Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multimodel evaluations can lead to model improvements; however, there have been no major intercomparisons of urban‐focussed land surface models in over a decade. Here, in Phase 1 of the Urban‐PLUMBER project, we evaluate the ability of 30 land surface models to simulate surface energy fluxes critical to atmospheric meteorological and air quality simulations. We establish minimum and upper performance expectations for participating models using simple information‐limited models as benchmarks. Compared with the last major model intercomparison at the same site, we find broad improvement in the current cohort's predictions of short‐wave radiation, sensible and latent heat fluxes, but little or no improvement in long‐wave radiation and momentum fluxes. Models with a simple urban representation (e.g., ‘slab’ schemes) generally perform well, particularly when combined with sophisticated hydrological/vegetation models. Some mid‐complexity models (e.g., ‘canyon’ schemes) also perform well, indicating efforts to integrate vegetation and hydrology processes have paid dividends. The most complex models that resolve three‐dimensional interactions between buildings in general did not perform as well as other categories. However, these models also tended to have the simplest representations of hydrology and vegetation. Models without any urban representation (i.e., vegetation‐only land surface models) performed poorly for latent heat fluxes, and reasonably for other energy fluxes at this suburban site. Our analysis identified widespread human errors in initial submissions that substantially affected model performances. Although significant efforts are applied to correct these errors, we conclude that human factors are likely to influence results in this (or any) model intercomparison, particularly where participating scientists have varying experience and first languages. These initial results are for one suburban site, and future phases of Urban‐PLUMBER will evaluate models across 20 sites in different urban and regional climate zones.

Urbanization leads to distinct meteorological features of urban environments, and one the best-known is the urban heat island (UHI) effect. For megacities, these features become mesoscale phenomena (scale ≥ 10 km) that are amplified by the tropospheric feedbacks, and have substantial implications on human well-being. For the first time, a three-dimensional statistical description of the megacity-induced meteorological effects extending towards the lower troposphere for summer is acquired on a quasi-climatological timescale (a decade) based on high-resolution (1 km) simulations for Moscow with the COSMO-CLM model with and without its urban canopy model TERRA_URB. Our results confirm the features from previous observational and modeling studies, including the UHI itself, the cooling effect above established by the cross-over effect, the urban dry/moist islands and the urban breeze circulation. Particularly, the UHI shows a strong diurnal variation in terms of intensity and vertical extent between daytime (≈0.5 K/≈1.5 km) and nighttime (>3 K/≈150 m). We have discovered a systematic veering in the downwind shift of the UHI spatial pattern established by the Coriolis effect, and an enhanced stable stratification of the rural surroundings established by the urban plumes further downwind. Finally, extending the analysis to multiple summers demonstrates a substantial increase in summer precipitation (up to +25%) over the city center and its leeward side. These urban-caused mesoclimatic effects need to be taken into account in weather and climate services, including the design of future megacities.

The Arctic has rapidly urbanized in recent decades with 2 million people currently living in more than a hundred cities north of 65^∘ N. These cities have a harsh but sensitive climate and warming here is the principle driver of destructive thawing, water leakages, air pollution and other detrimental environmental impacts. This study reports on the urban temperature anomaly in a typical Arctic city. This persistent warm anomaly reaches up to 11 K in winter with the wintertime mean urban temperature being 1.9 K higher on average in the city center than in the surrounding natural landscape. An urban temperature anomaly, also known as an urban heat island (UHI), was found using remote sensing and in situ temperature data. High-resolution (1 km) model experiments run with and without an urban surface parameterization helped to identify the leading physical and geographical factors supporting a strong temperature anomaly in a cold climate. The statistical analysis and modeling suggest that at least 50 % of this warm anomaly is caused by the UHI effect, driven mostly by direct anthropogenic heating, while the rest is created by natural microclimatic variability over the undulating relief of the area. The current UHI effect can be as large as the projected, and already amplified, warming for the region in the 21st century. In contrast to earlier reports, this study found that the wintertime UHI in the Arctic should be largely attributed to direct anthropogenic heating. This is a strong argument in support of energy efficiency measures, urban climate change mitigation policy and against high-density urban development in polar settlements. The complex pattern of thermal conditions, as revealed in this study, challenges urban planners to account for the observed microclimatic diversity in perspective sustainable development solutions.

Urban canopy parameters (UCPs) are essential in order to accurately model the complex interplay between urban areas and their environment. This study compares three different approaches to define the UCPs for Moscow (Russia), using the COSMO numerical weather prediction and climate model coupled to TERRA_URB urban parameterization. In addition to the default urban description based on the global datasets and hard-coded constants (1), we present a protocol to define the required UCPs based on Local Climate Zones (LCZs) (2) and further compare it with a reference UCP dataset, assembled from OpenStreetMap data, recent global land cover data and other satellite imagery (3). The test simulations are conducted for contrasting summer and winter conditions and are evaluated against a dense network of in-situ observations. For the summer period, advanced approaches (2) and (3) show almost similar performance and provide noticeable improvements with respect to default urban description (1). Additional improvements are obtained when using spatially varying urban thermal parameters instead of the hard-coded constants. The LCZ-based approach worsens model performance for winter however, due to the underestimation of the anthropogenic heat flux (AHF). These results confirm the potential of LCZs in providing internationally consistent urban data for weather and climate modelling applications, as well as supplementing more comprehensive approaches. Yet our results also underline the continued need to improve the description of built-up and impervious areas and the AHF in urban parameterizations.