Land–atmosphere interactions need to be optimally represented in climate models for the realistic representation of past and future climate. In this work, six different versions of land surface schemes (LSS) are used to simulate the climate over the Middle East–North Africa (MENA) region for the period 2000–2010 with a horizontal resolution of 0.44°, using the Weather Research and Forecasting (WRF) model. The monthly time series output is evaluated against observations for several surface climate variables using statistical metrics (climatology, 5th and 95th percentiles, standard deviation, linear trend) and Taylor diagrams. The resulting biases are presented for the whole MENA domain as well as 7 sub-domains. A ranking procedure objectively retrieves a performance spectrum among the schemes. The LSS that is closest to observations and is, therefore, considered as the best performing is Noah, followed by its augmented version (NoahMP). For these simulations at the relatively coarse horizontal resolution of 50 km, the more elaborate LSSs are not performing very well. These results are useful for the choice of LSS in climate change modelling of the MENA-CORDEX as a whole, as well as its sub-regions.

Warming and drying in the eastern Mediterranean and the Middle East (EMME) associated with climate change may have consequences for humans and ecosystems, including agriculture. Agriculture can be directly affected through meteorological conditions that influence crop growth and yield. Here we explored changes in the yield of durum (winter) wheat Triticum tirgidum in the EMME region caused by projected changes in thermal and hydrological conditions, employing the agro-ecological zones (AEZ) methodology. The climate input was obtained from simulations by the PRECIS regional climate model over the EMME domain at 25 km resolution, driven by a range of IPCC emissions scenarios for the 21st century. For the crop yield calculation, we used 2 steps of the biomass calculation from the AEZ methodology by calculating crop yield potentials with regard to temperature, solar radiation and evapotranspiration regimes. Our results showed that for the recent past, the highest wheat yields are found in the Fertile Crescent and the coastal areas of eastern Mediterranean Europe. The former region is projected to be negatively affected by the additional heat due to climate warming, while in the latter region yield gains may be expected. Our implementation of the AEZ methodology can be based on a range of climate data and can be applied to a number of crops, contributing to yield studies relevant for regional assessments of food security

The Eastern Mediterranean (EM) and the Middle East and North Africa (MENA) are projected to be exposed to extreme climatic conditions in the 21st century, which will likely induce adverse impacts in various sectors. Relevant climate change impact assessments utilise data from climate model projections and process-based impact models or simpler, index-based approaches. In this study, we explore the implied uncertainty from variations of climate change impact-related indices as induced by the modelled climate (WRF regional climate model) from different land surface schemes (Noah, NoahMP, CLM and RUC). The three climate change impact-related indicators examined here are the Radiative Index of Dryness (RID), the Fuel Dryness Index (Fd) and the Water-limited Yield (Yw). Our findings indicate that Noah simulates the highest values for both RID and Fd, while CLM gives the highest estimations for winter wheat Yw. The relative dispersion in the three indices derived by the different land schemes is not negligible, amounting, for the overall geographical domain of 25% for RID and Fd, and 10% for Yw. The dispersion is even larger for specific sub-regions.

The urban heat island (UHI) effect denotes that temperatures in urban areas are often higher compared to the rural background. This human‐induced phenomenon can exacerbate heat stress and adverse health impacts, adding to ongoing global warming. To examine the intensity and variability of the UHI effect, we used 40 years () of observational data (daily maximum and minimum temperature) from the Global Summary of the Day (GSOD), comprising about 1000 stations of varying temporal extent, spanning the Middle East and North Africa (MENA). The MENA is characterised by hot and dry summers and warms faster than other inhabited regions worldwide. The challenge in using data with diverse spatial and temporal extents is accounting for heterogeneities between each station comprising an urban–rural pair. One has to allow for differences in the distance between the pairs and, elevation, spatial–temporal changes in urbanisation as well as the distance from the coast. A new method is proposed, based on flexible statistical methods (Generalized Additive Models or GAMs), to quantify the temporal trend in the UHI effect while allowing for the aforementioned characteristics using regression splines of appropriately defined variables. A composition of high‐resolution satellite geospatial information, related to urbanisation properties and population data was utilised from the Global Human Settlement Layer database (GHSL‐SMOD), to characterise the stations in terms of their urbanisation type. These data were also used to quantify temporal changes in the extent of urbanisation relative to the surrounding areas. Results indicate consistent upward trends of the UHI effect in the MENA (up to °C), particularly at night (daily minimum temperature) during all seasons.

The effects of different land surface scheme (LSS) implementations on the simulated climate of the Middle East and North Africa (MENA) have been investigated with the Weather Research and Forecasting (WRF) regional model. Six simulations were carried out using four different LSSs (Noah, NoahMP, CLM, RUC) for the period 2000–2010, driven by ERA-Interim meteorological reanalyses at a horizontal resolution of 50 km. Deviations of key surface climate variables, radiation, and turbulent fluxes from the different LSS runs are presented relative to the default Noah scheme. The simulated annual mean climate variables in the MENA (indicating uncertainty) range from 0.7 to 2.4 ^∘C for air temperature, 2.0 to 3.4 ^∘C for land temperature, and 5 to 25 mm/month (54–65%) for precipitation. The Noah scheme deviates less than − 1 W/m² from the domain-wide surface energy balance and the NoahMP less than − 2 W/m², while for CLM and RUC the deviation is 3–4 W/m². Considering the differences among the surface energy balance from the various LSSs compared to the reference Noah, a surface climate response is calculated, and average LSS-induced climate sensitivity is derived for the air (and land) temperature of 0.1 ^∘C per W/m² and − 6 mm per W/m² for precipitation. The LSS-induced range in the modelled climate is of similar magnitude to the climate change projection estimates for the region, which highlights the importance of carefully selecting a land surface scheme in the regional climate simulations.