The research team, led by Mamadou Doumbia of Université Félix Houphouët-Boigny in Abidjan, together with colleagues from Ivorian and French institutions, focused on a deceptively simple question: if you keep everything else in the model the same, how much does the rainfall simulation change when you swap out the schemes that represent convection, cloud microphysics, and the planetary boundary layer? The answer, quantified across thirteen successful simulations, is that the convection scheme alone can swing annual rainfall totals by several hundred percent. In a region where the seasonal monsoon determines planting calendars and reservoir levels, that is not a technical footnote but a headline finding.

To understand why convection matters so much, it helps to know what the scheme actually does. Thunderstorms over West Africa are typically far smaller than the grid cells of a regional climate model, which in this study resolve the watershed at a scale where individual storm systems cannot be explicitly simulated. Instead, the model relies on a parameterization: a set of equations that estimates, on average, how much moisture rises, condenses, and rains out from each grid cell. Four such schemes were tested here: the Emanuel scheme, which bases its calculations on quasi-equilibrium thermodynamics; the Kain–Fritsch scheme, which tracks entrainment and detrainment in rising plumes; the Grell scheme, which treats convection as a stabilizing process between cloud and environment; and the Tiedtke scheme, a mass-flux approach that accounts for both deep and shallow convection.

The team built sixteen possible model configurations by combining these four convection schemes with two cloud-microphysics options, the Subgrid Explicit moisture scheme known as SUBEX and the Nogherotto–Tompkins scheme known as NoTo, and two planetary boundary layer schemes, Holtslag and the University of Washington scheme, or UW. All simulations were driven by the ERA5 reanalysis, a state-of-the-art global dataset that constrains the large-scale atmospheric circulation, and covered the period 2001 to 2009. Thirteen of the sixteen configurations completed successfully and were analyzed, giving the researchers a robust matrix of comparisons over the Bandama watershed.

Evaluating the simulations required trustworthy observations, and in a data-sparse region that is itself a challenge. The researchers turned to two independent satellite-based rainfall products: CHIRPS, the Climate Hazards Group InfraRed Precipitation with Station data, which blends satellite estimates with ground observations, and IMERG, the Integrated Multi-satellitE Retrievals for the Global Precipitation Measurement mission. Against these references, the team applied a battery of statistical measures, including bias, temporal correlation, and distribution- and event-based skill scores. This dual-reference approach matters because satellite rainfall products each carry their own uncertainties; agreement between CHIRPS and IMERG lends confidence that the model rankings reflect genuine differences in physical realism rather than quirks of a single dataset.

The verdict on the convection schemes was unambiguous. The Tiedtke and Emanuel schemes, particularly when paired with the UW boundary layer scheme, performed best. These configurations reproduced the main phases of the seasonal rainfall cycle, from the onset of the monsoon rains in the south to the single-peaked regime of the northern savanna, with moderate annual biases generally below thirty percent. For a watershed-scale climate simulation, that level of agreement is respectable and suggests these configurations could serve as a sound foundation for future climate projections and hydrological studies in the region.

At the other end of the spectrum, the results were sobering. The Grell scheme, in every pairing tested, produced massive overestimates of rainfall, with annual biases exceeding plus 440 percent. The Kain–Fritsch scheme fared poorly as well whenever it was not combined with the UW boundary layer. In practical terms, a modeler who selected Grell without prior calibration would simulate a Bandama watershed drenched by more than five times its actual rainfall, rendering any downstream analysis of floods, soil moisture, or water resources meaningless. The authors’ implicit warning is clear: these schemes should not be used over this region without careful validation, a caution that applies broadly to climate modeling in West Africa, where the interaction between the monsoon circulation, the African easterly jet, and mesoscale convection stresses parameterizations designed elsewhere.

The boundary layer scheme emerged as the second most influential factor. The UW scheme consistently outperformed Holtslag, indicating that how the model represents the turbulent mixing between the surface and the free atmosphere feeds directly into the triggering of convection and hence into rainfall. This finding aligns with earlier work by some of the same authors, who assessed boundary layer parameterization sensitivity over the wider West African domain using RegCM5 and found the choice to be consequential for the mean climate. By contrast, the cloud-microphysics schemes, SUBEX and NoTo, had only a secondary and non-systematic effect on precipitation in this configuration, suggesting that at parameterized-convection resolution the fate of moisture inside resolved clouds matters less than how convection itself is triggered and modulated.

One limitation united all thirteen configurations: a systematic excess of light rainfall, or drizzle. Every simulation produced too many days with small precipitation amounts compared with the satellite references. This drizzle bias is a well-known generic weakness of parameterized convection at these resolutions, arising because the schemes tend to moisten and rain out grid cells too readily, producing frequent weak events instead of the intense, organized squall lines that deliver much of West Africa’s rainfall. The study’s authors note this as a shared limitation rather than a scheme-specific failure, and it points toward the broader trajectory of the field: convection-permitting modeling, in which storms are simulated explicitly at kilometer-scale grid spacing, is increasingly seen as the path forward, and RegCM5 itself has already demonstrated such capability in European-wide experiments.

For Côte d’Ivoire, the practical stakes are considerable. The Bandama watershed feeds agriculture and water infrastructure across the country, and climate change is expected to intensify both drought stress and extreme rainfall in West Africa. Reliable regional projections require a model configuration that can be trusted, and this study provides exactly that kind of evidence-based guidance: favor Tiedtke or Emanuel convection with the UW boundary layer, treat Grell and unpaired Kain–Fritsch with skepticism, and account for the drizzle bias when interpreting simulated rainfall statistics. The work also carries a message for the global modeling community, demonstrating that sensitivity testing at the watershed scale, grounded in independent satellite observations and run on Ivorian supercomputing infrastructure at the Centre National de Calcul de Côte d’Ivoire, can deliver actionable results from the Global South that speak directly to international efforts to improve climate models for the regions that need them most.

Subject of Research: Sensitivity of the RegCM5 regional climate model's physical parameterization schemes for precipitation simulation over the Bandama watershed in Côte d'Ivoire

Article Title: Assessment of the sensitivity of the RegCM5 model to physical parameterization schemes in precipitation simulation in the Bandama watershed (Côte d’Ivoire)

Article References: Doumbia, M., Yoroba, F., Koné, B., Tiémoko, T. D., Kouassi, B. K., Kouadio, K., Diédhiou, A., Salifou, I. T., & Diawara, A. (2026). Assessment of the sensitivity of the RegCM5 model to physical parameterization schemes in precipitation simulation in the Bandama watershed (Côte d’Ivoire). Theoretical and Applied Climatology, 157(10), Article 653. https://doi.org/10.1007/s00704-026-06591-3

Image Credits: AI Generated

DOI: 10.1007/s00704-026-06591-3

Keywords: RegCM5, regional climate modeling, precipitation simulation, convection schemes, Bandama watershed, Côte d'Ivoire, West African monsoon, parameterization, CHIRPS, IMERG, planetary boundary layer, climate model evaluation

The team focused on the Yellow River Basin (YRB), a vast and topographically diverse region stretching from the rugged Tibetan Plateau margins in the upper reaches, through the Loess Plateau, down to the North China Plain. Wind behavior in this basin matters for practical reasons: it influences wind farm siting, soil erosion on the Loess Plateau, and evaporation rates that feed back into the basin’s already stressed water resources. To test how well regional models can capture these winds, the researchers used the fifth-generation Regional Climate Model, RegCM5, developed at the International Centre for Theoretical Physics (ICTP), and ran three parallel 50-year simulations spanning 1971 to 2020, each driven at its lateral boundaries by the ERA5 global reanalysis from the European Centre for Medium-Range Weather Forecasts.

The three configurations differed only in their dynamical cores, the numerical engines that solve the equations of atmospheric motion. Model 1 employed the traditional hydrostatic core, the long-standing workhorse of climate modeling, which assumes that vertical pressure forces balance gravity at every point. This hydrostatic approximation is valid for large-scale atmospheric motions but breaks down at fine horizontal scales where vigorous vertical accelerations occur. Model 2 used the MM5-like non-hydrostatic core, which solves the full equations of motion including vertical acceleration, a capability inherited from the Penn State–NCAR Mesoscale Model tradition. Model 3, the newest addition to RegCM5, employed the Moloch non-hydrostatic core, a dynamical framework originally developed in Italy and known for its efficient numerical formulation.

All three simulations used a 25-kilometer grid spacing, a resolution typical of regional climate downscaling experiments. The researchers validated their outputs against the CN05.1 observational dataset, a high-resolution gridded product built from station observations across China. The results revealed a striking pattern of shared strengths and shared weaknesses. All three configurations successfully reproduced the broad spatial climatology of wind speed across the basin, capturing the general geography of windy and calm zones. However, every configuration systematically overestimated near-surface wind speed, with mean biases ranging from +1.3 to +2.8 meters per second. The overestimation was most severe in the rugged Upper Reaches, where the authors point to sub-grid topographic smoothing and insufficient effective surface drag as likely culprits.

This bias mechanism deserves a closer look. When a model represents terrain on a 25-kilometer grid, individual peaks and valleys are averaged into smoother, lower-relief surfaces. Real winds near the ground are slowed by the friction of vegetation, buildings, and fine-scale topographic obstacles that the smoothed model surface simply cannot see. The result is a model atmosphere that flows too freely, producing winds that are too strong. This problem is well documented in the boundary-layer meteorology literature, and the new study confirms it persists across all three dynamical cores, suggesting the bias originates in the representation of the surface rather than in the core dynamics themselves.

Where the cores did differ was in the details of their statistical performance. The Moloch-based Model 3 achieved the lowest mean bias and the closest match to the observed probability distribution of wind speeds, meaning its simulated winds were not only closer on average but also better captured the frequency of calm and windy conditions. In probability density function skill scores, a standard diagnostic introduced by Perkins and colleagues for evaluating model distributions, Model 3 outperformed its rivals. Interestingly, the older hydrostatic Model 1 and the MM5-like Model 2 retained slightly higher correlations in some temporal diagnostics and Taylor-diagram comparisons, indicating that no single core dominates every metric. The authors conclude that the Moloch core improves mean-state and distributional fidelity under this experimental setup but does not by itself resolve errors in the timing of wind variations or in long-term trends.

Perhaps the most sobering finding concerns the phenomenon known as terrestrial stilling. Observations worldwide, synthesized in influential reviews by McVicar and colleagues and by Vautard and colleagues, show that near-surface wind speeds declined over much of the land surface during recent decades, a trend partly attributed to increases in surface roughness from vegetation growth and land-use change, though later work by Zeng and colleagues documented a reversal in some regions. The CN05.1 observations over the Yellow River Basin show this stilling signal clearly. All three model configurations captured part of the interannual variability in wind speed, but they substantially underestimated the observed multi-decadal declining trend. The authors attribute this shortfall to the use of static, time-invariant land-use and land-cover forcing: a model whose vegetation and surface roughness never change cannot simulate winds that slow because the landscape itself has changed. They call for updated or time-varying land-cover forcing and for independent assessment of uncertainties in both the ERA5 driving fields and the observational datasets.

Beyond accuracy, the study delivers a message that will resonate with any modeling group facing finite computing budgets: the choice of dynamical core dramatically changes computational cost. The MM5-like non-hydrostatic core proved expensive because its explicit treatment of sound-wave dynamics imposes a strict stability constraint known as the Courant-Friedrichs-Lewy condition, forcing a time step of only 25 seconds. The Moloch core, by contrast, remained numerically stable with a time step of 200 seconds, eight times larger. The payoff was substantial: total runtime dropped by 58 percent relative to the MM5-like core and 17 percent relative to the hydrostatic core under identical computing environments. For decadal or multi-decadal downscaling campaigns, ensemble experiments, or convection-permitting applications where every second of wall-clock time counts, this efficiency advantage is far from trivial.

The researchers did not stop at comparing cores. They designed a one-decade sensitivity experiment to test how much the land surface itself contributes to the wind speed bias. By refining the sub-grid land-surface representation, they reduced the whole-basin positive wind bias by approximately 39 percent, a substantial improvement, although spatial correlation decreased in some regions, a reminder that fixing one aspect of model performance can trade off against another. Combining this result with the core comparison, the authors conclude that the Moloch core, especially when paired with improved land-surface representation, is the most practical configuration for long-term RegCM5 wind downscaling over complex terrain within their experimental design.

The implications extend well beyond the Yellow River Basin. As wind energy expands globally, with the Global Wind Energy Council reporting continued rapid growth in installed capacity, accurate wind climatologies from regional climate models are increasingly used to assess future resource reliability under climate change. China’s onshore wind potential, in particular, is a subject of intense study in the context of carbon-neutrality goals. This study shows that the tools used for such assessments carry systematic biases that depend on both the dynamical core and the land surface, and that these biases can be quantified and partially corrected. It also demonstrates that efficiency and accuracy need not be opposing goals: the newest core in RegCM5 delivered both the best distributional fidelity and the fastest runtime. For scientists downscaling winds over mountains, plateaus, and river valleys worldwide, the message is clear: the engine under the hood matters, and so does the landscape you tell the model it is blowing over.

Subject of Research: Comparison of hydrostatic and non-hydrostatic dynamical cores in the RegCM5 regional climate model for simulating 50 years of near-surface wind speed over the Yellow River Basin

Article Title: Evaluating hydrostatic vs. non-hydrostatic dynamics in RegCM5: a 50-year simulation of near-surface wind speed over the Yellow River Basin

Article References: Jiao, Y., Deng, J., Li, Q., Zhao, N., Yue, T., Popovas, D., Zhou, C., Quan, C., Fang, Z., Si, P., & Song, X. (2026). Evaluating hydrostatic vs. non-hydrostatic dynamics in RegCM5: a 50-year simulation of near-surface wind speed over the Yellow River Basin. Climate Dynamics, 64(10), Article 439. https://doi.org/10.1007/s00382-026-08396-6

Image Credits: AI Generated

DOI: 10.1007/s00382-026-08396-6

Keywords: RegCM5, dynamical cores, near-surface wind speed, Yellow River Basin, Moloch core, non-hydrostatic dynamics, hydrostatic approximation, terrestrial stilling, regional climate modeling, ERA5 reanalysis, computational efficiency, land-surface representation