Search

This paper presents the third edition of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data, CLARA-A3. The content of earlier CLARA editions, namely cloud, surface albedo, and surface radiation products, has been extended with two additional surface albedo products (blue and white sky albedo), three additional surface radiation products (net shortwave and longwave radiation, surface radiation budget) and two top of atmosphere radiation budget products (reflected solar flux and outgoing longwave radiation). The record length is extended to 42 years (1979–2020) by also incorporating results from the first version of the Advanced Very High Resolution Radiometer (AVHRR) imager (AVHRR/1). A continuous extension of the climate data record (CDR) has also been implemented by processing an interim climate data record (ICDR) based on the same set of algorithms but with slightly changed ancillary input data. All products are briefly described together with validation results and inter-comparisons with currently existing similar CDRs. The extension of the product portfolio and the temporal coverage of the data record, together with product improvements, is expected to enlarge the potential of using CLARA-A3 for climate change studies and, in particular, studies of potential feedback effects between clouds, surface albedo and radiation. The CLARA-A3 data record is hosted by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Climate Monitoring (CM SAF) and is freely available at https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003.

Seasonal snow in the northern regions plays an important role providing water resources for both consumption and hydropower generation. Moreover, the snow depth in the northern Finland during winter exceeds 1 m, impacting local agriculture, vegetation, tourism and recreational activities. The objective of this study is to estimate snow depth using an empirical methodology applied to synthetic aperture radar (SAR) images and compare with in situ measurements collected by automatic weather stations (AWS) and snow courses in northern Finland. Snow depth estimates with high spatiotemporal resolution can improve our understand of seasonal snow mass in complex access areas. Here, we use an adapted version of the empirical methodology developed by Lievens et al. (2019) to estimate snow depth using Sentinel-1 constellation (C-band). The algorithm utilizes changes in the cross-polarized backscatter measurements of SAR images repeatedly acquired on the same orbit to avoid geometry distortions. We use SNAP toolbox, combined with the Copernicus digital elevation model (DEM), posted at 30 meters, in the pre-processing stage. The snow retrievals between 2019 and 2022 are compared to three automatic weather stations and four snow courses measurements collected over the same period. The ongoing Sentinel-1 snow depth retrievals during the winter 2021/2022 demonstrate a correlation of 0.76, when compared to in situ measurements, supporting the potential ability to derive snow changes in regions where in situ measurements of snow are currently lacking. Despite the good agreement between the empirical algorithm and the collected datasets on land, further investigation is still necessary to better understand the backscatter response over frozen lake areas. Thanks to the effort of international space agencies, we have available currently, and in a near future, global coverage at high resolution SAR imagery and, combined with installed automatic weather stations, opens the possibility of a wide spatial monitoring of snow variations.

Biogenic volatile organic compounds (BVOCs) emitted by the forests are known to have strong impacts in the atmosphere. However, lots of missing reactivity is found, especially in the forest air. Therefore better characterization of sources and identification/quantification of unknown reactive compounds is needed. While isoprene and monoterpene (MT) emissions of boreal needle trees have been studied quite intensively, there is much less knowledge on the emissions of boreal deciduous trees and emissions of larger terpenes and oxygenated volatile organic compounds (OVOCs). Here we quantified the downy birch (Betula pubescens) leaf emissions of terpenes, oxygenated terpenes and green leaf volatiles (GLVs) at the SMEAR II boreal forest site using in situ gas chromatographs with mass spectrometers. Sesquiterpenes (SQTs) and oxygenated sesquiterpenes (OSQTs) were the main emitted compounds. Mean emission rates of SQTs and OSQTs were significantly higher in the early growing season (510 and 650 ng g dw - 1 h −1 , respectively) compared to in the main (40 and 130 ng g dw - 1 h −1 , respectively) and late (14 and 46 ng g dw - 1 h −1 , respectively) periods, indicating that early leaf growth is a strong source of these compounds. The emissions had a very clear diurnal variation with afternoon maxima being on average 4 to 8 times higher than seasonal means for SQTs and OSQTs, respectively. β -Caryophyllene and β -farnesene were the main SQTs emitted. The main emitted OSQTs were tentatively identified as 14-hydroxy- β -caryophyllene acetate ( M=262 g mol −1 ) and 6-hydroxy- β -caryophyllene ( M=220 g mol −1 ). Over the whole growing season, the total MT emissions were only 24 % and 17 % of the total SQT and OSQT emissions, respectively. A stressed tree growing in a pot was also studied, and high emissions of α -farnesene and an unidentified SQT were detected together with high emissions of GLVs. Due to the relatively low volatility and the high reactivity of SQTs and OSQTs, downy birch emissions are expected to have strong impacts on atmospheric chemistry, especially on secondary organic aerosol (SOA) production.

Atmospheric aerosols play an important role in air pollution. Aerosol particle chemical composition is highly variable depending on the season, hour of the day, day of the week, meteorology, and location of the measurement site. Long measurement periods and highly time-resolved data are required in order to achieve a statistically relevant amount of data for assessing those variations and evaluating pollution episodes. In this study, we present continuous atmospheric PM1 (particulate matter < 1 µm) concentration and composition measurements at an urban street canyon site located in Helsinki, Finland. The study was performed for 4.5 years (2015–2019) and involved highly time-resolved measurements by taking advantage of a suite of online state-of-the-art instruments such as an aerosol chemical speciation monitor (ACSM), a multi-angle absorption photometer (MAAP), a differential mobility particle sizer (DMPS), and an Aethalometer (AE). PM1 consisted mostly of organics, with mean mass concentrations of 2.89 µg m−3 (53 % of PM1) followed by inorganic species (1.56 µg m−3, 29 %) and equivalent black carbon (eBC, 0.97 µg m−3, 18 %). A trend analysis revealed a decrease in BC from fossil fuel (BCFF), organics, and nitrate over the studied years. Clear seasonal and/or diurnal variations were found for the measured atmospheric PM1 constituents. Particle number and mass size distributions over different seasons revealed the possible influence of secondary organic aerosols (SOAs) during summer and the dominance of ultrafine traffic aerosols during winter. The seasonality of measured constituents also impacted the particle's coating and absorptive properties. The investigation of pollution episodes observed at the site showed that a large fraction of aerosol particle mass was comprised of inorganic species during long-range transport, while during local episodes eBC and organics prevailed together with elevated particle number concentration. Overall, the results increased knowledge of the variability of PM1 concentration and composition in a Nordic traffic site and its implications on urban air quality. Considering the effects of PM mitigation policies in northern Europe in the last decades, the results obtained in this study may be considered illustrative of probable future air quality challenges in countries currently adopting similar environmental regulations.

The biogenic volatile organic compound (BVOC) emission rates of Norway spruces published vary a lot. In this study we combined published Norway spruce emission rates measured in boreal forests (Meeningen et al., 2017; Bourtsoukidis et al., 2014a, 2014b; Hakola et al., (2003, 2019)) and added our new, unpublished emission data from southern (SF) and northern Finland (NF). Standardized summer monthly mean emission potentials of isoprene vary from below detection limit to 7 µg g-1(dw) h-1, and monoterpene (MT) and sesquiterpene (SQT) emission potentials 0.01–3 µg g-1(dw) h-1 and 0.03–2.7 µg g-1(dw) h-1, respectively. In this study, we found much higher SQT emissions from Norway spruces than measured before and on average SQTs had higher emission potentials than isoprene or MTs. The highest monthly mean SQT emission potential 13.6 µg g-1(dw) h-1 was observed in September in southern Finland. We found that none of the younger (33–40 years) trees in Hyytiälä, southern Finland, emitted isoprene, while one 50-year-old tree was a strong isoprene emitter. However, this could not be confirmed at other sites since all measured small trees were growing in Hyytiälä, so this could also be due to the same genetic origin. On average, older trees (>80 years) emitted about ten times more isoprene and MTs than younger ones ( As shown here for Norway spruce, it is possible that the emission factor of SQTs is significantly higher than what is currently used in models, which may have significant effects on the prediction of formation and growht of new particles, since the secondary organic aerosol (SOA) formation potential of SQTs is high and this may have significant effects on the formation and growth of new particles. Due to high secondary organic aerosol (SOA) formation potentials of SQTs the impact on SOA formation and mass could be even higher.

Norway Spruce dominates Europe’s Boreal and sub-Alpine coniferous forests and is widely planted in southeastern Canada, in the northeastern United States, in the Rocky Mountains, and in the Pacific Coast region. Therefore it is expected to have significant contribution to emissions of biogenic volatile organic compounds. There are studies on isoprene, monoterpene and sesquiterpene emissions of Norway spruce, but emission potentials and composition vary a lot. Especially the emission potentials of sesquiterpenes are highly varying. When considering the atmospheric impact of biogenic emissions, it is essential to know their composition since, for example, aerosol formation potential of different compounds varies a lot.In this study, we compiled all available data for Norway spruce emissions that could be used for emission potential calculations of terpenes and searched if growing location, season, or age of the tree could explain the variations. In addition to previously published data, we included new data from a boreal forest in southern Finland from 2019 and 2021 and a sub-Arctic boreal forest in northern Finland from 2020.The differences between various trees and sites were huge, but variations of emission potentials, especially for sesquiterpenes, over the growing season were also high, even for the same tree. Emission potentials of isoprene, monoterpenes and sesquiterpenes varied between 5 – 2500, 25 – 3200 and 42 – 15000 ng g(dw)-1 h-1, respectively. No clear trend could be observed on the basis of our measurements and other relevant studies. However, there were indication of some phenomena, e.g., none of the young trees emitted isoprene while some older trees had huge isoprene emissions. It is also noteworthy that sesquiterpene emission rates greatly increase at the end of a growing season.

Seasonal snow cover of the Northern Hemisphere (NH) is a major factor in the global climate system, which makes snow cover an important variable in climate models. Monitoring snow water equivalent (SWE) at continental scale is only possible from satellites, yet substantial uncertainties have been reported in NH SWE estimates. A recent bias-correction method significantly reduces the uncertainty of NH SWE estimation, which enables a more reliable analysis of the climate models' ability to describe the snow cover. We have intercompared the CMIP6 (Coupled Model Intercomparison Project Phase 6) and satellite-based NH SWE estimates north of 40° N for the period 1982–2014, and analyzed with a regression approach whether temperature (T) and precipitation (P) could explain the differences in SWE. We analyzed separately SWE in winter and SWE change rate in spring. The SnowCCI SWE data are based on satellite passive microwave radiometer data and in situ data. The analysis shows that CMIP6 models tend to overestimate SWE, however, large variability exists between models. In winter, P is the dominant factor causing SWE discrepancies especially in the northern and coastal regions. This is in line with the expectation that even too cold temperatures cannot cause too high SWE without precipitation. T contributes to SWE biases mainly in regions, where T is close to 0 °C in winter. In spring, the importance of T in explaining the snowmelt rate discrepancies increases. This is to be expected, because the increase in T is the main factor that causes snow to melt as spring progresses. Furthermore, it is obvious from the results that biases in T or P can not explain all model biases either in SWE in winter or in the snowmelt rate in spring. Other factors, such as deficiencies in model parameterizations and possibly biases in the observational datasets, also contribute to SWE discrepancies. In particular, linear regression suggests that when the biases in T and P are eliminated, the models generally overestimate the snowmelt rate in spring.

Solar energy production is growing at a very high rate. This growth is present also in Fennoscandia, where solar energy can answer to the renewable energy need during the long summer days. Solar energy is a naturally fluctuating energy source, one that requires the use of solar irradiance forecasts for optimal operation. In northern locations clouds cause the largest fluctuations of irradiance, which is why the forecasting of solar irradiance is often focused on forecasting the movement of clouds. At high latitudes there are also specific challenges due to the location, for instance estimating cloud location at the edge of the geostationary satellite instrument viewing area.Forecasting irradiance is possible using various methods. Methods using satellite imagery, for instance, are good at forecasting irradiance for a few hours forward, while Numerical Weather Prediction (NWP) methods are recognised as superior for forecasting the next day. Validations have been made for various methods and setups, at several different locations, and for example the satellite-imagery-based method Solis-Heliosat has been shown to work well also at high latitudes. Operational NWP models, however, are not often validated for this specific purpose.To understand the performance of operational local area NWP models in irradiance forecasting in Fennoscandia, we have compared and validated NWP irradiance forecasts at several measurement stations in Finland and Sweden. In the comparison, we have included the operational weather forecast model in the Nordic countries, the MetCoOp Ensemble Model (MEPS), as well as the MetCoOp Nowcasting Model (MNWC). Additionally, we have included the Solis-Heliosat method and a persistence method in the comparison.Initial results show MEPS to have a steady, small relative bias error during the forecast, particularly after the first hours. The relative Root Mean Square Error (rRMSE) is also steady with only a slight increase during the whole forecast. MNWC somewhat underpredicts irradiance in the beginning, but the errors improve throughout the forecast. Solis-Heliosat has good initial accuracy, but the quality deteriorates very fast, with MEPS and MNWC outperforming the method after a 1-3 hour lead time. The Persistence method, as expected, has a very good bias, but also a very fast increasing rRMSE with forecast lead time.The results show the NWP methods to be very suitable for forecasting irradiance. Differences in best performing lead times and varying run times between the methods increase our interest in using both NWP and satellite-based methods in forecasting irradiance, to get the most optimal accuracy for both short and long-term forecasting.

The parental magmas of massif-type anorthosites are suggested to originate from either the mantle or lower crust. If the source is the mantle, the magmas are presumed to have undergone crustal assimilation prior to plagioclase crystallization, which has produced melt compositions similar to anorthosite parental magmas (high-Al gabbros/basalts). If the source is the lower crust, the produced anorthosite parental melts are presumed to be monzodioritic (jotunitic) in composition. However, many studies have suggested that the monzodioritic rocks related to massif-type anorthosites rather represent residual melt compositions left after anorthosite fractionation. In this study, we have used the most recent thermodynamic modeling tools, Magma Chamber Simulator (MCS) and Rhyolite-MELTS to conduct partial melting, assimilation-fractional crystallization (AFC), and fractional crystallization (FC) models to address the unresolved questions about the source and compositional evolution of the anorthosite parental magmas.AFC models were conducted at high lower crustal pressures (1000 MPa) by using MCS. In the models, we used four different sublithospheric mantle partial melt compositions and 11 different assimilants with representative average lower crustal compositions compiled from literature. In addition, equilibrium partial melting of the same lower crustal compositions was modeled separately by using rhyolite-MELTS. The melt major element compositions produced by both modeling tools were compared to suggested natural anorthosite parental magma compositions. Finally, to further study the evolution of these melts after their generation, FC models were run at different crustal pressures (1000-100 MPa) by using MCS. These differentiated melt compositions were compared to a global array of monzodioritic rocks presumed to represent residual melts left after anorthosite fractionation.The preliminary modeling results point towards the mantle being a more suitable candidate for the source of the anorthosite parental magmas and that the parental magma compositions are better represented by high-Al gabbros than monzodioritic rocks: assimilation of mafic lower crustal material by mantle-derived magmas produces melts that are the most fitting analogues. Somewhat similar melts can also be produced by directly melting the lower crust, but this requires the crust to melt completely, which we consider improbable. The models further suggest fractional crystallization of high-Al gabbroic parental magmas produce residual melt evolution trends similar to the array of anorthosite-related monzodioritic rocks.

Even though isoprene and monoterpene (MT) emissions of boreal needle trees have been studied quite intensively, there is less knowledge on the emissions of broadleaved deciduous trees and emissions of larger terpenes and oxygenated volatile organic compounds (OVOCs). Here we studied the downy birch (Betula pubescens) leaf emissions of terpenes, OVOCs and green leaf volatiles (GLVs) at the SMEAR II boreal forest site using in situ gas chromatographs with mass spectrometers in 2017 and 2019. The highest emissions were detected during the early growing season, indicating that bud break and early leaf growth are a strong source of these compounds. Sesquiterpenes (SQTs) and oxygenated sesquiterpenes (OSQTs) were the main emitted compounds almost throughout the summer. Mean emissions (averaged over bud break/early/main and late growing season) of SQTs and OSQTs were 5–690 and 46–650 ng gdw−1 h−1, respectively. Isoprene emissions were very low or below detection limits (seasonal means dw−1 h−1), whereas variable levels of emissions of MTs, C5-C10 aldehydes and GLVs were detected. On average SQT and OSQT emissions were 5 and 6 times higher than MT emissions, but variation over the growing season was high and during the late growing season MTs were the main compound group emitted. Of the SQTs, β-caryophyllene and β-farnesene were the main compounds emitted in 2019, while in 2017 also high, possibly stress-induced emissions, of α-farnesene were detected. The main emitted OSQTs were tentatively identified as 14-hydroxy-β-caryophyllene acetate (M 262 g/mol) and 6-hydroxy-β-caryophyllene (M 220 g/mol). Of the MTs, α-pinene, β-pinene, limonene and sabinene were the most abundant compounds except during the last two days of the measurements in August in 2019, when β-ocimene emissions had the major contribution. In 2017, when the measured tree was suffering from leaf damages possibly due to drought and high chamber temperature, high emissions of GLVs, linalool, α-farnesene and an unidentified SQT were detected. Emission potentials calculated for 30 °C had very high variation between seasons and the highest potentials were detected during bud break and early growing season. Emission potentials calculated separately for each measurement day followed the leaf growth and were highest during the most rapid leaf growth. To our knowledge this is the first time when birch emission rates of OSQTs have been quantified. Even with low emissions, these compounds are expected to have strong impacts on the atmospheric chemistry and especially on secondary organic aerosol (SOA) production.

The uncertainty in the radiative forcing caused by aerosols and its effect on the climate change calls for research to improve knowledge of the aerosol particle formation and growth processes. While the experimental research has provided large amount of high quality data on aerosols in the last two decades, the inference of the process rates is still inadequate, mainly due to limitations in the analysis of data. This paper focuses on developing computational methods to infer aerosol process rates from size distribution measurements. In the proposed approach, the temporal evolution of aerosol size distributions is modeled with the general dynamic equation equipped with stochastic terms that account for the uncertainties of the process rates. The time-dependent particle size distribution and the rates of the underlying formation and growth processes are reconstructed based on time series of particle analyzer data using Bayesian state estimation – which not only provides (point) estimates for the process rates but also enables quantifying their uncertainties. The feasibility of the proposed computational framework is demonstrated by a set of numerical simulation studies.