• Energy Research

AbstractHeat pumps (HPs) have emerged as a key technology for reducing energy use and greenhouse gas emissions. This study evaluates the potential switch to air-to-air HPs (AAHPs) in Toulouse, France, where conventional space heating is split between electric and gas sources. In this context, we find that AAHPs reduce heating energy consumption by 57% to 76%, with electric heating energy consumption decreasing by 6% to 47%, resulting in virtually no local heating-related CO2 emissions. We observe a slight reduction in near-surface air temperature of up to 0.5 °C during cold spells, attributable to a reduction in sensible heat flux, which is unlikely to compromise AAHPs operational efficiency. While Toulouse’s heating energy mix facilitates large energy savings, electric energy consumption may increase in cities where gas or other fossil fuel sources prevail. Furthermore, as AAHPs efficiency varies with internal and external conditions, their impact on the electrical grid is more complex than conventional heating systems. The results underscore the importance of matching heating system transitions with sustainable electricity generation to maximize environmental benefits. The study highlights the intricate balance between technological advancements in heating and their broader environmental and policy implications, offering key insights for urban energy policy and sustainability efforts.

Overview This is the data archive for paper "Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city". It contains the paper's data archive with model outputs (see notebooks folder) and the Singularity image for (optionally) re-running experiments. For the standalone models to model air conditioners and heat pumps please refer to MinimalDX. Prerequisites Linux with Bash shell. Git version >= 2. Singularity version >= 3. Portable Batch System*. Intel Fortran Compiler [Required for MesoNH simulations]. A compatible version of the MPI library implementation version 3 [Required for MesoNH simulations]. Please note that most steps require Singularity. If you are looking for information on how to install or use Singularity, please refer to the Singularity documentation. Please note that depending on your specific system settings and resource availability, you may need to modify PBS parameters at the top of submit scripts stored in the hpc directory. Simulations Offline Build Surfex with qsub hpc/surfex_build.pbs. Run scenarios with the following commands: qsub -v case_name=fincap hpc/surfex_run.pbs qsub -v case_name=fincap_extended_autosize hpc/surfex_run.pbs qsub -v case_name=minidx_cop=2.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.0 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=3.5 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=4.0 hpc/surfex_run.pbs qsub -v case_name=minidx_cop=4.5 hpc/surfex_run.pbs Online To run MesoNH simulations, follow the three steps outlined below in the same order. Note: Depending on your system and configuration, submit scripts may require change. Build MesoNH with qsub hpc/build_mnh_intel.pbs. Run the preprocessing step with qsub hpc/submit_mnh_prep.pbs. Finally run MesoNH cases with the following commands for fincap and minidx simulations respectively: hpc/submit_mnh.sh fincap 20050120 12 1 toulouse hpc/submit_mnh.sh minidx 20050120 12 1 toulouse Post-process the results with qsub hpc/submit_mnh_post.pbs Analyses qsub hpc/postprocess_results.pbs # Plots in notebooks/

AbstractAccurately 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.

UMEP (Urban Multi-scale Environmental Predictor), a city-based climate service tool, combines models and tools essential for climate simulations. Applications are presented to illustrate UMEP's potential in the identification of heat waves and cold waves; the impact of green infrastructure on runoff; the effects of buildings on human thermal stress; solar energy production; and the impact of human activities on heat emissions. UMEP has broad utility for applications related to outdoor thermal comfort, wind, urban energy consumption and climate change mitigation. It includes tools to enable users to input atmospheric and surface data from multiple sources, to characterise the urban environment, to prepare meteorological data for use in cities, to undertake simulations and consider scenarios, and to compare and visualise different combinations of climate indicators. An open-source tool, UMEP is designed to be easily updated as new data and tools are developed, and to be accessible to researchers, decision-makers and practitioners.

AbstractUrban land surface processes need to be represented to inform future urban climate and building energy projections. Here, the single layer urban canopy model Town Energy Balance (TEB) is coupled to the Weather Research and Forecasting (WRF) model to create WRF‐TEB. The coupling method is described generically, implemented into software, and the code and data are released with a Singularity image to address issues of scientific reproducibility. The coupling is implemented modularly and verified by an integration test. Results show no detectable errors in the coupling. Separately, a meteorological evaluation is undertaken using observations from Toulouse, France. The latter evaluation, during an urban canopy layer heat island episode, shows reasonable ability to estimate turbulent heat flux densities and other meteorological quantities. We conclude that new model couplings should make use of integration tests as meteorological evaluations by themselves are insufficient, given that errors are difficult to attribute because of the interplay between observational errors and multiple parameterization schemes (e.g., radiation, microphysics, and boundary layer).