Impact of AVHRR channel 3b noise on climate data records: filtering method applied to the CM SAF CLARA-A2 data record

A method for reducing the impact of noise in the 3.7 micron spectral channel in climate data records derived from coarse resolution (4 km) global measurements from the Advanced Very High Resolution Radiometer (AVHRR) data is presented. A dynamic size-varying median filter is applied to measurements guided by measured noise levels and scene temperatures for individual AVHRR sensors on historic National Oceanic and Atmospheric Administration (NOAA) polar orbiting satellites in the period 1982–2001. The method was used in the preparation of the CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data—Second Edition (CLARA-A2), a cloud climate data record produced by the EUMETSAT Satellite Application Facility for Climate Monitoring (CM SAF), as well as in the preparation of the corresponding AVHRR-based datasets produced by the European Space Agency (ESA) Climate Change Initiative (CCI) project ESA-CLOUD-CCI. The impact of the noise filter was equivalent to removing an artificial decreasing trend in global cloud cover of 1–2% per decade in the studied period, mainly explained by the very high noise levels experienced in data from the first satellites in the series (NOAA-7 and NOAA-9). View Full-Tex

Aerosol indirect effect in the thermal spectral range as seen from satellites

Insufficient knowledge about aerosol-cloud interactions has caused uncertainty in the Earth Radiation Budget. Lack of information about aerosol type, composition and concentration on global and regional scales also has restrained numerous efforts that have been made in the past to quantify the modulation of cloud properties by aerosols. Clouds, which cover more than half of the earth at any given time, have a key role in radiation budget. Most of the work has been done so far to understand modulation of cloud microphysical properties (invisible spectrum) by aerosols neglecting the thermal part. Satellites play unique role in improving knowledge about aerosol-cloud interactions through their ability to quantify spectral signatures of clouds and uniform, continuous sampling of the earth. Using long-term satellite data evaluations, this study reveals an entirely new aspect of these interactions and suggests that there exists indirect aerosol effect in the thermal spectrum. It suggests that anthropogenic aerosols, finer particles in particular, and cloud top temperature covary. This thermal effect could be equally important and hence cannot be neglected in radiation budget studies. First evidence of the impact of ship emissions on cloud properties over coastal waters is also presented

Impact of ship emissions on cloud properties over coastal areas

Although land based emissions in Europe are decreasing, ship emissions continue to grow. The main emissions from ships can modulate cloud properties of coastal areas and are of direct relevance to the earth radiation budget. In this context, satellite data from AVHRR onboard NOAA-14 are evaluated for six years (1997–02) in order to assess impact of ship emissions on cloud properties over coastal areas. Study area was chosen in such a way that it includes the English Channel and top three polluting harbours in Europe. Results present first evidence of possible impact of ship emissions on both cloud albedo and cloud top temperature over coastal areas using long-term satellite measurements. Increase in cloud albedo (with corresponding decrease in cloud top temperature) and higher variability are observed over coastal areas. This effect is more pronounced for areas over and around harbours and the English Channel. It also confirms indirect aerosol effects

Comparison of aerosol optical properties above clouds between POLDER and AeroCom models over the South East Atlantic Ocean during the fire season

Aerosol properties above clouds have been retrieved over the South East Atlantic Ocean during the fire season 2006 using satellite observations from POLDER (Polarization and Directionality of Earth Reflectances). From June to October, POLDER has observed a mean Above-Cloud Aerosol Optical Thickness (ACAOT) of 0.28 and a mean Above-Clouds Single Scattering Albedo (ACSSA) of 0.87 at 550 nm. These results have been used to evaluate the simulation of aerosols above clouds in 5 AeroCom (Aerosol Comparisons between Observations and Models) models (GOCART, HadGEM3, ECHAM5-HAM2, OsloCTM2 and SPRINTARS). Most models do not reproduce the observed large aerosol load episodes. The comparison highlights the importance of the injection height and the vertical transport parameterizations to simulate the large ACAOT observed by POLDER. Furthermore, POLDER ACSSA is best reproduced by models with a high imaginary part of black carbon refractive index, in accordance with recent recommendations

Warm‐air advection, air mass transformation and fog causes rapid ice melt

Direct observations during intense warm-air advection over the East Siberian Sea reveal a period of rapid sea-ice melt. A semi-stationary, high-pressure system north of the Bering Strait forced northward advection of warm, moist air from the continent. Air-mass transfor-mation over melting sea ice formed a strong, surface-based temperature inversion in which dense fog formed. This induced a positive net longwave radiation at the surface, while reduc-ing net solar radiation only marginally; the inversion also resulted in downward turbulent heat flux. The sum of these processes enhanced the surface energy flux by an average of ~15 W m-2 for a week. Satellite images before and after the episode show sea-ice concentrations decreasing from > 90% to ~50% over a large area affected by the air-mass transformation. We argue that this rapid melt was triggered by the increased heat flux from the atmosphere due to the warm-air advection

Atmospheric conditions during the Arctic Clouds in Summer Experiment (ACSE): Contrasting open-water and sea-ice surfaces during melt and freeze-up seasons

The Arctic Clouds in Summer Experiment (ACSE) was conducted during summer and early autumn 2014, providing a detailed view of the seasonal transition from ice melt into freeze-up. Measurements were taken over both ice-free and ice-covered surfaces near the ice edge, offering insight into the role of the surface state in shaping the atmospheric conditions. The initiation of the autumn freeze-up was related to a change in air mass, rather than to changes in solar radiation alone; the lower atmosphere cooled abruptly, leading to a surface heat loss. During melt season, strong surface inversions persisted over the ice, while elevated inversions were more frequent over open water. These differences disappeared during autumn freeze-up, when elevated inversions persisted over both ice-free and ice-covered conditions. These results are in contrast to previous studies that found a well-mixed boundary layer persisting in summer and an increased frequency of surface-based inversions in autumn, suggesting that knowledge derived from measurements taken within the pan-Arctic area and on the central ice pack does not necessarily apply closer to the ice edge. This study offers an insight into the atmospheric processes that occur during a crucial period of the year; understanding and accurately modeling these processes is essential for the improvement of ice-extent predictions and future Arctic climate projections

The application of molecular modelling in the safety assessment of chemicals: A case study on ligand-dependent PPARγ dysregulation.

The aim of this paper was to provide a proof of concept demonstrating that molecular modelling methodologies can be employed as a part of an integrated strategy to support toxicity prediction consistent with the mode of action/adverse outcome pathway (MoA/AOP) framework. To illustrate the role of molecular modelling in predictive toxicology, a case study was undertaken in which molecular modelling methodologies were employed to predict the activation of the peroxisome proliferator-activated nuclear receptor γ (PPARγ) as a potential molecular initiating event (MIE) for liver steatosis. A stepwise procedure combining different in silico approaches (virtual screening based on docking and pharmacophore filtering, and molecular field analysis) was developed to screen for PPARγ full agonists and to predict their transactivation activity (EC50). The performance metrics of the classification model to predict PPARγ full agonists were balanced accuracy=81%, sensitivity=85% and specificity=76%. The 3D QSAR model developed to predict EC50 of PPARγ full agonists had the following statistical parameters: q(2)cv=0.610, Nopt=7, SEPcv=0.505, r(2)pr=0.552. To support the linkage of PPARγ agonism predictions to prosteatotic potential, molecular modelling was combined with independently performed mechanistic mining of available in vivo toxicity data followed by ToxPrint chemotypes analysis. The approaches investigated demonstrated a potential to predict the MIE, to facilitate the process of MoA/AOP elaboration, to increase the scientific confidence in AOP, and to become a basis for 3D chemotype development

Correcting orbital drift signal in the time series of AVHRR derived convective cloud fraction using rotated empirical orthogonal function: Correcting orbital drift signal in the time series of AVHRR derivedconvective cloud fraction using rotated empirical orthogonal function

The Advanced Very High Resolution Radiometer (AVHRR) instruments onboard the series of National Oceanic and Atmospheric Administration (NOAA) satellites offer the longest available meteorological data records from space. These satellites have drifted in orbit resulting in shifts in the local time sampling during the life span of the sensors onboard. Depending upon the amplitude of the diurnal cycle of the geophysical parameters derived, orbital drift may cause spurious trends in their time series. We investigate tropical deep convective clouds, which show pronounced diurnal cycle amplitude, to estimate an upper bound of the impact of orbital drift on their time series. We carry out a rotated empirical orthogonal function analysis (REOF) and show that the REOFs are useful in delineating orbital drift signal and, more importantly, in subtracting this signal in the time series of convective cloud amount. These results will help facilitate the derivation of homogenized data series of cloud amount from NOAA satellite sensors and ultimately analyzing trends from them. However, we suggest detailed comparison of various methods and rigorous testing thereof applying final orbital drift corrections

Leveraging the satellite-based climate data record CLARA-A3 to understand the climatic trend regimes relevant for solar energy applications over Europe

Efficient transitioning to renewable energy requires a fundamental understanding of the past and future climate change. This is particularly true in the case of solar energy, since the surface incoming solar radiation (SIS) is heavily regulated by atmospheric essential climate variables (ECVs) such as aerosols and clouds. Given the complexity of the interactions and feedbacks in the Earth system, even small changes in ECVs could have large direct and indirect effects on SIS. The net efficacy of the solar energy systems designed therefore depends on how well we account for the role of ECVs in modulating SIS. In this study, by leveraging the satellite-based climate data record (CDR) CLARA-A3, we investigate the recent trends in SIS and cloud properties over Europe during the 1982–2020 period. Furthermore, we derive emerging climatic trend regimes that are relevant for solar energy applications. Results show a large-scale increase in SIS in spring and early summer over Europe, particularly noticeable in April and June. The corresponding trends in cloud fraction and cloud optical thickness and their correlation with SIS suggest an increasingly important role of clouds in defining the favourable and unfavourable conditions for solar energy applications. We also note a strong spatiotemporal variability in trends and correlations. The results provide valuable metrics for the evaluation of climate models that have a dynamically integrated solar energy component.</p

Stratospheric aerosol - Observations, processes, and impact on climate

Interest in stratospheric aerosol and its role in climate have increased over the last decade due to the observed increase in stratospheric aerosol since 2000 and the potential for changes in the sulfur cycle induced by climate change. This review provides an overview about the advances in stratospheric aerosol research since the last comprehensive assessment of stratospheric aerosol was published in 2006. A crucial development since 2006 is the substantial improvement in the agreement between in situ and space-based inferences of stratospheric aerosol properties during volcanically quiescent periods. Furthermore, new measurement systems and techniques, both in situ and space based, have been developed for measuring physical aerosol properties with greater accuracy and for characterizing aerosol composition. However, these changes induce challenges to constructing a long-term stratospheric aerosol climatology. Currently, changes in stratospheric aerosol levels less than 20% cannot be confidently quantified. The volcanic signals tend to mask any nonvolcanically driven change, making them difficult to understand. While the role of carbonyl sulfide as a substantial and relatively constant source of stratospheric sulfur has been confirmed by new observations and model simulations, large uncertainties remain with respect to the contribution from anthropogenic sulfur dioxide emissions. New evidence has been provided that stratospheric aerosol can also contain small amounts of nonsulfate matter such as black carbon and organics. Chemistry-climate models have substantially increased in quantity and sophistication. In many models the implementation of stratospheric aerosol processes is coupled to radiation and/or stratospheric chemistry modules to account for relevant feedback processes