How adequately are elevated moist layers represented in reanalysis and satellite observations?

Author(s):
Prange, Marc; Buehler, Stefan A.; Brath, Manfred
Publication title: Atmospheric Chemistry and Physics
2023
 | Volume: 23 | Issue: 1
2023
DOI: 10.5194/acp-23-725-2023
Abstract:
We assess the representation of elevated moist layers (EMLs) in ERA5 reanalysis, the Infrared Atmospheric Sounding Interferometer (IASI) L2 retrieval … We assess the representation of elevated moist layers (EMLs) in ERA5 reanalysis, the Infrared Atmospheric Sounding Interferometer (IASI) L2 retrieval Climate Data Record (CDR) and the Atmospheric Infrared Sounder (AIRS)-based Community Long-term Infrared Microwave Combined Atmospheric Product System (CLIMCAPS)-Aqua L2 retrieval. EMLs are free-tropospheric moisture anomalies that typically occur in the vicinity of deep convection in the tropics. EMLs significantly affect the spatial structure of radiative heating, which is considered a key driver for meso-scale dynamics, in particular convective aggregation. To our knowledge, the representation of EMLs in the mentioned data products has not been explicitly studied - a gap we start to address in this work. We assess the different datasets' capability of capturing EMLs by collocating them with 2146 radiosondes launched from Manus Island within the western Pacific warm pool, a region where EMLs occur particularly often. We identify and characterise moisture anomalies in the collocated datasets in terms of moisture anomaly strength, vertical thickness and altitude. By comparing the distributions of these characteristics, we deduce what specific EML characteristics the datasets are capturing well and what they are missing. Distributions of ERA5 moisture anomaly characteristics match those of the radiosonde dataset quite well, and remaining biases can be removed by applying a 1km moving average to the radiosonde profiles. We conclude that ERA5 is a suitable reference dataset for investigating EMLs. We find that the IASI L2 CDR is subject to stronger smoothing than ERA5, with moisture anomalies being on average 13% weaker and 28% thicker than collocated ERA5 anomalies. The CLIMCAPS L2 product shows significant biases in its mean vertical humidity structure compared to the other investigated datasets. These biases manifest as an underestimation of mean moist layer height of about 1.3km compared to the three other datasets that yields a general mid-tropospheric moist bias and an upper-tropospheric dry bias. Aside from these biases, the CLIMCAPS L2 product shows a similar, if not better, capability of capturing EMLs compared to the IASI L2 CDR. More nuanced evaluations of CLIMCAPS' capabilities may be possible once the underlying cause for the identified biases has been found and fixed. Biases found in the all-sky scenes do not change significantly when limiting the analysis to clear-sky scenes. We calculate radiatively driven vertical velocities ωrad derived from longwave heating rates to estimate the dynamical effect of the moist layers. Moist-layer-associated ωrad values derived from Global Climate Observing System Reference Upper-Air Network (GRUAN) soundings range between 2 and 3hPah-1, while mean meso-scale pressure velocities from the EUREC4A (Elucidating the Role of Clouds-Circulation Coupling in Climate) field campaign range between 1 and 2hPah-1, highlighting the dynamical significance of EMLs. Subtle differences in the representation of moisture and temperature structures in ERA5 and the satellite datasets create large relative errors in ωrad on the order of 40% to 80% with reference to GRUAN, indicating limited usefulness of these datasets to assess the dynamical impact of EMLs. © 2023 Marc Prange et al. more
Atmosphere Climate Data Records EUM-Secr Validation

How strong are the links between global warming and surface solar radiation changes?

Author(s):
Stamatis, M.; Hatzianastassiou, N.; Korras-Carraca, M.-B.; Matsoukas, C.; Wild, M.; Vardavas, I.
Publication title: Climatic Change
2024
 | Volume: 177 | Issue: 10
2024
DOI: 10.1007/s10584-024-03810-6
Abstract:
The aim of this study is to investigate the possible relationship between the recent global warming and the interdecadal changes in incoming surface s… The aim of this study is to investigate the possible relationship between the recent global warming and the interdecadal changes in incoming surface solar radiation (SSR), known as global dimming and brightening (GDB). The analysis is done on a monthly and annual basis on a global scale for the 35-year period 1984–2018 using surface temperature data from the European Centre for Medium-Range Weather Forecasts (ECMWF) v5 (ERA5) reanalysis and SSR fluxes from the FORTH (Foundation for Research and Technology-Hellas) radiative transfer model (RTM). Our analysis shows that on a monthly basis, SSR is correlated with temperature more strongly over global land than ocean areas. According to the RTM calculations, the SSR increased (inducing brightening) over most land areas during 1984–1999, while this increase leveled-off (causing dimming) in the 2000s and strengthened again in the 2010s. These SSR fluctuations are found to affect the global warming rates. Specifically, during the dimming phase in the 2000s, the warming rates across land areas with intense anthropogenic pollution, like Europe and East Asia, slowed down, while during the brightening phases, in the 1980s, 1990s and 2010s, the warming rates were reinforced. Although the magnitude of GDB and the Earth’s surface warming trends are not proportional, indicating that GDB is not the primary driver of the recent global warming, it seems that GDB can affect the warming rates, partly counterbalancing the dominant greenhouse warming during the dimming or accelerating the greenhouse-induced warming during the brightening phases of GDB. © The Author(s), under exclusive licence to Springer Nature B.V. 2024. more
Application Atmosphere CM-SAF Climate Data Records

IASI on Metop-A: Operational Level 2 retrievals after five years in orbit

Author(s):
August, Thomas; Klaes, Dieter; Schlüssel, Peter; Hultberg, Tim; Crapeau, Marc; Arriaga, Arlindo; O'Carroll, Anne; Coppens, Dorothée; Munro, Rose; Calbet, Xavier
Publication title: Journal of Quantitative Spectroscopy and Radiative Transfer
2012
 | Volume: 113 | Issue: 11
2012
DOI: 10.1016/j.jqsrt.2012.02.028
Abstract:
Geophysical parameters from the IASI instrument on Metop-A are essential products provided from EUMETSAT's Central Facility in near real time. They in… Geophysical parameters from the IASI instrument on Metop-A are essential products provided from EUMETSAT's Central Facility in near real time. They include vertical profiles of temperature and humidity, related cloud information, surface emissivity and temperature, and atmospheric composition parameters (CO, ozone and several other trace gases). As compared to previous operational processor versions, the latest processor version 5 delivers significant improvements in retrieval performance for most major products. These include improvements to cloud properties products, cloud detection (with a positive impact on the knowledge of the sea surface temperature, SST), the temperature profile (especially in the mid and upper troposphere), and ozone and carbon monoxide total columns. This paper provides a comprehensive summary of the processing algorithms, the latest scientific developments, and the related validation studies and activities. It concludes with a discussion of the future outlook. more
Climate Data Records EUM-Secr Method

Ice water path retrievals from Meteosat-9 using quantile regression neural networks

Author(s):
Amell, A.; Eriksson, P.; Pfreundschuh, S.
Publication title: Atmospheric Measurement Techniques
2022
 | Volume: 15 | Issue: 19
2022
DOI: 10.5194/amt-15-5701-2022
Abstract:
The relationship between geostationary radiances and ice water path (IWP) is complex, and traditional retrieval approaches are not optimal. This work … The relationship between geostationary radiances and ice water path (IWP) is complex, and traditional retrieval approaches are not optimal. This work applies machine learning to improve the IWP retrieval from Meteosat-9 observations, with a focus on low latitudes, training the models against retrievals based on CloudSat. Advantages of machine learning include avoiding explicit physical assumptions on the data, an efficient use of information from all channels, and easily leveraging spatial information. Thermal infrared (IR) retrievals are used as input to achieve a performance independent of the solar angle. They are compared with retrievals including solar reflectances as well as a subset of IR channels for compatibility with historical sensors. The retrievals are accomplished with quantile regression neural networks. This network type provides case-specific uncertainty estimates, compatible with non-Gaussian errors, and is flexible enough to be applied to different network architectures. Spatial information is incorporated into the network through a convolutional neural network (CNN) architecture. This choice outperforms architectures that only work pixelwise. In fact, the CNN shows a good retrieval performance by using only IR channels. This makes it possible to compute diurnal cycles, a problem that CloudSat cannot resolve due to its limited temporal and spatial sampling. These retrievals compare favourably with IWP retrievals in CLAAS, a dataset based on a traditional approach. These results highlight the possibilities to overcome limitations from physics-based approaches using machine learning while providing efficient, probabilistic IWP retrieval methods. Moreover, they suggest this first work can be extended to higher latitudes as well as that geostationary data can be considered as a complement to the upcoming Ice Cloud Imager mission, for example, to bridge the gap in temporal sampling with respect to space-based radars. Copyright © 2022 Adrià Amell et al. more
Atmosphere CM-SAF Climate Data Records Validation

How well can brightness temperature differences of spaceborne imagers help to detect cloud phase? A sensitivity analysis regarding cloud phase and related cloud properties

Author(s):
Mayer, J.; Mayer, B.; Bugliaro, L.; Meerkötter, R.; Voigt, C.
Publication title: Atmospheric Measurement Techniques
2024
 | Volume: 17 | Issue: 17
2024
DOI: 10.5194/amt-17-5161-2024
Abstract:
This study investigates the sensitivity of two brightness temperature differences (BTDs) in the infrared (IR) window of the Spinning Enhanced Visible … This study investigates the sensitivity of two brightness temperature differences (BTDs) in the infrared (IR) window of the Spinning Enhanced Visible and Infrared Imager (SEVIRI) to various cloud parameters in order to understand their information content, with a focus on cloud thermodynamic phase. To this end, this study presents radiative transfer calculations, providing an overview of the relative importance of all radiatively relevant cloud parameters, including thermodynamic phase, cloud-Top temperature (CTT), optical thickness (τ), effective radius (Reff), and ice crystal habit. By disentangling the roles of cloud absorption and scattering, we are able to explain the relationships of the BTDs to the cloud parameters through spectral differences in the cloud optical properties. In addition, an effect due to the nonlinear transformation from radiances to brightness temperatures contributes to the specific characteristics of the BTDs and their dependence on τ and CTT. We find that the dependence of the BTDs on phase is more complex than sometimes assumed. Although both BTDs are directly sensitive to phase, this sensitivity is comparatively small in contrast to other cloud parameters. Instead, the primary link between phase and the BTDs lies in their sensitivity to CTT (or more generally the surface-cloud temperature contrast), which is associated with phase. One consequence is that distinguishing high ice clouds from low liquid clouds is straightforward, but distinguishing mid-level ice clouds from mid-level liquid clouds is challenging. These findings help to better understand and improve the working principles of phase retrieval algorithms. © 2024 Johanna Mayer et al. more
Application Atmosphere CM-SAF Climate Data Records

Identification and intercomparison of surface-based inversions over Antarctica from IASI, ERA-Interim, and Concordiasi dropsonde data: Surface-Based Inversions Over Antarctica

Author(s):
Boylan, Patrick; Wang, Junhong; Cohn, Stephen A.; Hultberg, Tim; August, Thomas
Publication title: Journal of Geophysical Research: Atmospheres
2016
 | Volume: 121 | Issue: 15
2016
DOI: 10.1002/2015JD024724
Abstract:
Surface-based temperature inversions (SBIs) occur frequently over Antarctica and play an important role in climate and weather. Antarctic SBIs are exa… Surface-based temperature inversions (SBIs) occur frequently over Antarctica and play an important role in climate and weather. Antarctic SBIs are examined during the austral spring of 2010 using measurements from dropsondes, ERA-Interim Atmospheric Reanalysis Model, and the recently released version 6 of the Infrared Atmospheric Sounding Interferometer (IASI) level 2 product. A SBI detection algorithm is applied to temperature profiles from these data sets. The results will be used to determine if satellite and reanalysis products can accurately characterize SBIs, and if so, then they may be used to study SBIs outside of the spring 2010 study period. From the dropsonde data, SBIs occur in 20% of profiles over sea ice and 54% of profiles over land. IASI and ERA-Interim surface air temperatures are found to be significantly warmer than dropsonde observations at high plateau regions, while IASI surface air temperatures are colder over sea ice. IASI and ERA-Interim have a cold bias at nearly all levels above the surface when compared to the dropsonde. SBIs are characterized by their frequency, depth, and intensity. It is found that SBIs are more prevalent, deeper, and more intense over the continent than over sea ice, especially at higher surface elevations. Using IASI and ERA-Interim data the detection algorithm has a high probability of detection of SBIs but is found to severely overestimate the depth and underestimate the intensity for both data sets. These overestimation and underestimation are primarily due to the existence of extremely shallow inversion layers that neither satellite nor reanalysis products can resolve. more
Climate Data Records EUM-Secr Validation

Impact of AVHRR Channel 3b Noise on Climate Data Records: Filtering Method Applied to the CM SAF CLARA-A2 Data Record

Author(s):
Karlsson, Karl-Göran; Håkansson, Nina; Mittaz, Jonathan; Hanschmann, Timo; Devasthale, Abhay
Publication title: Remote Sensing
2017
 | Volume: 9 | Issue: 6
2017
DOI: 10.3390/rs9060568
Abstract:
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 meas… 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). more
Atmosphere CM-SAF Climate Data Records Method

Impact assessment of snow thickness, sea ice density and water density in CryoSat-2-derived sea ice thickness

Author(s):
Sievers, I.; Skourup, H.; Rasmussen, T.A.S.
Publication title: Cryosphere
2024
 | Volume: 18 | Issue: 12
2024
DOI: 10.5194/tc-18-5985-2024
Abstract:
Sea ice thickness is an essential climate variable, which is often derived from satellite altimetry freeboard estimates, e.g., by CryoSat-2. In order … Sea ice thickness is an essential climate variable, which is often derived from satellite altimetry freeboard estimates, e.g., by CryoSat-2. In order to convert freeboard to sea ice thickness, assumptions are needed for snow thickness, snow density, sea ice density and water density. These parameters are difficult to observe when co-located in time and space with the satellite-derived freeboard measurements. For this reason, most available CryoSat-2 sea ice thickness products rely on climatologies based on outdated observations and empirical values. Model- and observation-based alternatives to sea ice density and snow thickness values have been suggested in recent years, but their combined influence on the freeboard to sea ice thickness conversion has not been analyzed. This study evaluates model-based spatially varying snow thickness, sea ice density and water density with in situ observations and the associated parameters used in the classical CryoSat-2 sea ice thickness production. The observations used for the comparison are a snow thickness product from Ku- and Ka-band radar, sea ice density observations from airborne campaigns and ice core measurements as well as water density from a large variety of observation platforms included in the World Ocean Atlas. Furthermore, this study calculates the mean sea ice thickness differences resulting from substituting the parameters used in a classical CryoSat-2 sea ice thickness product with model-based values. The evaluation shows that the model-derived snow thickness, sea ice density and water density compare better to observations than the associated parameters used in the CryoSat-2 sea ice thickness product. The parameters were compared to the weekly CryoSat-2 sea ice thickness (SIT) product from the Alfred Wegener Institute, which uses similar values for snow thickness, sea ice density and water density to other available CryoSat-2 SIT products. Furthermore, we find that the model-based snow thickness and sea ice density separately lead to the largest sea ice thickness differences but that, to some extent, their differences cancel out when both parameters are used in combination. For the water density, we find the average and maximum sea ice thickness difference to be small in comparison to the sea ice thickness differences introduced by the snow thickness and sea ice density, but this is not negligible, as currently stated in most studies. We find that the origin of the assumption that water density is negligible in the freeboard to sea ice thickness conversion originates from a study investigating the seasonal Arctic sea ice density variability, not taking into account the spacial variability. Based on our findings, we recommend using either a water density climatology or an uncertainty value of 2.6 kg m-3 instead of the commonly used value of 0 to 0.5 kg m-3 in CryoSat-2 freeboard to sea ice thickness conversion. © 2024 Imke Sievers et al. more
Application Climate Data Records Cryosphere OSI-SAF

Impact of climate change on the behaviour of solar radiation using AFR-CORDEX model over West Africa

Author(s):
Ojo, O. S.; Emmanuel, I.; Adedayo, K. D.; Ogolo, E. O.; Adeyemi, B.
Publication title: Meteorology and Atmospheric Physics
2024
 | Volume: 136 | Issue: 5
2024
DOI: 10.1007/s00703-024-01033-z
Abstract:
The study evaluated the impact of climate change on incoming solar radiation (RSDS) in West Africa by comparing observed data from the CMSAF solar pro… The study evaluated the impact of climate change on incoming solar radiation (RSDS) in West Africa by comparing observed data from the CMSAF solar products (SARAH and CLARA-A1) for the period 1983–2019 with simulated data from the AFR-CORDEX models (RegCM-4.7 and CCCma-canRCM4) for the historical period (1983–2004) and various RCP emission scenarios (2.6, 4.5, 8.5) for 2005–2099. The values of the RCP in parentheses signify the level of increasing radiative forcings due to varying emission controls. Assessment metrics like correlation coefficient (R), Taylor Skill Score (TSS), and root mean square errors (RMSE) were employed for comparative analysis on annual and seasonal timescales. The analyses revealed annual mean RSDS intensities of 256.22 for SARAH, 238.53 for CLARA-A1, 270.81 for Historical, 270.26 for RCP 2.6, 255.90 for RCP 4.5, and 271.93 for the RCP 8.5 scenarios in watts per square metres. The TSS analyses showed average agreement values between observed CMSAF and simulated AFR-CORDEX solar radiation with values of 0.8450 and 0.8575 with historical, 0.8750 and 0.8600 with RCP 2.6, 0.9025 and 0.8550 with RCP 4.5, and 0.8675 and 0.8525 with RCP 8.5 scenarios for SARAH and CLARA-A1 respectively. All the metrics showed better agreement with SARAH than CLARA-A1, likely due to the associated cloud influence on CLARA-A1. Notably, the CORDEX-CCCma-canRCM4 model under RCP 4.5 demonstrated the highest accuracy, with an average correlation of 0.82 and a mean TSS of 0.90 against the SARAH reference dataset. The results suggest the AFR-CORDEX model, particularly the CCCma-canRCM4 for RCP 4.5 scenario, could reliably predict solar radiation and inform climate change impacts on solar energy potential in West Africa under moderate emission conditions. more
Atmosphere CM-SAF Climate Data Records Validation
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