Increased concentrations of aerosol can enhance the albedo of warm low-level cloud. Accurately quantifying this relationship from space is challenging due in part to contamination of aerosol ...statistics near clouds. Aerosol retrievals near clouds can be influenced by stray cloud particles in areas assumed to be cloud-free, particle swelling by humidification, shadows and enhanced scattering into the aerosol field from (3-D radiative transfer) clouds. To screen for this contamination we have developed a new cloud–aerosol pairing algorithm (CAPA) to link cloud observations to the nearest aerosol retrieval within the satellite image. The distance between each aerosol retrieval and nearest cloud is also computed in CAPA. Results from two independent satellite imagers, the Advanced Along-Track Scanning Radiometer (AATSR) and Moderate Resolution Imaging Spectroradiometer (MODIS), show a marked reduction in the strength of the intrinsic aerosol indirect radiative forcing when selecting aerosol pairs that are located farther away from the clouds (−0.28±0.26 W m−2) compared to those including pairs that are within 15 km of the nearest cloud (−0.49±0.18 W m−2). The larger aerosol optical depths in closer proximity to cloud artificially enhance the relationship between aerosol-loading, cloud albedo, and cloud fraction. These results suggest that previous satellite-based radiative forcing estimates represented in key climate reports may be exaggerated due to the inclusion of retrieval artefacts in the aerosol located near clouds.
Abstract
Estimates of the anthropogenic effective radiative forcing (ERF) trend have increased by 50% since 2000 (from +0.4 W m
−2
decade
−1
in 2000–09 to +0.6 W m
−2
decade
−1
in 2010–19), the ...majority of which is driven by changes in the aerosol ERF trend, as a result of aerosol emissions reductions. Here we study the extent to which observations of the climate system agree with these ERF assumptions. We use a large ERF ensemble from the IPCC’s Sixth Assessment Report (AR6) to attribute the anthropogenic contributions to global mean surface temperature (GMST), top-of-atmosphere radiative flux, and we use aerosol optical depth observations. The GMST trend has increased from +0.18°C decade
−1
in 2000–09 to +0.35°C decade
−1
in 2010–19, coinciding with the anthropogenic warming trend rising from +0.19°C decade
−1
in 2000–09 to +0.24°C decade
−1
in 2010–19. This, as well as observed trends in top-of-atmosphere radiative fluxes and aerosol optical depths, supports the claim of an aerosol-induced temporary acceleration in the rate of warming. However, all three observation datasets additionally suggest that smaller aerosol ERF trend changes are compatible with observations since 2000, since radiative flux and GMST trends are significantly influenced by internal variability over this period. A
zero-trend-change
aerosol ERF scenario results in a much smaller anthropogenic warming acceleration since 2000 but is poorly represented in AR6’s ERF ensemble. Short-term ERF trends are difficult to verify using observations, so caution is required in predictions or policy judgments that depend on them, such as estimates of current anthropogenic warming trend, and the time remaining to, or the outstanding carbon budget consistent with, 1.5°C warming. Further systematic research focused on quantifying trends and early identification of acceleration or deceleration is required.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Atmospheric aerosols and their impact on cloud properties remain the largest uncertainty in the human forcing of the climate system. By increasing the concentration of cloud droplets (Nd), aerosols ...reduce droplet size and increase the reflectivity of clouds (a negative radiative forcing). Central to this climate impact is the susceptibility of cloud droplet number to aerosol (β), the diversity of which explains much of the variation in the radiative forcing from aerosol–cloud interactions (RFaci) in global climate models. This has made measuring β a key target for developing observational constraints of the aerosol forcing.
Recent years have seen the increasing inclusion of per-retrieval prognostic (predictive) uncertainty estimates within satellite aerosol optical depth (AOD) data sets, providing users with ...quantitative tools to assist in the optimal use of these data. Prognostic estimates contrast with diagnostic (i.e. relative to some external truth) ones, which are typically obtained using sensitivity and/or validation analyses. Up to now, however, the quality of these uncertainty estimates has not been routinely assessed. This study presents a review of existing prognostic and diagnostic approaches for quantifying uncertainty in satellite AOD retrievals, and it presents a general framework to evaluate them based on the expected statistical properties of ensembles of estimated uncertainties and actual retrieval errors. It is hoped that this framework will be adopted as a complement to existing AOD validation exercises; it is not restricted to AOD and can in principle be applied to other quantities for which a reference validation data set is available. This framework is then applied to assess the uncertainties provided by several satellite data sets (seven over land, five over water), which draw on methods from the empirical to sensitivity analyses to formal error propagation, at 12 Aerosol Robotic Network (AERONET) sites. The AERONET sites are divided into those for which it is expected that the techniques will perform well and those for which some complexity about the site may provide a more severe test. Overall, all techniques show some skill in that larger estimated uncertainties are generally associated with larger observed errors, although they are sometimes poorly calibrated (i.e. too small or too large in magnitude). No technique uniformly performs best. For powerful formal uncertainty propagation approaches such as optimal estimation, the results illustrate some of the difficulties in appropriate population of the covariance matrices required by the technique. When the data sets are confronted by a situation strongly counter to the retrieval forward model (e.g. potentially mixed land–water surfaces or aerosol optical properties outside the family of assumptions), some algorithms fail to provide a retrieval, while others do but with a quantitatively unreliable uncertainty estimate. The discussion suggests paths forward for the refinement of these techniques.
The most widely used satellite products are averages of data onto a regular spatiotemporal grid, known as Level 3 data. Some atmospheric variables can vary rapidly in response to changing conditions. ...Over the scales of Level 3 averaging, the combination of observations across different conditions may result in data that is not normally distributed, such that a simple mean is not representative. The problem is illustrated by the distribution of aerosol optical depth from different sensors and algorithms. A simple statistical technique is proposed to better convey the diversity of satellite observations to users whereby a multimodal log-normal distribution is fit to the distribution of data observed within each grid cell. Allowing multiple modes within each cell is shown to improve the agreement between satellite products by highlighting regions of significant variability and isolating systematic differences between instruments.
Uncertainty-bounded satellite retrievals of volcanic ash cloud properties such as ash cloud-top height, effective radius, optical depth and mass loading are needed for the robust quantitative ...assessment required to warn aviation of potential hazards. Moreover, there is an imperative to improve quantitative ash cloud estimation due to the planned move towards quantitative ash concentration forecasts by the Volcanic Ash Advisory Centers. Here we apply the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm to Advanced Himawari Imager (AHI) measurements of the ash clouds produced by the June 2019 Raikoke (Russia) eruption. The ORAC algorithm uses an optimal estimation technique to consolidate a priori information, satellite measurements and associated uncertainties into uncertainty-bounded estimates of the desired state variables. Using ORAC, we demonstrate several improvements in thermal infrared volcanic ash retrievals applied to broadband imagers. These include an improved treatment of measurement noise, accounting for multi-layer cloud scenarios, distinguishing between heights in the troposphere and stratosphere, and the retrieval of a wider range of effective radii sizes than existing techniques by exploiting information from the 10.4 µm channel. Our results indicate that 0.73 ± 0.40 Tg of very fine ash (radius ≤ 15 µm) was injected into the atmosphere during the main eruptive period from 21 June 18:00 UTC to 22 June 10:00 UTC. The total mass of very fine ash decreased from 0.73 to 0.10 Tg over ∼ 48 h, with an e-folding time of 20 h. We estimate a distal fine ash mass fraction of 0.73 % ± 0.62 % based on the total mass of very fine ash retrieved and the ORAC-derived height–time series. Several distinct ash layers were revealed by the ORAC height retrievals. Generally, ash in the troposphere was composed of larger particles than ash present in the stratosphere. We also find that median ash cloud concentrations fall below peak ash concentration safety limits (< 4 mg m−3) 11–16 h after the eruption begins, if typical ash cloud geometric thicknesses are assumed. The ORAC height retrievals for the near-source plume showed good agreement with GOES-17 side-view height data (R=0.84; bias = −0.75 km); however, a larger negative bias was found when comparing ORAC height retrievals for distal ash clouds against Cloud-Aerosol Lidar with Orthogonal Polarisation (CALIOP) measurements (R=0.67; bias = −2.67 km). The dataset generated here provides uncertainties at the pixel level for all retrieved variables and could potentially be used for dispersion model validation or be implemented in data assimilation schemes. Future work should focus on improving ash detection, improving height estimation in the stratosphere and exploring the added benefit of visible channels for retrieving effective radius and optical depth in opaque regions of nascent ash plumes.
The question of how to derive and present uncertainty information in climate data records (CDRs) has received sustained attention within the European Space Agency Climate Change Initiative (CCI), a ...programme to generate CDRs addressing a range of essential climate variables (ECVs) from satellite data. Here, we review the nature, mathematics, practicalities, and communication of uncertainty information in CDRs from Earth observations. This review paper argues that CDRs derived from satellite-based Earth observation (EO) should include rigorous uncertainty information to support the application of the data in contexts such as policy, climate modelling, and numerical weather prediction reanalysis. Uncertainty, error, and quality are distinct concepts, and the case is made that CDR products should follow international metrological norms for presenting quantified uncertainty. As a baseline for good practice, total standard uncertainty should be quantified per datum in a CDR, meaning that uncertainty estimates should clearly discriminate more and less certain data. In this case, flags for data quality should not duplicate uncertainty information, but instead describe complementary information (such as the confidence in the uncertainty estimate provided or indicators of conditions violating the retrieval assumptions). The paper discusses the many sources of error in CDRs, noting that different errors may be correlated across a wide range of timescales and space scales. Error effects that contribute negligibly to the total uncertainty in a single-satellite measurement can be the dominant sources of uncertainty in a CDR on the large space scales and long timescales that are highly relevant for some climate applications. For this reason, identifying and characterizing the relevant sources of uncertainty for CDRs is particularly challenging. The characterization of uncertainty caused by a given error effect involves assessing the magnitude of the effect, the shape of the error distribution, and the propagation of the uncertainty to the geophysical variable in the CDR accounting for its error correlation properties. Uncertainty estimates can and should be validated as part of CDR validation when possible. These principles are quite general, but the approach to providing uncertainty information appropriate to different ECVs is varied, as confirmed by a brief review across different ECVs in the CCI. User requirements for uncertainty information can conflict with each other, and a variety of solutions and compromises are possible. The concept of an ensemble CDR as a simple means of communicating rigorous uncertainty information to users is discussed. Our review concludes by providing eight concrete recommendations for good practice in providing and communicating uncertainty in EO-based climate data records.
Producing a global and comprehensive description of atmospheric aerosols requires integration of ground-based, airborne, satellite and model datasets. Due to its complexity, aerosol monitoring ...requires the use of several data records with complementary information content. This paper describes the lessons learned while developing and qualifying algorithms to generate aerosol Climate Data Records (CDR) within the European Space Agency (ESA) Aerosol_cci project. An iterative algorithm development and evaluation cycle involving core users is applied. It begins with the application-specific refinement of user requirements, leading to algorithm development, dataset processing and independent validation followed by user evaluation. This cycle is demonstrated for a CDR of total Aerosol Optical Depth (AOD) from two subsequent dual-view radiometers. Specific aspects of its applicability to other aerosol algorithms are illustrated with four complementary aerosol datasets. An important element in the development of aerosol CDRs is the inclusion of several algorithms evaluating the same data to benefit from various solutions to the ill-determined retrieval problem. The iterative approach has produced a 17-year AOD CDR, a 10-year stratospheric extinction profile CDR and a 35-year Absorbing Aerosol Index record. Further evolution cycles have been initiated for complementary datasets to provide insight into aerosol properties (i.e., dust aerosol, aerosol absorption).
Extreme biomass burning (BB) events, such as those seen during the 2019–2020 Australian bushfire season, are becoming more frequent and intense with climate change. Ground-based observations of these ...events can provide useful information on the macro- and micro-physical properties of the plumes, but these observations are sparse, especially in regions which are at risk of intense bushfire events. Satellite observations of extreme BB events provide a unique perspective, with the newest generation of geostationary imagers, such as the Advanced Himawari Imager (AHI), observing entire continents at moderate spatial and high temporal resolution. However, current passive satellite retrieval methods struggle to capture the high values of aerosol optical thickness (AOT) seen during these BB events. Accurate retrievals are necessary for global and regional studies of shortwave radiation, air quality modelling and numerical weather prediction. To address these issues, the Optimal Retrieval of Aerosol and Cloud (ORAC) algorithm has used AHI data to measure extreme BB plumes from the 2019–2020 Australian bushfire season. The sensitivity of the retrieval to the assumed optical properties of BB plumes is explored by comparing retrieved AOT with AErosol RObotic NETwork (AERONET) level-1.5 data over the AERONET site at Tumbarumba, New South Wales, between 1 December 2019 at 00:00 UTC and 3 January 2020 at 00:00 UTC. The study shows that for AOT values > 2, the sensitivity to the assumed optical properties is substantial. The ORAC retrievals and AERONET data are compared against the Japan Aerospace Exploration Agency (JAXA) Aerosol Retrieval Product (ARP), Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue over land, MODIS MAIAC, Sentinel-3 SYN and VIIRS Deep Blue products. The comparison shows the ORAC retrieval significantly improves coverage of optically thick plumes relative to the JAXA ARP, with approximately twice as many pixels retrieved and peak retrieved AOT values 1.4 times higher than the JAXA ARP. The ORAC retrievals have accuracy scores of 0.742–0.744 compared to the values of 0.718–0.833 for the polar-orbiting satellite products, despite successfully retrieving approximately 28 times as many pixels over the study period as the most successful polar-orbiting satellite product. The AHI and MODIS satellite products are compared for three case studies covering a range of BB plumes over Australia. The results show good agreement between all products for plumes with AOT values ≤ 2. For extreme BB plumes, the ORAC retrieval finds values of AOT > 15, significantly higher than those seen in events classified as extreme by previous studies, although with high uncertainty. A combination of hard limits in the retrieval algorithms and misclassification of BB plumes as cloud prevents the JAXA and MODIS products from returning AOT values significantly greater than 5.
New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include ...components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude–longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.For each dataset a digital object identifier has been issued:Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002
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