Ability Of Regional Climate Models Research Articles

Large-scale heavy precipitation over the Czech Republic and its link to atmospheric circulation in CORDEX regional climate models

AbstractThe study evaluates ability of regional climate models (RCMs) to reproduce relationships between large-scale heavy precipitation events (LHPEs) over the Czech Republic and atmospheric circulation. We use an ensemble of 32 RCM simulations with the 0.11° resolution from the Euro-CORDEX project, and compare the historical simulations (1951–2005) against observations from the E-OBS data set. A novel selection criterion for LHPEs is proposed, defining these as days with at least 70% of all grid boxes over a given area with precipitation amounts exceeding the 90th grid-specific percentile of the seasonal distribution of daily amounts. The association with atmospheric circulation is investigated through circulation types derived from sea level pressure using airflow indices (direction, strength and vorticity). The majority of the RCMs capture that the frequency of days with LHPEs is higher in winter than summer, but almost all underestimate the occurrence of LHPEs in both seasons. In winter, the observed LHPEs are connected mainly with cyclonic types and westerly supertype; the role of nonwesterly and cyclonic-nonwesterly supertypes is significant only in the eastern part, where the Atlantic influence is weaker. In summer, the importance of cyclonic and nonwesterly types in producing LHPEs increases compared to winter. The RCMs reasonably well reproduce these links, including differences between seasons and regions, if their ensemble mean is evaluated, but large variations occur among individual simulations mainly in summer. The importance of cyclonic vorticity is overestimated in the RCMs, while westerly advection of moist air plays a smaller role in models than in observations.

More intense daily precipitation in CORDEX‐SEA regional climate models than their forcing global climate models over Southeast Asia

AbstractThe ability of regional climate models (RCMs) to accurately simulate the current climate is increasingly important for impact assessments over Southeast Asia (SEA), identified as one of the world's most vulnerable regions to climate change. In this study, we evaluate the performance of a set of regional high‐resolution simulations from the Coordinated Regional Climate Downscaling Experiment‐SEA (CORDEX‐SEA) in simulating rainfall over the region. Simulations of the 1982–2005 seasonal mean climatology of daily precipitation and precipitation distribution over land are compared to observations from different sources (i.e., in situ‐based and satellite‐based). We also evaluate to what extent the precipitation distribution in RCMs is closer to observations than their associated forcing global climate models (GCMs). Observational estimates of precipitation over SEA have large uncertainties, making the model evaluations complicated. Despite these difficulties, our results highlight that RCMs can reproduce some complexities in the spatial distribution of seasonal rainfall but generally have a larger wet bias than GCMs. This is particularly true for the extremes in which RCMs show a large overestimation of rainfall intensity. There are some precipitation quantiles and grid points in which RCMs show limited reductions in biases compared to observations, but there is no consistency across all simulations and RCMs are generally further away from observations than their forcing GCMs. We find that greater intensity in RCMs over CORDEX‐SEA compared to their associated forcing GCMs is firstly associated with the increased supply of moisture from both local and large‐scale sources. Second, a widespread increase in convective precipitation is found across the region in RCMs. Our findings suggest that a model's ability to simulate precipitation over the region relies more on the RCM setup itself (e.g., parameterization scheme), rather than its forcing GCM. This should be considered when assessing the reliability of RCM precipitation simulations for future projections.

Assessing the performance of 33 CMIP6 models in simulating the large-scale environmental fields of tropical cyclones

General circulation model (GCM) biases are one of the important sources of biases and uncertainty in dynamic downscaling–based simulations. The ability of regional climate models to simulate tropical cyclones (TCs) is strongly affected by the ability of GCMs to simulate the large-scale environmental field. Thus, in this work, we employ a recently developed multivariable integrated evaluation method to assess the performance of 33 CMIP6 (phase 6 of the Coupled Model Intercomparison Project) models in simulating multiple fields in terms of their climatology. The CMIP6 models are quantitatively evaluated against two reanalysis datasets over five ocean areas. The results show that most of the CMIP6 models overestimate the mid-level humidity in almost all tropical oceans. The multi-model ensemble mean overestimates the vertical shear of the horizontal winds in the Northeast Pacific and North Atlantic. An increase in model horizontal resolution appears to be helpful in improving the model simulations. For example, there are 6–8 models with higher resolution among the top 10 models in terms of overall model performance in simulating the climatology and interannual variability of multiple variables. Similarly, there are 7–8 models with lower resolution among the bottom 10 models. The model skill varies depending on the region and variable being evaluated. Although no model performs best in all regions and for all variables, some models do show relatively good capability in simulating the large-scale environmental field of TCs.

The impact of regional climate model formulation and resolution on simulated precipitation in Africa

Abstract. We investigate the impact of model formulation and horizontal resolution on the ability of Regional Climate Models (RCMs) to simulate precipitation in Africa. Two RCMs (SMHI-RCA4 and HCLIM38-ALADIN) are utilized for downscaling the ERA-Interim reanalysis over Africa at four different resolutions: 25, 50, 100, and 200 km. In addition to the two RCMs, two different parameter settings (configurations) of the same RCA4 are used. By contrasting different downscaling experiments, it is found that model formulation has the primary control over many aspects of the precipitation climatology in Africa. Patterns of spatial biases in seasonal mean precipitation are mostly defined by model formulation, while the magnitude of the biases is controlled by resolution. In a similar way, the phase of the diurnal cycle in precipitation is completely controlled by model formulation (convection scheme), while its amplitude is a function of resolution. However, the impact of higher resolution on the time-mean climate is mixed. An improvement in one region/season (e.g. reduction in dry biases) often corresponds to a deterioration in another region/season (e.g. amplification of wet biases). At the same time, higher resolution leads to a more realistic distribution of daily precipitation. Consequently, even if the time-mean climate is not always greatly sensitive to resolution, the realism of the simulated precipitation increases as resolution increases. Our results show that improvements in the ability of RCMs to simulate precipitation in Africa compared to their driving reanalysis in many cases are simply related to model formulation and not necessarily to higher resolution. Such model formulation related improvements are strongly model dependent and can, in general, not be considered as an added value of downscaling.

Evaluation of the ability of regional climate models and a statistical model to represent the spatial characteristics of extreme precipitation

AbstractExtreme precipitation is one of the most severe weather hazards which have a significant influence on society, agriculture and ecosystems. The spatial extension and intensity of extreme precipitation events are two important features which need to be quantified for improved flood risk and water resource management. Here, we evaluate how well regional climate models (RCMs) reproduce precipitation extremes with respect to spatial dependency and intensity. We show by using seasonal extreme intensities in Brandenburg‐Berlin, Germany, that some RCMs underestimate the spatial dependence of extremes in summer and overestimate it in winter, compared with an observational‐based data set. Most RCMs significantly underestimate the magnitudes of extremes in summer and overestimate the magnitudes in autumn and winter. A statistical model, based on a max‐stable process, accounting for both the spatial and temporal variability is developed. We show that this model performs better in representing the spatial dependency and intensity characteristics of extreme precipitation compared to the RCMs.

Evaluation of CORDEX-RCMS and their driving GCMs of CMIP5 in simulation of Indian summer monsoon rainfall and its future projections

Climate models are widely used for global and regional assessment of climate change. The present study aims to assess the ability of regional climate models (RCMs) of the Coordinated Regional Climate Downscaling Experiment (CORDEX) and their driving global climate models (GCMs) of Coupled Models Intercomparison Project phase 5 (CMIP5) in simulating the Indian summer monsoon rainfall (ISMR) over India (1979–2005). The assessment of CORDEX-RCMs driven by the boundary conditions from GCMs is necessary to know the capability of RCMs in the simulation of ISMR over India. The spatiotemporal analysis of ISMR is performed for past periods to understand its characteristics while attempt is made for future projected changes over the homogeneous rainfall region of India. The study reveals that RCM REMO2009 (MPI) and its driving GCM MPI-ESM-LR are close to the observed rainfall of Global Precipitation Climatology Centre (GPCC). Further, it is noticed that the biases in REMO2009 (MPI) and GCMs viz. MPI-ESM-LR and GFDL-CM3 are of comparable amplitude making them suitable for future projection of ISMR for 2016–2045 under Representative Concentration Pathways (RCPs) 4.5 and 8.5 at 99% and 95% confidence levels. In the simulation of REMO2009 (MPI) and its driving GCM (MPI-ESM-LR) under RCPs 4.5 and 8.5, an excess of rainfall is possible over the parts of Peninsular India (PI) and West Central India (WCI) while a deficit over the North West India (NWI). The simulation of the GCM, GFDL-CM3 depicts an excess of rainfall over NWI, PI, and deficit over WCI under both emission scenarios.

Investigating Indian summer monsoon in coupled regional land–atmosphere downscaling experiments using RegCM4

The ability of Regional Climate Model (RCM: RegCM4) forced with two different Global Climate Model (GCM: CCSM4 and MIROC5) and three land-surface parameterization (LSP) (i.e., BATS, CLM4.5 and Subgrid-BATS) in simulating the Indian summer monsoon (ISM) is tested for the present climate (1975–2005). Thus six simulation combinations are assessed for seasonal mean temperature, precipitation, and low-level wind for ISM season (June, July, August, September: JJAS) over the COordinated Regional climate Downscaling EXperiment-South Asia (CORDEX-SA) domain. The simulations are evaluated in terms of Taylor’s metric (for precipitation, temperature, zonal wind, meridional wind and total cloud fraction), mean annual cycle, index of agreement, normalized root mean squared deviation and probability distribution function. The experiments simulated moderate events more accurately than high-intensity precipitation events compared to the corresponding observations. The inherent biases in the model simulations are attributed to the weaker meridional wind along with restrained vertical motion during ISM, especially with CCSM4 forcing. A careful analysis of tropospheric temperature gradient (TTG) suggests a weaker north–south (N–S) gradient due to the warmer atmospheric column in these experiments. On the contrary, the MIROC5_CLM4.5 experiment captures the magnitude and the temporal evolution of TTG better during ISM. It also represents the mean features of ISM better than other experiments. Also, the CLM4.5 LSP shows promising performance in ISM simulation when forced with MIROC5. It also provides further avenues for testing of the same combination under different frameworks, including the intended future climate study. This study emphasizes the importance of using appropriate GCM and LSP forcings to the RCM for simulating a coupled complex systems such as ISM.

Evaluating reanalysis-driven CORDEX regional climate models over Australia: model performance and errors

The ability of regional climate models (RCMs) to accurately simulate current and future climate is increasingly important for impact assessment. This is the first evaluation of all reanalysis-driven RCMs within the CORDEX Australasia framework [four configurations of the Weather Forecasting and Research (WRF) model, and single configurations of COSMO-CLM (CCLM) and the Conformal-Cubic Atmospheric Model (CCAM)] to simulate the historical climate of Australia (1981–2010) at 50 km resolution. Simulations of near-surface maximum and minimum temperature and precipitation were compared with gridded observations at annual, seasonal, and daily time scales. The spatial extent, sign, and statistical significance of biases varied markedly between the RCMs. However, all RCMs showed widespread, statistically significant cold biases in maximum temperature which were the largest during winter. This bias exceeded − 5 K for some WRF configurations, and was the lowest for CCLM at ± 2 K. Most WRF configurations and CCAM simulated minimum temperatures more accurately than maximum temperatures, with biases in the range of ± 1.5 K. RCMs overestimated precipitation, especially over Australia’s populous eastern seaboard. Strong negative correlations between mean monthly biases in precipitation and maximum temperature suggest that the maximum temperature cold bias is linked to precipitation overestimation. This analysis shows that the CORDEX Australasia ensemble is a valuable dataset for future impact studies, but improving the representation of land surface processes, and subsequently of surface temperatures, will improve RCM performance. The varying RCM capabilities identified here serve as a foundation for the development of future regional climate projections and impact assessments for Australia.

Blocking representation in the ERA-Interim driven EURO-CORDEX RCMs

While Regional Climate Models (RCMs) have been shown to yield improved simulations compared to General Circulation Model (GCM), their representation of large-scale phenomena like atmospheric blocking has been hardly addressed. Here, we evaluate the ability of RCMs to simulate blocking situations present in their reanalysis driving data and analyse the associated impacts on anomalies and biases of European 2-m air temperature (TAS) and precipitation rate (PR). Five RCM runs stem from the EURO-CORDEX ensemble while three RCMs are WRF models with different nudging realizations, all of them driven by ERA-Interim for the period 1981–2010. The detected blocking systems are allocated to three sectors of the Euro-Atlantic region, allowing for a characterization of distinctive blocking-related TAS and PR anomalies. Our results indicate some misrepresentation of atmospheric blocking over the EURO-CORDEX domain, as compared to the driving reanalysis. Most of the RCMs showed fewer blocks than the driving data, while the blocking misdetection was negligible for RCMs strongly conditioned to the driving data. A higher resolution of the RCMs did not improve the representation of atmospheric blocking. However, all RCMs are able to reproduce the basic anomaly structure of TAS and PR connected to blocking. Moreover, the associated anomalies do not change substantially after correcting for the misrepresentation of blocking in RCMs. The overall model bias is mainly determined by pattern biases in the representations of surface parameters during non-blocking situations. Biases in blocking detections tend to have a secondary influence in the overall bias due to compensatory effects of missed blockings and non-blockings. However, they can lead to measurable effects in the presence of a strong blocking underestimation.

Greenland Ice Sheet seasonal and spatial mass variability from model simulations and GRACE (2003–2012)

Abstract. Improving the ability of regional climate models (RCMs) and ice sheet models (ISMs) to simulate spatiotemporal variations in the mass of the Greenland Ice Sheet (GrIS) is crucial for prediction of future sea level rise. While several studies have examined recent trends in GrIS mass loss, studies focusing on mass variations at sub-annual and sub-basin-wide scales are still lacking. At these scales, processes responsible for mass change are less well understood and modeled, and could potentially play an important role in future GrIS mass change. Here, we examine spatiotemporal variations in mass over the GrIS derived from the Gravity Recovery and Climate Experiment (GRACE) satellites for the January 2003–December 2012 period using a "mascon" approach, with a nominal spatial resolution of 100 km, and a temporal resolution of 10 days. We compare GRACE-estimated mass variations against those simulated by the Modèle Atmosphérique Régionale (MAR) RCM and the Ice Sheet System Model (ISSM). In order to properly compare spatial and temporal variations in GrIS mass from GRACE with model outputs, we find it necessary to spatially and temporally filter model results to reproduce leakage of mass inherent in the GRACE solution. Both modeled and satellite-derived results point to a decline (of −178.9 ± 4.4 and −239.4 ± 7.7 Gt yr−1 respectively) in GrIS mass over the period examined, but the models appear to underestimate the rate of mass loss, especially in areas below 2000 m in elevation, where the majority of recent GrIS mass loss is occurring. On an ice-sheet-wide scale, the timing of the modeled seasonal cycle of cumulative mass (driven by summer mass loss) agrees with the GRACE-derived seasonal cycle, within limits of uncertainty from the GRACE solution. However, on sub-ice-sheet-wide scales, some areas exhibit significant differences in the timing of peaks in the annual cycle of mass change. At these scales, model biases, or processes not accounted for by models related to ice dynamics or hydrology, may lead to the observed differences. This highlights the need for further evaluation of modeled processes at regional and seasonal scales, and further study of ice sheet processes not accounted for, such as the role of subglacial hydrology in variations in glacial flow.

Bias reduction in decadal predictions of West African monsoon rainfall using regional climate models

AbstractThe West African monsoon rainfall is essential for regional food production, and decadal predictions are necessary for policy makers and farmers. However, predictions with global climate models reveal precipitation biases. This study addresses the hypotheses that global prediction biases can be reduced by dynamical downscaling with a multimodel ensemble of three regional climate models (RCMs), a RCM coupled to a global ocean model and a RCM applying more realistic soil initialization and boundary conditions, i.e., aerosols, sea surface temperatures (SSTs), vegetation, and land cover. Numerous RCM predictions have been performed with REMO, COSMO‐CLM (CCLM), and Weather Research and Forecasting (WRF) in various versions and for different decades. Global predictions reveal typical positive and negative biases over the Guinea Coast and the Sahel, respectively, related to a southward shifted Intertropical Convergence Zone (ITCZ) and a positive tropical Atlantic SST bias. These rainfall biases are reduced by some regional predictions in the Sahel but aggravated by all RCMs over the Guinea Coast, resulting from the inherited SST bias, increased westerlies and evaporation over the tropical Atlantic and shifted African easterly waves. The coupled regional predictions simulate high‐resolution atmosphere‐ocean interactions strongly improving the SST bias, the ITCZ shift and the Guinea Coast and Central Sahel precipitation biases. Some added values in rainfall bias are found for more realistic SST and land cover boundary conditions over the Guinea Coast and improved vegetation in the Central Sahel. Thus, the ability of RCMs and improved boundary conditions to reduce rainfall biases for climate impact research depends on the considered West African region.

Improving evaluation of climate change impacts on the water cycle by remote sensing ET-retrieval

Abstract. Population growth and intense consumptive water uses are generating pressures on water resources in the southeast of Spain. Improving the knowledge of the climate change impacts on water cycle processes at the basin scale is a step to building adaptive capacity. In this work, regional climate model (RCM) ensembles are considered as an input to the hydrological model, for improving the reliability of hydroclimatic projections. To build the RCMs ensembles, the work focuses on probability density function (PDF)-based evaluation of the ability of RCMs to simulate of rainfall and temperature at the basin scale. To improve the spatial calibration of the continuous hydrological model used, an algorithm for remote sensing actual evapotranspiration (AET) retrieval was applied. From the results, a clear decrease in runoff is expected for 2050 in the headwater basin studied. The plausible future scenario of water shortage will produce negative impacts on the regional economy, where the main activity is irrigated agriculture.

Urban precipitation extremes: How reliable are regional climate models?

We evaluate the ability of regional climate models (RCMs) that participated in the North American Regional Climate Change Assessment Program (NARCCAP) to reproduce the historical season of occurrence, mean, and variability of 3 and 24‐hour precipitation extremes for 100 urban areas across the United States. We show that RCMs with both reanalysis and global climate model (GCM) boundary conditions behave similarly and underestimate 3‐hour precipitation maxima across almost the entire U.S. RCMs with both boundary conditions broadly capture the season of occurrence of precipitation maxima except in the interior of the western U.S. and the southeastern U.S. On the other hand, the RCMs do much better in identifying the season of 24‐hour precipitation maxima. For mean annual precipitation maxima, regardless of the boundary condition, RCMs consistently show high (low) bias for locations in the western (eastern) U.S. Our results indicate that RCM‐simulated 3‐hour precipitation maxima at 100‐year return period could be considered acceptable for stormwater infrastructure design at less than 12% of the 100 urban areas (regardless of boundary conditions). RCM performance for 24‐hour precipitation maxima was slightly better, with performance acceptable for stormwater infrastructure design judged adequate at about 25% of the urban areas.

Assessment of Ocean Surface Winds and Tropical Cyclones around Japan by RCMs

This study assesses ocean surface winds in regional climate models (RCMs) and evaluates the ability of RCMs to downscale the features of tropical cyclones (TCs). RCMs show a smaller bias in the mean ocean surface wind around Japan during summer than the reanalysis data that is used as boundary data because of the better representation of land/ocean contrast in RCMs. However, for extreme values of ocean surface winds, all RCMs show a large bias over the ocean south of Japan. The RCMs reasonably simulate the TC tracks for about 40% of TCs, whereas these models fail to simulate realistic TC tracks for the remaining TCs. The TC track errors in the RCMs spread over a wide range with peaks ranging from 100 to 200 km. Although two RCMs underestimate the surface wind speed associated with TCs, one RCM simulates it reasonably. Therefore, it is suggested that the bias in the extreme values of ocean surface winds can be caused not only by an insu‰cient representation of surface winds associated with a model TC but also by the model TC track errors. Moreover, these errors may a¤ect the extreme values of precipitation produced by the interaction between TCs and topographies in Japan; therefore the extreme values should be used with caution. Multi-model ensemble approach contributes to reduce TC track errors. As a result, number of the TCs with the relatively small TC track errors increases up to about 60%.

Monitoring and Predicting of Maize Chilling Damage Based on Crop Growth Model in Northeast China

Although atmospheric temperature is obviously increased in northeast China with the global warming, regional maize chilling damage still occurs due to North extension of cultivation region of relative late-maturing varieties. Therefore, the research on monitoring and predicting maize chilling damage is still necessary. In this paper, we firstly constructed chilling damage indexes based on northeast China maize growth model (NEC_MaGM), and then developed the methods of monitoring and predicting maize chilling damage. The results are as follows: (1) In the eight individual chilling damage indicators selected from adverse weather conditions and the response of maize growth to low temperature, the second one (DC_Tas9, reduction of heat units during tasseling to September 31 compared with multi-year average) and the first one (DN_Tas, the difference of tasseling stage between this year and multi-year average) had the best historical matching capability for chilling damage. The accuracy of chilling damage simulation based on DN_Tas and DC_Tas9 was 93.0% and 81.4%, respectively. (2) Based on formation mechanism of chilling damage, historical matching capability of every individual indicator and its independence, we constructed an integrated chilling damage index, which included the first, the second, the forth (DW_GrS, the loss of WSO suffered by low temperature in growing season compared with multi-year average) and the seventh individual indicators (DW_Fro, the loss of WSO suffered by first frost). (3) Chilling damage was monitored for every site by using NEC_MaGM and indicators, and then regional maize chilling was described by calculating the proportion of the site with chilling damage. On the base of integrated index, we defined that regional chilling damage should have more than 45% of maize chilling site. Thus, the accuracy and the threat score of maize chilling damage simulation were 93.6% and 84.2%, respectively. The result of independent samples test was consistent with actual situation. (4) Monitoring of chilling damage in the grid scale could be described more detailed spatial distribution. With the constantly updated live weather data, chilling dynamic monitoring could be achieved. The causes of chilling damage in the different regions were explored by using every individual indicator. The severity and spatial dis-tribution of chilling damage simulations in the grid scale were fairly consistent with the literature. This method of monitoring chilling damage was favorable to business development of agricultural meteorological services. (5) According to weather data measured and predicted by regional climate models, combined with climate average data, regional maize chilling damage could be predicted by using NEC_MaGM. Case study showed that the method reflected to some extent the development and severity of chilling damage, but its accuracy was not only related with crop models, but also depended on simulation ability of regional climate model. The study could provide a scientific basis for the disaster prevention and mitigation.

Determination of Precipitation Return Values in Complex Terrain and Their Evaluation

Abstract To determine return values at various return periods for extreme daily precipitation events over complex orography, an appropriate threshold value and distribution function are required. The return values are calculated using the peak-over-threshold approach in which only a reduced sample of precipitation events exceeding a predefined threshold is analyzed. To fit the distribution function to the sample, the L-moment method is used. It is found that the deviation between the fitted return values and the plotting positions of the ranked precipitation events is smaller for the kappa distribution than for the generalized Pareto distribution. As a second focus, the ability of regional climate models to realistically simulate extreme daily precipitation events is assessed. For this purpose the return values are derived using precipitation events exceeding the 90th percentile of the precipitation time series and a fit of a kappa distribution. The results of climate simulations with two different regional climate models are analyzed for the 30-yr period 1971–2000: the so-called consortium runs performed with the climate version of the Lokal Modell (referred to as the CLM-CR) at 18-km resolution and the Regional Model (REMO)–Umweltbundesamt (UBA) simulations at 10-km resolution. It was found that generally the return values are overestimated by both models. Averaged across the region the overestimation is higher for REMO–UBA compared to CLM-CR.

Regional Model Simulations of the Bodélé Low-Level Jet of Northern Chad during the Bodélé Dust Experiment (BoDEx 2005)

Abstract The low-level jet (LLJ) over the Bodélé depression in northern Chad is a newly identified feature. Strong LLJ events are responsible for the emission of large quantities of mineral dust from the depression, the world’s largest single dust source, and its subsequent transport to West Africa, the tropical Atlantic, and beyond. Accurate simulation of this key dust-generating atmospheric feature is, therefore, an important requirement for dust models. The objectives of the present study are (i) to evaluate the ability of regional climate models (RCMs) and global analyses/reanalyses to represent this feature, and (ii) to determine the driving mechanisms of the LLJ and its strong diurnal cycle. Observational data obtained during the Bodélé Dust Experiment (BoDEx 2005) are utilized for comparison. When suitably configured, the fifth-generation Pennsylvania State University–NCAR Mesoscale Model (MM5) RCM can represent very accurately many of the key features of the jet including the structure, diurnal cycle, and day-to-day variability. Surface winds are also well reproduced, including the peak winds, which activate dust emission. Model fidelity is, however, strongly dependent on the boundary layer parameterization scheme, surface roughness, and vertical resolution in the lowest layers. A model horizontal resolution of a few tens of kilometers is sufficient to resolve most of the key features of the LLJ, while in global analyses/reanalyses many features of the LLJ are not adequately represented. Idealized RCM simulations indicate that under strong synoptic forcing the surrounding orography of the Tibesti and Ennedi Mountains acts to focus the LLJ onto the Bodélé and to accelerate the jet by ∼40%. From the RCM experiments it is diagnosed that the pronounced diurnal cycle of the Bodélé LLJ is largely a result of varying eddy viscosity, with elevated heating/cooling over the Tibesti Mountains to the north as a second-order contribution.