The societal benefits of numerical weather prediction (NWP) forecasts are most evident in populated areas. An urban representation within NWP models should provide improved forecast accuracy. Here, ...we present the preliminary implementation of an urban scheme within the Integrated Forecasting System (IFS) using a simplified single‐layer urban canopy model. The scheme makes assumptions of canyon geometry and considers fluxes from roads, walls, and roofs. Temperature observations were used to optimize single‐column model (SCM) parameters using the Gauss‐Newton method. Observation comparisons over six European cities, show a 2‐m temperature root‐mean‐squared error reduction from 1.85 to 1.75 K with the urban scheme. Optimized parameters were used globally at kilometric scale in a land surface model. A sensitivity experiment assuming a 100% urban world showed spatially averaged northern hemisphere 2‐m temperatures increased by 0.54 K (January) and 0.42 K (July) at night caused by changes in the albedo, emissivity, roughness, and thermal and hydrological properties. Global ∼1‐km resolution simulations using ancillary urban mapping information produce an urban heat island effect over major and minor conurbations. Only major conurbations were well represented at ∼9‐km resolution. Results from SCM simulations show a heightening of the planetary boundary layer over city sites, with the largest enhancements occurring at night in July (84 ± 48 m) caused by an increased sensible heat flux. These initial developments show the importance of a high‐resolution urban representation within NWP models. Improved parameterization and mapping will enable an online representation of energy, water, and trace gas fluxes over residential areas.
Plain Language Summary
Urban areas make up only a small fraction of the Earth's surface; however, they are home to over 50% of the world's population. In these areas a phenomenon known as the urban heat island effect causes increased temperatures due to human activities, an effect often missing in weather forecasts. Forecasts, generated using computer models, consider not only the atmosphere but also the role of the land surface on the weather above. Typically these models do not include an urban map, so they miss key urban processes. We introduced a representation of urban areas to the model of the European Center for Medium‐Range Weather Forecasts. We considered several ways in which the urban environment interacts with the weather, including through changes in heat storage and treatment of rainfall. We find these developments result in a more accurate model forecast over six European cities. The model accurately predicts the increased heating observed over cities at night and some of the observed changes in the atmosphere. Future work should continue to improve the urban representation in weather and air quality/greenhouse gas models by implementing an urban scheme in operational forecasts.
Key Points
An urban scheme has been introduced and optimized within the ECMWF IFS single‐column and surface‐only model
Assuming an urban world, average nighttime 2‐m temperatures increased for January (0.54 K) and July (0.42 K) in a surface only simulation
Using realistic urban cover for eight cities, PBL height in July increases by an average of 66 and 84 m for the day and night, respectively
The impact of urbanization on local weather patterns affects over half the global population. Global numerical weather prediction systems have reached a resolution at which urban conurbations can be ...spatially resolved, justifying their representation within land surface parameterizations with the aim of improving local predictions. Additionally, real‐time atmospheric monitoring of trace gas emissions can utilize weather variables relevant for urban areas. We investigated whether a simple single‐layer urban canopy scheme can be used within a global forecast model to jointly improve predictions of near‐surface weather variables and residential CO2 emissions. The scheme has been implemented in the Integrated Forecast System used operationally at the European Centre for Medium‐Range Weather Forecasts running at ∼9 km horizontal resolution. First, we selected a suitable urban land cover map (ECOCLIMAP‐SG) based on comparisons with regional data and land surface temperature MODIS retrievals. The urban scheme is verified by providing improved 2 m temperature (∼10%) and 10 m wind (∼17%) RMSE values for both summer and winter months around urban environments. The influence of the scheme was most noticeable at night. Additionally, we have implemented a simple temperature‐dependent residential emissions model to calculate real‐time CO2 heating emissions. These were validated against existing offline products, national reporting and by comparing atmospheric simulations with total column CO2 observations. The results show an improved temporal variability of emissions, which arise from synoptic scale temperature changes. Given the improved predictability from the urban scheme for both weather and emissions, it will be operationally implemented in an upcoming model cycle.
Plain Language Summary
In urban areas, temperatures are often elevated due to an effect known as the urban heat island. Although global weather forecasts, generated using computer models, typically include a representation of land surface processes, they often do not include the urban environment. We implemented a relatively simple urban scheme in the model of the European Centre for Medium‐Range Weather Forecasts and selected an appropriate urban cover map to use by comparing forecast land temperatures with satellite observations. We then compared this scheme with observations from urban sites around the globe and found improved temperature and wind forecasts. Furthermore, we used information from the urban scheme to generate a global forecast of residential CO2 emissions from heating. We find that by forecasting these emissions using the weather model we improve our prediction of atmospheric CO2 concentrations around urban environments.
Key Points
Several urban land cover maps were evaluated using satellite land‐surface temperature retrievals and independent data
At ∼9 km horizontal resolution, an urban scheme improves modeled 2 m temperature and 10 m wind forecasts over urban areas of varying size
An online residential heating CO2 emissions model (Modeling Emissions from Heating in Near‐real‐time Driven by the Integrated Forecasting System) using model variables improves forecasts of atmospheric concentrations
Framed within the Copernicus Climate Change Service (C3S) of the European Commission, the European Centre for Medium-Range Weather Forecasts (ECMWF) is producing an enhanced global dataset for the ...land component of the fifth generation of European ReAnalysis (ERA5), hereafter referred to as ERA5-Land. Once completed, the period covered will span from 1950 to the present, with continuous updates to support land monitoring applications. ERA5-Land describes the evolution of the water and energy cycles over land in a consistent manner over the production period, which, among others, could be used to analyse trends and anomalies. This is achieved through global high-resolution numerical integrations of the ECMWF land surface model driven by the downscaled meteorological forcing from the ERA5 climate reanalysis, including an elevation correction for the thermodynamic near-surface state. ERA5-Land shares with ERA5 most of the parameterizations that guarantees the use of the state-of-the-art land surface modelling applied to numerical weather prediction (NWP) models. A main advantage of ERA5-Land compared to ERA5 and the older ERA-Interim is the horizontal resolution, which is enhanced globally to 9 km compared to 31 km (ERA5) or 80 km (ERA-Interim), whereas the temporal resolution is hourly as in ERA5. Evaluation against independent in situ observations and global model or satellite-based reference datasets shows the added value of ERA5-Land in the description of the hydrological cycle, in particular with enhanced soil moisture and lake description, and an overall better agreement of river discharge estimations with available observations. However, ERA5-Land snow depth fields present a mixed performance when compared to those of ERA5, depending on geographical location and altitude. The description of the energy cycle shows comparable results with ERA5. Nevertheless, ERA5-Land reduces the global averaged root mean square error of the skin temperature, taking as reference MODIS data, mainly due to the contribution of coastal points where spatial resolution is important. Since January 2020, the ERA5-Land period available has extended from January 1981 to the near present, with a 2- to 3-month delay with respect to real time. The segment prior to 1981 is in production, aiming for a release of the whole dataset in summer/autumn 2021. The high spatial and temporal resolution of ERA5-Land, its extended period, and the consistency of the fields produced makes it a valuable dataset to support hydrological studies, to initialize NWP and climate models, and to support diverse applications dealing with water resource, land, and environmental management.
Lakes influence the structure of the atmospheric boundary layer and, consequently, the local weather and local climate. Their influence should be taken into account in the numerical weather ...prediction (NWP) and climate models through parameterisation. For parameterisation, data on lake characteristics external to the model are also needed. The most important parameter is the lake depth. Global database of lake depth GLDB (Global Lake Database) is developed to parameterise lakes in NWP and climate modelling. The main purpose of the study is to upgrade GLDB by use of indirect estimates of the mean depth for lakes in boreal zone, depending on their geological origin. For this, Tectonic Plates Map, geological, geomorphologic maps and the map of Quaternary deposits were used. Data from maps were processed by an innovative algorithm, resulting in 141 geological regions where lakes were considered to be of kindred origin. To obtain a typical mean lake depth for each of the selected regions, statistics from GLDB were gained and analysed. The main result of the study is a new version of GLDB with estimations of the typical mean lake depth included. Potential users of the product are NWP and climate models.
Lakes are of fundamental importance in the Earth system as they support essential environmental and economic services, such as freshwater supply. Streamflow variability and temporal evolution are ...impacted by the presence of lakes in the river network; therefore, any change in the lake state can induce a modification of the regional hydrological regime. Despite the importance of the impact of lakes on hydrological fluxes and the water balance, a representation of the mass budget is generally not included in climate models and global-scale hydrological modeling platforms. The goal of this study is to introduce a new lake mass module, MLake (Mass-Lake model), into the river-routing model CTRIP to resolve the specific mass balance of open-water bodies. Based on the inherent CTRIP parameters, the development of the non-calibrated MLake model was introduced to examine the influence of such hydrological buffer areas on global-scale river-routing performance.
Atmospheric flux inversions use observations of atmospheric CO2 to provide anthropogenic and biogenic CO2 flux estimates at a range of spatio-temporal scales. Inversions require prior flux, a forward ...model and observation errors to estimate posterior fluxes and uncertainties. Here, we investigate the forward transport error and the associated biogenic feedback in an Earth system model (ESM) context. These errors can occur from uncertainty in the initial meteorology, the analysis fields used, or the advection schemes and physical parameterisation of the model. We also explore the spatio-temporal variability and flow-dependent error covariances. We then compare the error with the atmospheric response to uncertainty in the prior anthropogenic emissions. Although transport errors are variable, average total-column CO2 (XCO2) transport errors over anthropogenic emission hotspots (0.1–0.8 ppm) are comparable to, and often exceed, prior monthly anthropogenic flux uncertainties projected onto the same space (0.1–1.4 ppm). Average near-surface transport errors at three sites (Paris, Caltech and Tsukuba) range from 1.7 to 7.2 ppm. The global averageXCO2 transport error standard deviation plateaus at ∼0.1 ppm after 2–3 d, after which atmospheric mixing significantly dampens the concentration gradients. Error correlations are found to be highly flow dependent, with XCO2 spatio-temporal correlation length scales ranging from 0 to 700 km and 0 to 260 min. Globally, the average model error caused by the biogenic response to atmospheric meteorological uncertainties is small (<0.01 ppm); however, this increases over high flux regions and is seasonally dependent (e.g. the Amazon; January and July:0.24±0.18 ppm and 0.13±0.07 ppm). In general, flux hotspots are well-correlated with model transport errors. Our model error estimates, combined with the atmospheric response to anthropogenic flux uncertainty, are validated against three Total Carbon Observing Network (TCCON) XCO2 sites. Results indicate that our model and flux uncertainty account for 21 %–65 % of the total uncertainty. The remaining uncertainty originates from additional sources, such as observation, numerical and representation errors, as well as structural errors in the biogenic model. An underrepresentation of transport and flux uncertainties could also contribute to the remaining uncertainty. Our quantification of CO2 transport error can be used to help derive accurate posterior fluxes and error reductions in future inversion systems. The model uncertainty diagnosed here can be used with varying degrees of complexity and with different modelling techniques by the inversion community.
Abstract
The impact of urbanization on local weather patterns affects over half the global population. Global numerical weather prediction systems have reached a resolution at which urban ...conurbations can be spatially resolved, justifying their representation within land surface parameterizations with the aim of improving local predictions. Additionally, real‐time atmospheric monitoring of trace gas emissions can utilize weather variables relevant for urban areas. We investigated whether a simple single‐layer urban canopy scheme can be used within a global forecast model to jointly improve predictions of near‐surface weather variables and residential CO
2
emissions. The scheme has been implemented in the Integrated Forecast System used operationally at the European Centre for Medium‐Range Weather Forecasts running at ∼9 km horizontal resolution. First, we selected a suitable urban land cover map (ECOCLIMAP‐SG) based on comparisons with regional data and land surface temperature MODIS retrievals. The urban scheme is verified by providing improved 2 m temperature (∼10%) and 10 m wind (∼17%) RMSE values for both summer and winter months around urban environments. The influence of the scheme was most noticeable at night. Additionally, we have implemented a simple temperature‐dependent residential emissions model to calculate real‐time CO
2
heating emissions. These were validated against existing offline products, national reporting and by comparing atmospheric simulations with total column CO
2
observations. The results show an improved temporal variability of emissions, which arise from synoptic scale temperature changes. Given the improved predictability from the urban scheme for both weather and emissions, it will be operationally implemented in an upcoming model cycle.
Plain Language Summary
In urban areas, temperatures are often elevated due to an effect known as the urban heat island. Although global weather forecasts, generated using computer models, typically include a representation of land surface processes, they often do not include the urban environment. We implemented a relatively simple urban scheme in the model of the European Centre for Medium‐Range Weather Forecasts and selected an appropriate urban cover map to use by comparing forecast land temperatures with satellite observations. We then compared this scheme with observations from urban sites around the globe and found improved temperature and wind forecasts. Furthermore, we used information from the urban scheme to generate a global forecast of residential CO
2
emissions from heating. We find that by forecasting these emissions using the weather model we improve our prediction of atmospheric CO
2
concentrations around urban environments.
Key Points
Several urban land cover maps were evaluated using satellite land‐surface temperature retrievals and independent data
At ∼9 km horizontal resolution, an urban scheme improves modeled 2 m temperature and 10 m wind forecasts over urban areas of varying size
An online residential heating CO
2
emissions model (Modeling Emissions from Heating in Near‐real‐time Driven by the Integrated Forecasting System) using model variables improves forecasts of atmospheric concentrations
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