJO, and they provide realistic uncertainty estimates of convectively coupled equatorial waves at seasonal time scales. The scale-adaptive behavior of the scheme is also studied by running the global model with 25-, 13-, 9-, and 3-km grid spacing. It is found that the convective area fraction and the convective updraft velocity are both scale adaptive, leading to a reduction of subgrid convective precipitation in the higher-resolution simulations.

In the atmosphere, convection can organize from smaller scale updrafts into more coherent structures on various scales. In bulk‐plume cumulus convection parameterizations, this type of organization has to be represented in terms of how the resolved flow would “feel” convection if more coherent structures were present on the subgrid. This type of subgrid organization acts as building blocks for larger scale tropical convective organization known to modulate local and remote weather. In this work a parameterization for subgrid (and cross‐grid) organization in a bulk‐plume convection scheme is proposed using the stochastic, self‐organizing, properties of cellular automata (CA). We investigate the effects of using a CA which can interact with three different components of the bulk‐plume scheme that modulate convective activity: entrainment, triggering, and closure. The impacts of the revised schemes are studied in terms of the model's ability to organize convectively coupled equatorial waves (CCEWs). The differing impacts of adopting the stochastic CA scheme, as compared to the widely used Stochastically Perturbed Physics Tendency (SPPT) scheme, are also assessed. Results show that with the CA scheme, precipitation is more spatially and temporally organized, and there is a systematic shift in equatorial wave phase speed not seen with SPPT. Previous studies have noted a linear relationship between Gross Moist Stability (GMS) and Kelvin wave phase speed. Analysis of GMS in this study shows an increase in Kelvin wave phase speed and an increase in GMS with the CA scheme, which is tied to a shift from large‐scale precipitation to convective precipitation.