Surface and atmospheric driven variability of the single‐layer urban canopy model under clear‐sky conditions over London

Urban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single‐layer urban canopy model when fully interactive with a 1‐D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54‐h period with clear‐sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations

On‐ and off‐line evaluation of the single‐layer urban canopy model in London summertime conditions

Urban canopy models are essential tools in forecasting weather and air quality in cities. However, they require many surface parameters, which are uncertain and can reduce model performance if inappropriately prescribed. Here, we evaluate the model sensitivity of the Single-Layer Urban Canopy Model (SLUCM) in the Weather Research and Forecasting model (WRF) to surface parameters in two different configurations, one coupled to the overlying atmosphere (on-line) in a 1D configuration and one without coupling (off-line). A 2-day summertime period in London is used as a case study, with clear skies and low wind speeds. Our sensitivity tests indicate that SLUCM reacts differently, when coupled to the atmosphere. For certain surface parameters, atmospheric feedback effects can outweigh the variations caused by surface parameter settings. Hence to fully understand model sensitivity atmospheric feedbacks should be considered

Summary and Recommendations from Working Group 1: model uncertainty representations in convection-permitting / shorter lead-time / limited-area ensembles

WG3 discussed both the pros and cons of existing schemes as Working group 1 considered the treatment of model uncertainty (MU) in high-resolution ensembles, at grid spacings of order 1-5 km. These systems are often run for regional weather forecasting, perhaps over a single country, and for lead times of up to 5 days. Looking ahead, ECMWF’s strategy seeks to deliver global medium-range ensemble forecasts with 3-4 km grid spacings by 2030. It is questionable for what grid spacing we should dispense with a deep convection parameterization, but it will be either switched off or damped in these systems, such that deep convection can be assumed to be dominated by explicit motions. One of the problems with limited-area ensemble systems at this scale is that spread depends not only on the modelling system itself but also on the variability inherited from the large-scale boundary conditions. There is often thought to be a lack of spread in our high-resolution EPS (ensemble prediction systems), but this could reflect a lack of diversity on larger scales. The relative importance of lateral-boundary diversity and the model uncertainty mechanisms is regime dependent. The lateral boundaries will generally be more important in midlatitude winter but less so for summertime convection in relatively weak synoptic flow

Surface and Atmospheric Driven Variability of the Single-Layer Urban Canopy Model Under Clear-Sky Conditions Over London

Urban canopy models (UCMs) are parametrization schemes that are used to improve weather forecasts in urban areas. The performance of UCMs depends on understanding potential uncertainty sources that can generally originate from the (a) urban surface parameters, (b) atmospheric forcing, and (c) physical description. Here, we investigate the relative importance of surface and atmospheric driven model sensitivities of the single-layer urban canopy model when fully interactive with a 1-D configuration of the Weather Research and Forecasting model (WRF). The impact of different physical descriptions in UCMs and other key parameterization schemes of WRF is considered. As a case study, we use a 54-hr period with clear-sky conditions over London. Our analysis is focused on the surface radiation and energy flux partitioning and the intensity of turbulent mixing. The impact of changes in atmospheric forcing and surface parameter values on model performance appears to be comparable in magnitude. The advection of potential temperature, aerosol optical depth, exchange coefficient and roughness length for heat, surface albedo, and the anthropogenic heat flux are the most influential. Some atmospheric forcing variations have similar impact on the key physical processes as changes in surface parameters. Hence, error compensation may occur if one optimizes model performance using a single variable or combinations that have potential for carryover effects (e.g., temperature). Process diagrams help differences to be understood in the physical description of different UCMs, boundary layer, and radiation schemes and between the model and the observations

Associated results of Phase 1 of the Urban-PLUMBER model evaluation project

Archive of: https://urban-plumber.github.io/AU-Preston/plots/ Files in this folder are associated with the manuscript: “Evaluation of 30 urban land surface models in the Urban-PLUMBER project: Phase 1 results” Files are an archive of the website https://urban-plumber.github.io/AU-Preston/plots/ as of 2nd December 2022. Use of any data must give credit through citation of the above manuscript, the data repository, and other site references as appropriate. Corresponding author: Mathew Lipson ([email protected]) Usage Load the "index.html" to navigate through plots and results subpages Description These files include results from Phase 1 of the Urban-PLUMBER model evaluation project for urban areas. Data includes: - individual model results (error metrics) and submission metadata - individual model plots (timeseries, subsets, energy closure, distributions) - collective timeseries for every submitted output in the baseline experiment - collective timeseries for every submitted output in the detailed experiment - supplementary material for the manuscript - variable definitions Authors Mathew Lipson, Sue Grimmond, Martin Best, Gab Abramowitz, Andrew Coutts, Nigel Tapper, Jong-Jin Baik, Meiring Beyers, Lewis Blunn, Souhail Boussetta, Elie Bou-Zeid, Martin G. De Kauwe, Cécile de Munck, Matthias Demuzere, Simone Fatichi, Krzysztof Fortuniak, Beom-Soon Han, Maggie Hendry, Yukihiro Kikegawa, Hiroaki Kondo, Doo-Il Lee, Sang-Hyun Lee, Aude Lemonsu, Tiago Machado, Gabriele Manoli, Alberto Martilli, Valéry Masson, Joe McNorton, Naika Meili, David Meyer, Kerry A. Nice, Keith W. Oleson, Seung-Bu Park32, Michael Roth33, Robert Schoetter34, Andres Simon35, Gert-Jan Steeneveld, Ting Sun, Yuya Takane, Marcus Thatcher, Aristofanis Tsiringakis, Mikhail Varentsov, Chenghao Wang, Zhi-Hua Wan

Evaluation of 30 urban land surface models in the Urban-PLUMBER project:Phase 1 results

Abstract 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

Evaluation of 30 urban land surface models in the Urban‐PLUMBER project: phase 1 results

Accurately predicting weather and climate in cities is critical for safeguarding human health and strengthening urban resilience. Multi‐model evaluations can lead to model improvements, however there have been no major intercomparisons of urban‐focused land surface models in over a decade. Here, in Phase 1 of the Urban‐PLUMBER project, we evaluate 30 land surface models' ability 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 shortwave radiation, sensible and latent heat fluxes, but little or no improvement in longwave 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 twenty sites in different urban and regional climate zones