Abstract. In this paper, we discuss the method for validation of random uncertainties in the remote sensing measurements based on evaluation of the structure function, i.e., root-mean-square differences as a function of increasing spatiotemporal separation of the measurements. The limit at the zero mismatch provides the experimental estimate of random noise in the data. At the same time, this method allows probing of the natural variability of the measured parameter. As an illustration, we applied this method to the clear-sky total ozone measurements by the TROPOspheric Monitoring Instrument (TROPOMI) on board the Sentinel-5P satellite. We found that the random uncertainties reported by the TROPOMI inversion algorithm, which are in the range 1–2 DU, agree well with the experimental uncertainty estimates by the structure function. Our analysis of the structure function has shown the expected results on total ozone variability: it is significantly smaller in the tropics compared to mid-latitudes. At mid-latitudes, ozone variability is much larger in winter than in summer. The ozone structure function is anisotropic (being larger in the latitudinal direction) at horizontal scales larger than 10–20 km. The structure function rapidly grows with the separation distance. At mid-latitudes in winter, the ozone values can differ by 5 % at separations 300–500 km. The method discussed is a powerful tool in experimental estimates of the random noise in data and studies of natural variability, and it can be used in various applications.
Abstract. Satellite measurements in nadir and limb viewing geometry provide a complementary view of the atmosphere. An effective combination of the limb and nadir measurements can give new information about atmospheric composition. In this work, we present tropospheric ozone column datasets that have been created using a combination of total ozone columns from OMI (Ozone Monitoring Instrument) and TROPOMI (TROPOspheric Monitoring Instrument) with stratospheric ozone column datasets from several available limb-viewing instruments: MLS (Microwave Limb Sounder), OSIRIS (Optical Spectrograph and InfraRed Imaging System), MIPAS (Michelson Interferometer for Passive Atmospheric Sounding), SCIAMACHY (SCanning Imaging Spectrometer for Atmospheric CHartographY), OMPS-LP (Ozone Mapping and Profiles Suite – Limb Profiler), and GOMOS (Global Ozone Monitoring by Occultation of Stars). We have developed further the methodological aspects of the assessment of tropospheric ozone using the residual method supported by simulations with the chemistry transport model SILAM (System for Integrated modeLling of Atmospheric coMposition). It has been shown that the accurate assessment of ozone in the upper troposphere and the lower stratosphere (UTLS) is of high importance for detecting the ground-level ozone patterns. The stratospheric ozone column is derived from a combination of ozone profiles from several satellite instruments in limb-viewing geometry. We developed a method for the data homogenization, which includes the removal of biases and a posteriori estimation of random uncertainties, thus making the data from different instruments compatible with each other. The high-horizontal- and vertical-resolution dataset of ozone profiles is created via interpolation of the limb profiles from each day to a 1∘×1∘ horizonal grid. A new kriging-type interpolation method, which takes into account data uncertainties and the information about natural ozone variations from the SILAM-adjusted ozone field, has been developed. To mitigate the limited accuracy and coverage of the limb profile data in the UTLS, a smooth transition to the model data is applied below the tropopause. This allows for the estimation of the stratospheric ozone column with full coverage of the UTLS. The derived ozone profiles are in very good agreement with collocated ozonesonde measurements. The residual method was successfully applied to OMI and TROPOMI clear-sky total ozone data in combination with the stratospheric ozone column from the developed high-resolution limb profile dataset. The resulting tropospheric ozone column is in very good agreement with other satellite data. The global distributions of tropospheric ozone exhibit enhancements associated with the regions of high tropospheric ozone production. The main datasets created are (i) a monthly 1∘×1∘ global tropospheric ozone column dataset (from ground to 3 km below the tropopause) using OMI and limb instruments, (ii) a monthly 1∘×1∘ global tropospheric ozone column dataset using TROPOMI and limb instruments, and (iii) a daily 1∘×1∘ interpolated stratospheric ozone column from limb instruments. Other datasets, which are created as an intermediate step of creating the tropospheric ozone column data, are (i) a daily 1∘×1∘ clear-sky and total ozone column from OMI and TROPOMI, (ii) a daily 1∘×1∘ homogenized and interpolated dataset of ozone profiles from limb instruments, and (iii) a daily 1∘×1∘ dataset of ozone profiles from SILAM simulations with adjustment to satellite data. These datasets can be used in various studies related to variability and trends in ozone distributions in both the troposphere and the stratosphere. The datasets are processed from the beginning of OMI and TROPOMI measurements until December 2020 and are planned to be regularly extended in the future.
<p>Urban green areas have multiple benefits extending from heat mitigation and carbon sinks to human well-being. Due to their multi-benefits, they are an attractive natural solution to aid climate change adaptation and mitigation. In cities of Helsinki and Tampere located in Finland, intensive observations and modelling of urban water and carbon dioxide (CO<sub>2</sub>) fluxes have taken place to improve our understanding of the functioning and carbon sequestration potential of different urban green areas and provide science-based evidence for decision-makers on how urban green areas should be planned and constructed to maximize their climate benefits.</p> <p>Extensive eco-physiological observations were collected from different vegetation types (urban forest, park, garden, and street vegetation) in Helsinki during summers 2020-2022. The observations were made in the vicinity of the ICOS Associated Ecosystem Station FI-Kmp where eddy covariance (EC) measurements presenting the ecosystem level are conducted. The measurements included photosynthesis, sap flow, soil respiration, phenology, fine root growth, meteorology and soil properties. FI-Kmp represents mixed land use and vegetation, and to get more information of the behavior of lawns, additional EC measurements were conducted over urban lawn in the city of Espoo in 2021-2022. The observations are complemented by ecosystem modelling using SUEWS (Surface Urban Energy and Water balance Scheme). SUEWS is used to examine the impact of different urban green area planning options on carbon sinks and storages with focus on the city of Tampere.&#160;</p> <p>This work will highlight some of the findings made so far and provide examples on the carbon and water fluxes in different urban green areas. We also demonstrate how science-based knowledge can aid decision-making concerning urban green areas.</p>
Abstract. The Surface Urban Energy and Water Balance Scheme (SUEWS) has recently been introduced to include a bottomup approach to modelling carbon dioxide (CO2) emissions and sink in urban areas. In this study, SUEWS is evaluated against radiation flux observations and eddy covariance (EC) measured turbulent fluxes of sensible heat (QH), latent heat (QE), and CO2 (FC) at a densely built neighborhood in Beijing. The model sensitivity to maximum conductance (gmax) and leaf area index (LAI) is examined. Site-specific gmax is obtained from observations over local vegetation species, and LAI parameters by optimization with remotely sensed LAI obtained from a MODIS/Terra data product. For simulation of anthropogenic CO2 components, local traffic and population data are collected. In model evaluation, the mismatch between the measurement source area and simulation domain is also considered. Using the optimized gmax and LAI, the modelling of heat fluxes is noticeably improved, showing higher correlation with observations, lower bias, and more realistic seasonal dynamics of QE and QH. In comparison to heat fluxes, the FC module shows lower sensitivity to the choice of gmax and LAI. This can be explained by the low relative contribution of vegetation to net FC in the modelled area. SUEWS successfully reproduces the average diurnal cycle of FC and annual cumulative sums. Depending on the size of the simulation domain, the modelled annual accumulated FC ranges from 7.2 to 8.5 kg C m−2 yr−1, when compared to 7.5 kg C m−2 yr−1 observed by EC. Traffic is the dominant CO2 source, contributing 63–73 % to the annual total CO2 emissions, followed by human metabolism (14–18 %), respiration released by vegetation and soil (6–11 %) and building heating (6–9 %). Vegetation photosynthesis offsets only 4–8 % of the total CO2 emissions. We highlight the importance of choosing optimal LAI parameters and gmax when SUEWS is used to model surface fluxes. The FC module of SUEWS is a promising tool in quantifying urban CO2 emissions at the local scale, and therefore assisting to mitigate urban CO2 emissions.
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