OVERALL PERFORMANCE MEASURES
1st QUARTER METRIC COMPLETED:
Demonstrating Use of E3SM With an Urban Canopy Parameterization for Modeling Urban Impact on Local-to-Regional Heat Extremes
Product Definition
Urban areas exhibit distinct biophysical, morphological, and thermodynamic characteristics that influence local and regional weather and climate. These impacts include changes to the surface energy budget, near-surface meteorology, atmospheric composition, hydrological cycle, energy systems, and carbon cycle (Arnfield 2003, Qian et al. 2022). Urbanization disproportionately affects local populations by altering weather, particularly heat extremes (Tuholske et al. 2021). However, limited observations and model deficiencies hinder the understanding of urban systems (Grimmond 2011, Muller et al. 2013). Despite advancements, key aspects of urbanization, such as spatial variability and temporal evolution, remain poorly captured in models, especially Earth system models (ESMs), which are essential for studying climate processes (Chakraborty and Qian 2024). As we move from rural to suburban to urban regions, we expect sharp gradients in temperature, humidity, wind, and cloud cover that ESMs at coarse resolutions cannot resolve. The challenges of accurately representing within-city variability, relevant for examining community-scale heat hazards, highlight the need for improving ESM modeling capabilities to address uncertainties in urban environments (Sharma et al. 2021).
Most ESMs have limited or no representation of urban areas due to a legacy focus on large-scale climate impacts and coarse model grids that overlook urban processes (Zheng et al. 2021). The U.S. Department of Energy’s (DOE) Energy Exascale Earth System Model (E3SM; Golaz et al. 2019) is one of the few exceptions, incorporating an urban canopy model from the Community Land Model (CLM) using an "urban canyon" approach (Oleson et al. 2010) that represents roofs, walls, and canyon floors (Figure 1a). A global urban surface biophysical data set (Jackson et al. 2010), critical for constraining the surface energy budget, is also embedded in the E3SM Land Model (ELM). However, this model has two major limitations.
First, the urban surface data set in ELM is coarse-grained and outdated. Urban areas are categorized into three density classes across 33 global regions, with uniform radiative, morphological, and thermal properties that fail to capture real-world variability. For example, the prescribed albedo values differ significantly from satellite observations, which reveal greater variability within and across these 33 regions (Figure 1b). Poorly constrained urban parameters have a larger impact on model performance than model complexity, as evidenced by discrepancies in urban heat island (UHI) intensity simulations (Chakraborty et al. 2021, Grimmond 2011). Second, it neglects a key feature of many urban areas by not considering any vegetation within the urban canyon. As such, urban areas are treated as biologically inactive, with pervious surfaces modeled as bare soil. This neglects the critical role of urban vegetation in influencing temperature, air quality, and the water, energy, and carbon cycles within cities (Paschalis et al. 2021). In the context of extreme heat, it is well established that urban greenery mitigates daytime heat hazards (Ziter et al. 2019), a key requirement for effective climate adaptation in a warming world. While meso- and micro-scale models have advanced to include urban vegetation explicitly (Krayenhoff 2021), ESMs have lagged, often relying on outdated tiled approaches that cannot resolve spatial variability within urban areas (Krayenhoff 2020).
With ESMs now running at finer spatial resolutions, these deficiencies become more critical. For example, even when the Simple Cloud Resolving E3SM Atmosphere Model (SCREAM; Caldwell 2021) is run at km scale, the land cover constraints are still coarse, thus underestimating near-surface heterogeneity. High-resolution modeling, in theory, can reveal urban heat hotspots, UHI patterns, and urban-induced cloud formation (Theeuwes et al. 2019), aiding urban planning, isolating communities vulnerable to urban environmental stressors (Chakraborty et al. 2023), and guiding stormwater management strategies. However, SCREAM would still underestimate variability within and across cities due to its simplified representation of urban areas. To accurately capture urban climate signals, models must incorporate spatially continuous and biologically active urban representations, particularly for fine scale simulations. This would improve insights into urban impacts and enhance planning for climate adaptation and resilience.
This report documents recent improvements in ELM and the ELM urban model through the Integrated Coastal Modeling (ICoM) project and a DOE Early Career project that advance surface constraints for high-resolution urban-resolving E3SM simulations. We explore these improvements and their implications for urban heat and heat stress using both land-atmosphere coupled SCREAM simulations at 3.2 km and ELM land-only simulations over the United States for a heatwave period in July 2020. The coupled simulations run with and without spatially explicit urban surface data sets show improvements in capturing urban heat signals and their spatial and diurnal variability, while the land-only simulations allow us to estimate the sensitivity of urban heat to radiative and morphological urban parameters.
Product Documentation
This report documents the modeling of urban heat extremes in the contiguous United States using SCREAM with regional refinement capability. To accommodate km-scale modeling, we update the surface data of SCREAM based on several 1-km data sets to resolve spatial variability more realistically.
High-Resolution Land Cover Constraints
A global 1-km data set of land surface parameters was developed by Li et al. (2024) as part of the ICoM project using a combination of data sources. This data set, covering the years 2001 to 2020, provides higher-resolution information and more spatial variability of land use and land cover (LULC), vegetation properties, soil properties, and topography than conventional coarse-resolution data sets. Specifically, we update organic matter, clay, and sand percentages in soil, lake, and glacier percentages, the fractions of different plant function types in natural vegetation, leaf area index, stem area index, and canopy height information in the SCREAM surface data.
Global Spatially Explicit Urban Biophysical Properties for Urban Scale Modeling
The urban component of E3SM requires facet-level (roofs, walls, roads, etc.) properties critical for constraining the surface energy budget and anthropogenic signals. Recent work supported by a DOE Early Career project has developed U-Surf, a new global data set of urban surface parameters at a 1-km resolution (Figure 2b). U-Surf leverages high-resolution satellite remote-sensing data, machine learning techniques, and planetary-scale geospatial analyses on cloud computing platforms to generate a comprehensive and internally consistent set of biophysical parameters for urban areas worldwide. These parameters include radiative, structural, and thermal properties, aligning with the structural requirements of various urban canopy models, particularly E3SM (Figure 2). This compatibility enables realistic comparisons of urban climate signals both within and across cities. More details can be found in Cheng et al. 2024.
We update urban properties in the SCREAM surface data using U-Surf except for urban fractions, which are from another global 1-km data set of annual urban dynamics between 1870 and 2100 developed by Li et al. (2021), who trained an urban cellular automata model using satellite observations of urban extent between 1992 and 2013 and ran the model to simulate urban dynamics before 1990 and after 2020 under five Shared Socioeconomic Pathways (SSPs). The data set thus provides temporally continuous self-consistent high-resolution urban coverages, which is helpful in studies relevant to urban expansion and shrinkage. Urban fractions in 2010 from the data set are used to update our SCREAM surface data.
Regionally Refined Simulations for Km-Scale Urban-Resolving Continental Simulations
Although the update of LULC percentages, soil properties, and vegetation parameters in the surface data can also influence the simulation of heat extremes, its comparison with the default ELM surface data set has already been performed by Li et al. (2024). In this report, we use this data set in all the simulations and focus on understanding how updated urban properties from U-Surf can alter the heat extreme simulation since these properties are more relevant to the urban model. We conduct two atmosphere-land coupled SCREAM simulations, one with the default urban properties (hereafter named the default simulation) and the other with U-Surf urban properties (hereafter named the U-Surf simulation) to investigate the potential impact of updated urban properties on the simulation of a heat wave that occurred in late July 2020 in the eastern United States. The simulation period is from July 18 to July 30 with the first day as spin-up. To reduce the computational cost, we run SCREAM on a regional refinement mesh (RRM) with a horizontal resolution of ~3.2 km over the contiguous U.S. (CONUS) and ~100 km for other areas globally (Figure 3a). The atmospheric initial condition is based on a combination of the hourly 3-km High-Resolution Rapid Refresh (HRRR) and 25-km ERA5 reanalysis data sets. The land's initial condition is obtained from a 10-year land-only simulation on the same SCREAM RRM constrained by atmospheric forcing in 2020 from ERA5. The SCREAM simulation uses prescribed hourly sea surface temperature (SST) from ERA5. Three-dimensional zonal and meridional winds, temperature, and specific humidity above 850 hPa are nudged towards hourly ERA5 reanalysis with a relaxation timescale of six hours for the coarse-resolution grids with equivalent physical grid spacing larger than 5 km. Finally, we regrid the coupled simulation outputs to 4 km to facilitate comparison and analysis.
Besides the above two coupled simulations, we run five additional land-only simulations to resolve the distinct impacts of facet-level albedo, facet-level emissivity, and morphological (roof height, roof fraction, pervious road fraction, height of wind in canyon, and canyon height to width ratio) urban parameters from U-Surf on the heat extreme simulation (Table 1). The land-only simulation period is from July 19 to July 30, and the simulations are constrained by atmospheric forcing from the default coupled simulation. The restart file at 0:00Z on July 19 from the default coupled simulation is used as the initial condition for the land-only simulations except for the “All New Properties” simulation, which uses the restart file from the U-Surf coupled simulation due to different urban levels between the default and U Surf urban property data set.