The model research and development includes: (i) downscaling/ nesting for high resolutions, (ii) improved resolving planetary boundary layer and surface layer structures, (iii) urbanization and sub-layer processes, (iv) improvement of advection schemes, (v) integration of natural and anthropogenic emission inventories, (vi) implementation of gas-phase chemistry mechanisms, (vii) implementation of aerosol dynamics and microphysics, (viii) implementation of aerosol feedback and interactions mechanisms.
The NWP part developed by the HIRLAM consortium (Unden et al., 2002) can be used for operational weather forecasting.
The Enviro-components (Figure 1-right) includes:
For full list of references for the Enviro- & NWP components, including above mentioned, see Baklanov et al. 2017 (https://doi.org/10.5194/gmd-10-2971-2017) & Unden et al. 2002, respectively.
Figure 1. Enviro-HIRLAM schematics (top) and Enviro-components (bottom).
Enviro-HIRLAM can be run in a downscaling chain (Figure 2), for the outer model domain (run at low resolution) the initial and boundary conditions for meteorology and atmospheric composition are taken from ECMWF; vertical levels vary between 40-60; time step varies 30-360 sec; finest horizontal resolution is about 1+ km. Emissions include anthropogenic, biogenic, and natural; and these are pre-processed.
Figure 2. Enviro-HIRLAM downscaling chain from hemispheric- down to urban-scale.
Different parts of the model were evaluated vs. ETEX-1 experiment, Chernobyl accident, Paris summer/winter 2009-2010 campaigns, etc. The model was used in various projects, such as EU FP6 FUMAPEX; FP7 MEGAPOLI, PEGASOS, MACC, TRANSPHORM, MarcoPolo; NordForsk NetFAM, MUSCATEN, CarboNord, CRAICC-PEEX, CRUCIAL, TRAKT-2018; COST Actions – 728, 732, ES0602 ENCWF, ES1004 EuMetChem) and others. Currently, Enviro-HIRLAM is used within frameworks of the Pan-Eurasian EXperiment programme (PEEX; https://www.atm.helsinki.fi/peex). At regional-subregional scales, the model was used for various geographical European domains including Nordic countries, Arctic regions, Asia (China). At urban scales, the model was used for several metropolitan areas such as Copenhagen (Denmark), Paris (France), Rotterdam (The Netherlands), Vilnius (Lithuania), Bilbao (Spain), St. Petersburg (Russia), Shanghai, Beijing, Perl-River Delta (China), Helsinki (Finland), Apatity (Russia), and others.
The model source code is available at the HIRLAM Chemical Branch (https://hirlam.org). Registration and signing of Agreement ”Enviro-HIRLAM Model Code Transfer and Use” are required. Contact at Univ Helsinki Dr. Alexander Mahura – alexander.mahura(at)helsinki.fi – for more information.
Model Application (Examples)
Example of the Enviro-HIRLAM downscaling is shown with focus on the Paris measurement summer 2009 campaign (Figure 3; EU FP7 MEGAPOLI project), where meteorological and chemical fields are nested from a lower (15 km) to a higher (2.5 km) horizontal resolution runs with focus on the Paris metropolitan area (France). To underline impact of urban areas, the high resolution run was performed also with/ without implemented urban module (which includes anthropogenic heat fluxes and effects of building). As seen, the footprint of the metropolitan area was extended for temperature, humidity, wind and cloud cover patterns for larger distances from the city.
Figure 3. Enviro-HIRLAM: downscaling from regional-subregional-to-urban scales (from left to right: CTRL 15—5—2.5 km & 2.5+URB/ urbanization included) for meteorological (top—air temperature, middle—humidity) and chemical (bottom—ozone) fields on 4 Jul 2009, 00+24 UTC.
Another example of the Enviro-HIRLAM simulation for operational purposes is shown with focus on the Shanghai metropolitan area (Figure 4; EU FP7 MarcoPolo project), where simultaneous forecasting of meteorology and particulate matter (PM2.5 & PM10) was performed for the entire China (regional), East China (subregional) and Shahghai (urban scales) regions. Simulations reflected presence of elevated pollution levels in these populated regions due to large-scale industrial activities and related anthropogenic emissions as well as frequent domination of unfavorable meteorological conditions. Chinese densely populated areas (megacities) show overlapping footprint, which is extended on hundreds of kilometers away from the urbanized areas.
Figure 4: Enviro-HILRAM: Forecast (21 Aug 2017 00UTC+12 hour forecast length) for the Eastern China (at 5 km resolution) (left) PM2.5 concentration & for the Shanghai metropolitan area for the (middle) PM2.5 concentration and (right) relative humidity at 2m (incl. plotted wind speed and mean sea level pressure).
The third example of the Enviro-HIRLAM studies on aerosol feedbacks and interactions is shown with focus on the European and Arctic regions territories (Figure 5; Enviro-PEEX project), where simulations were performed in several modes: reference/ control run; run with direct, indirect and combined (direct + indirect) aerosols effects included. The January differences between the control vs. modified runs are less pronounced for average concentration of black carbon in the Arctic compared with other geographical regions. But these differences are observed for maximum concentration, and especially for the Siberia and Ural regions of Russia, where industrial complexes of metallurgy, etc. are located.
Figure 5: Enviro-HIRLAM: Difference fields between CTRL&DAE (left), CTRL&IDAE (middle), CTRL&DAE+IDEA (right) runs with the Enviro-HIRLAM model for monthly (January) averaged concentration of black carbon, BC (in µg/m3) /CTRL – reference run; DAE – Direct Aerosol Effect; IDAE – Indirect Aerosol Effect.
At the University of Helsinki, the Enviro-HIRLAM, being as one the PEEX-Modelling-Platform (PEEX-MP) models (https://www.atm.helsinki.fi/peex/index.php/modelling-tools-demonstration), is further developing within long-term HPC projects – “Enviro-HIRLAM seamless modelling of meteorology-chemistry-aerosols interactions and feedbacks on multi-scales” (at the Centre for Scientific Computing, CSC; https://www.csc.fi/csc) & “PEEX-MP research and development for online coupled integrated meteorology-chemistry-aerosols feedbacks and interactions in weather, climate and atmospheric composition multi-scale modelling” (Enviro-PEEX on ECMWF; https://www.atm.helsinki.fi/peex/index.php/enviro). It is applied for different research tasks according to the PEEX Science Plan (PEEX, 2015; https://www.atm.helsinki.fi/peex/images/PEEX_Science_Plan.pdf) and research proposals. The emphasis is on evaluating and testing the seamless/ online integrated approach for in-depth sensitivity analyses of mechanisms, relationships, feedbacks, interactions, etc. between chemistry-aerosols and meteorology in a changing climate.
The Enviro-HIRLAM model application areas are the following: aerosols-chemistry feedbacks studies on various meteorological variables; effects of various interactions of aerosols and cloud formation processes and radiative forcing on urban-regional scales; boundary layer and sublayer parameterizations; urbanization processes impact on changes in urban weather and climate on urban-subregional-regional scales; studies on atmospheric pollution and its local impacts; improving prediction of extreme weather events; providing meteorology-chemistry input to assessment studies for population and environment; integration modelling results into GIS environment for further risk/vulnerability/consequences/etc. estimation, and others.
Baklanov A., U. Korsholm, A. Mahura, C. Petersen, A. Gross, (2008): Enviro-HIRLAM: on-line coupled modelling of urban meteorology & air pollution. Adv. Sci. Res., 2, 41-46.
Baklanov A., A. Mahura, R. Sokhi (Eds), (2010): Integrated Systems of Meso-Meteorological and Chemical Transport Models, Springer, 192p.
Baklanov, A., Smith Korsholm, U., Nuterman, R., Mahura, A., Nielsen, K. P., Sass, B. H., Rasmussen, A., Zakey, A., Kaas, E., Kurganskiy, A., Sørensen, B., and González-Aparicio, I. (2017): Enviro-HIRLAM online integrated meteorology–chemistry modelling system: strategy, methodology, developments and applications (v7.2), Geosci. Model Dev., 10, 2971-2999, https://doi.org/10.5194/gmd-10-2971-2017, 2017.
Unden, P., L. Rontu, H. Järvinen, P. Lynch, J. Calvo, G. Cats, J. Cuhart, K. Eerola, etc. 2002: HIRLAM-5 Scientific Documentation. Dec 2002, HIRLAM-5 Project Report, SMHI.