A Sea Surface Temperature data record (2004–2012) from Meteosat Second Generation satellites

Author(s):
Saux Picart, Stéphane; Marsouin, Anne; Legendre, Gérard; Roquet, Hervé; Péré, Sonia; Nano-Ascione, Nolwenn; Gianelli, Thibauld
Publication title: Remote Sensing of Environment
2020
 | Volume: 240
2020
DOI: 10.1016/j.rse.2020.111687
Abstract:
The Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) of the EUropean Organisation for the Exploitation of METeorological Satellites (EUMETSA… The Ocean and Sea-Ice Satellite Application Facility (OSI-SAF) of the EUropean Organisation for the Exploitation of METeorological Satellites (EUMETSAT) has performed a reprocessing of Sea Surface Temperature (SST) from the Spinning Enhanced Visible and Infrared Imager (SEVIRI) onboard Meteosat Second Generation (MSG) archive (2004–2012). The retrieval method consists of a non-linear split-window algorithm and an algorithm correction relying on simulations of infrared brightness temperatures performed using atmospheric profiles of water vapour and temperature from a Numerical Weather Prediction model, and a radiative transfer model. The cloud mask used is the Climate SAF reprocessing of the MSG/SEVIRI archive which is consistent over the period considered. Atmospheric Saharan dust has a strong impact on the retrieved SST in the Atlantic and Mediterranean regions, they are taken into consideration through the computation of the Saharan Dust Index (SDI) which is then used to determine an empirical correction applied to SST. The reprocessing has benefited from the experience of the OSI SAF team in operational near real time processing of MSG/SEVIRI data, and the methods have been improved to provide a higher quality SST. The MSG/SEVIRI SST reprocessing dataset consists of hourly level 3 composites of sub-skin temperature projected onto a regular 0.05° grid over the region delimited by 60N,60S and 60W,60E. It has been thoroughly validated against drifting buoys and moored buoys measurements. Results of this validation have shown that the reprocessed data record is of significantly better quality than the OSI SAF operational processing (for instance the day-time robust standard deviation is 0.45 K for the operational processing and 0.35 K for the reprocessed dataset). The data record has been used to characterize the diurnal variability of SST over large temporal and spatial scales. more
Climate Data Records Method OSI-SAF Ocean Validation

A statistically significant increase in ice supersaturation in the atmosphere in the past 40 years

Author(s):
Benetatos, C.; Eleftheratos, K.; Gierens, K.; Zerefos, C.
Publication title: Scientific Reports
2024
 | Volume: 14 | Issue: 1
2024
DOI: 10.1038/s41598-024-75756-9
Abstract:
Ice saturation (and supersaturation) is a frequent phenomenon in cold regions of the upper troposphere. Its existence is essential for the formation o… Ice saturation (and supersaturation) is a frequent phenomenon in cold regions of the upper troposphere. Its existence is essential for the formation of ice clouds and a necessary condition for the persistence of contrails. Its spatial and temporal evolution is important for weather and climate. The ice saturation and supersaturation values are found in the upper tail of the probability density function (pdf) of upper tropospheric humidity with respect to ice (UTHi). Here, we analyse the changes in the frequency of occurrence of ice saturation and supersaturation from 1979 to 2020 and compare them to changes in the mean UTHi. Our results show that while the mean UTHi increases near-globally with a rate of about 0.15% per decade, high UTHi values exceeding the 70%, 80%, 90% and 100% thresholds increase faster than the mean, at rates of about 0.7%, 0.6%, 0.4% and 0.3% per decade, respectively. The increasing rates of values found in the upper tail of the UTHi pdf suggest that the ambient conditions for cirrus and contrail formation and persistence will be more favourable in the future and this is expected to further enhance the impact of aviation on climate. © The Author(s) 2024. more
Application Atmosphere CM-SAF Climate Data Records

A three-dimensional simulation and process analysis of tropospheric ozone depletion events (ODEs) during the springtime in the Arctic using CMAQ (Community Multiscale Air Quality Modeling System)

Author(s):
Cao, L.; Li, S.; Gu, Y.; Luo, Y.
Publication title: Atmospheric Chemistry and Physics
2023
 | Volume: 23 | Issue: 5
2023
DOI: 10.5194/acp-23-3363-2023
Abstract:
The tropospheric ozone depletion event (ODE), first observed at Barrow (now known as Utqiagvik), Alaska, is a phenomenon that frequently occurs during… The tropospheric ozone depletion event (ODE), first observed at Barrow (now known as Utqiagvik), Alaska, is a phenomenon that frequently occurs during the springtime in the Arctic. In this study, we performed a three-dimensional model study on ODEs occurring at Barrow and its surrounding areas between 28 March and 6 April 2019 using a 3-D multi-scale air quality model, CMAQ (Community Multiscale Air Quality Modeling System). Several ODEs observed at Barrow were captured, and two of them were thoroughly analyzed using the process analysis method to estimate contributions of horizontal transport, vertical transport, dry deposition, and the overall chemical process to the variations in ozone and bromine species during ODEs. We found that the ODE occurring between 30 and 31 March 2019 (referred to as ODE1) was primarily caused by the horizontal transport of low-ozone air from the Beaufort Sea to Barrow. The formation of this low-ozone air over the sea was largely attributed to a release of sea-salt aerosols from the Bering Strait under strong wind conditions, stemming from a cyclone generated on the Chukotka Peninsula. It was also discovered that the surface ozone dropped to less than 5 ppb over the Beaufort Sea, and the overall chemical process contributed up to 10 ppb to the ozone loss. Moreover, BrO over the sea reached a maximum of approximately 80 ppt. This low-ozone air over the sea was then horizontally transported to Barrow, leading to the occurrence of ODE1. Regarding another ODE on 2 April (ODE2), we found that its occurrence was also dominated by the horizontal transport from the sea, but under the control of an anticyclone. The termination of this ODE was mainly attributed to the replenishment of ozone-rich air from the free troposphere by a strong vertical transport. Copyright: © 2023 Le Cao et al. more
AC-SAF Application Atmosphere Climate Data Records

A simulator for the CLARA-A2 cloud climate data record and its application to assess EC-Earth polar cloudiness

Author(s):
Eliasson, S.; Karlsson, K.-G.; Willén, U.
Publication title: Geoscientific Model Development
2020
 | Volume: 13 | Issue: 1
2020
DOI: 10.5194/gmd-13-297-2020
Abstract:
This paper describes a new satellite simulator for the CLARA-A2 climate data record (CDR). This simulator takes into account the variable skill in clo… This paper describes a new satellite simulator for the CLARA-A2 climate data record (CDR). This simulator takes into account the variable skill in cloud detection in the CLARA-A2 CDR by using a different approach to other similar satellite simulators to emulate the ability to detect clouds. In particular, the paper describes three methods to filter out clouds from climate models undetectable by observations. The first method is comparable to the current simulators in the Cloud Feedback Model Intercomparison Project (CFMIP) Observation Simulator Package (COSP), since it relies on a single visible cloud optical depth at 550 nm (τc) threshold applied globally to delineate cloudy and cloud-free conditions. Methods two and three apply long/lat-gridded values separated by daytime and nighttime conditions. Method two uses gridded varying τc as opposed to method one, which uses just a τc threshold, and method three uses a cloud probability of detection (POD) depending on the model τc. The gridded POD values are from the CLARA-A2 validation study by Karlsson and Håkansson (2018). Methods two and three replicate the relative ease or difficulty for cloud retrievals depending on the region and illumination. They increase the cloud sensitivity where the cloud retrievals are relatively straightforward, such as over midlatitude oceans, and they decrease the sensitivity where cloud retrievals are notoriously tricky, such as where thick clouds may be inseparable from cold snow-covered surfaces, as well as in areas with an abundance of broken and small-scale cumulus clouds such as the atmospheric subsidence regions over the ocean. The simulator, together with the International Satellite Cloud Climatology Project (ISCCP) simulator of the COSP, is used to assess Arctic clouds in the EC-Earth climate model compared to the CLARA-A2 and ISCCP H-Series (ISCCPH) CDRs. Compared to CLARA-A2, EC-Earth generally underestimates cloudiness in the Arctic. However, compared to ISCCP and its simulator, the opposite conclusion is reached. Based on EC-Earth, this paper shows that the simulated cloud mask of CLARA-A2, using method three, is more representative of the CDR than method one used for the ISCCP simulator. The simulator substantially improves the simulation of the CLARA-A2-detected clouds, especially in the polar regions, by accounting for the variable cloud detection skill over the year. The approach to cloud simulation based on the POD of clouds depending on their τc, location, and illumination is the preferred one as it reduces cloudiness over a range of cloud optical depths. Climate model comparisons with satellite-derived information can be significantly improved by this approach, mainly by reducing the risk of misinterpreting problems with satellite retrievals as cloudiness features. Since previous studies found that the CLARA-A2 CDR performs well in the Arctic during the summer months, and that method three is more representative than method one, the conclusion is that EC-Earth likely underestimates clouds in the Arctic summer. © Author(s) 2020. more
Atmosphere CM-SAF Climate Data Records Method

Aerosol influence on cloud macrophysical and microphysical properties over the Tibetan Plateau and its adjacent regions

Author(s):
Wei, Chengqiang; Zhao, Pengguo; Wang, Yuting; Wang, Yuan; Mo, Shuying; Zhou, Yunjun
Publication title: Environmental Science and Pollution Research
2024
 | Volume: 31 | Issue: 20
2024
DOI: 10.1007/s11356-024-33247-4
Abstract:
This study uses aerosol optical depth (AOD) and cloud properties data to investigate the influence of aerosol on the cloud properties over the Tibetan… This study uses aerosol optical depth (AOD) and cloud properties data to investigate the influence of aerosol on the cloud properties over the Tibetan Plateau and its adjacent regions. The study regions are divided as the western part of the Tibetan Plateau (WTP), the Indo-Gangetic Plain (IGP), and the Sichuan Basin (SCB). All three regions show significant cloud effects under low aerosol loading conditions. In WTP, under low aerosol loading conditions, the effective radius of liquid cloud particles (LREF) decreases with the increase of aerosol loading, while the effective radius of ice cloud particles (IREF) and cloud top height (CTH) increase during the cold season. Increased aerosol loading might inhibit the development of warm rain processes, transporting more cloud droplets above the freezing level and promoting ice cloud development. During the warm season, under low aerosol loading conditions, both the cloud microphysical (LREF and IREF) and macrophysical (cloud top height and cloud fraction) properties increase with the increase of aerosol loading, likely due to higher dust aerosol concentration in this region. In IGP, both LREF and IREF increase with the increase in aerosol loading during the cold season. In SCB, LREF increases with the increase in aerosol loading, while IREF decreases, possibly due to the higher hygroscopic aerosol concentration in the SCB during the cold season. Meteorological conditions also modulate the aerosol-cloud interaction. Under different convective available potential energy (CAPE) and relative humidity (RH) conditions, the influence of aerosol on clouds varies in the three regions. Under low CAPE and RH conditions, the relationship between LREF and aerosol in both the cold and warm seasons is opposite in the WTP: LREF decreases with the increase of aerosol in the cold season, while it increases in the warm season. This discrepancy may be attributed to a difference in the moisture condition between the cold and warm seasons in this region. In general, the influence of aerosols on cloud properties in TP and its adjacent regions is characterized by significant nonlinearity and spatial variability, which is likely related to the differences in aerosol types and meteorological conditions between different regions. more
Application Atmosphere CM-SAF Climate Data Records

Accounting for inter-annual variability alters long-term estimates of climate suitability

Author(s):
Gardner, A.S.; Gaston, K.J.; Maclean, I.M.D.
Publication title: Journal of Biogeography
2021
 | Volume: 48 | Issue: 8
2021
DOI: 10.1111/jbi.14125
Abstract:
Aim: Species respond to environmental conditions and so reliable assessments of climate suitability are important for predicting how climate change co… Aim: Species respond to environmental conditions and so reliable assessments of climate suitability are important for predicting how climate change could alter their distributions. Long-term average climate data are often used to evaluate the climate suitability of an area, but in these aggregated climate datasets, inter-annual variability is lost. Due to non-linearity in species’ biological responses to climate, estimates of long-term climate suitability from average climate data may be biased and so differ from estimates derived from the average annual suitability over the same period (average response). We investigate the extent to which such differences manifest in a regional assessment of climate suitability for 255 plant species across two 17-year time periods. Location: Cornwall in South-West England provides a case study. Taxon: Plantae. Methods: We run a simple mechanistic climate suitability model and derive quantitative estimates of climate suitability for 1984–2000 and 2001–2017. For each period, we run the model using climate data representing average monthly values for that period. We then run the model for each year using monthly climate data for that year and average the annual suitability scores across each period (average response). We compare estimates of climate suitability from these two approaches. Results: Average climate data gave higher estimates of suitability than the average response, suggesting bias against years of poor suitability in temporally aggregated climate datasets. Differences between suitability estimates were larger in areas of high climate variability and correlated with species’ environmental requirements, being larger for species with small thermal niches and narrow ranges of precipitation tolerance. Main Conclusions: Incorporating inter-annual variability into climate suitability assessments or understanding the extent to which average climate data might obscure this variance will be important to predict reliably the impacts of climate change on species distributions and should be considered when using mechanistic species distribution models. © 2021 The Authors. Journal of Biogeography published by John Wiley & Sons Ltd. more
Application Atmosphere CM-SAF Climate Data Records

Advances in Methodology and Generation of All-Weather Land Surface Temperature Products from Polar-Orbiting and Geostationary Satellites: A comprehensive review

Author(s):
Jia, A.; Liang, S.; Wang, D.; Mallick, K.; Zhou, S.; Hu, T.; Xu, S.
Publication title: IEEE Geoscience and Remote Sensing Magazine
2024
 | Volume: 12 | Issue: 4
2024
DOI: 10.1109/MGRS.2024.3421268
Abstract:
Land surface temperature (LST) is crucial for understanding surface energy budgets, hydrological cycling, and land-Atmosphere interactions. However, c… Land surface temperature (LST) is crucial for understanding surface energy budgets, hydrological cycling, and land-Atmosphere interactions. However, cloud cover leads to numerous data gaps in existing remote sensing thermal infrared (TIR) LST products, seriously restricting their applications. This article provides a comprehensive review concerning both LST recovery methodologies and 26 emerging all-weather products derived from polar-orbiting and geostationary (GEO) satellites. Clarifying product distinctions will enable end users to select suitable options for diverse research. Methodologies are categorized into spatiotemporal interpolation, surface energy balance (SEB)-based physical estimation, passive microwave (PMW)-based methods, and simulated temperature-based approaches. Historical research trajectories, strengths, limitations, and potential research directions of the methodologies and products are discussed. The review reports that existing all-weather LST products generally exhibit root-mean-square errors (RMSEs) of more
Climate Data Records LSA-SAF Terrestrial Validation

An Adaptive Channel Selection Method for Assimilating the Hyperspectral Infrared Radiances

Author(s):
Zhou, L.; Lei, L.; Whitaker, J.S.; Tan, Z.-M.
Publication title: Monthly Weather Review
2024
 | Volume: 152 | Issue: 3
2024
DOI: 10.1175/MWR-D-23-0131.1
Abstract:
Hyperspectral infrared (IR) satellites can provide high-resolution vertical profiles of the atmospheric state, which significantly contributes to the … Hyperspectral infrared (IR) satellites can provide high-resolution vertical profiles of the atmospheric state, which significantly contributes to the forecast skill of numerical weather prediction, especially for regions with sparse observations. One challenge in assimilating the hyperspectral radiances is how to effectively extract the observation information, due to the interchannel correlations and correlated observation errors. An adaptive channel selection method is proposed, which is implemented within the data assimilation scheme and selects the radiance observation with the maximum reduction of variance in observation space. Compared to the commonly used channel selection method based on the maximum entropy reduction (ER), the adaptive method can provide flow-dependent and time-varying channel selections. The performance of the adaptive selection method is evaluated by assimilating only the synthetic Fengyun-4A (FY-4A) GIIRS IR radiances in an observing system simulation experiment (OSSE), with model resolutions from 7.5 to 1.5 km and then 300 m. For both clear-sky and all-sky conditions, the adaptive method generally produces smaller RMS errors of state variables than the ER-based method given similar amounts of assimilated radiances, especially with fine model resolutions. Moreover, the adaptive method has minimum RMS errors smaller than or approaching those with all channels assimilated. For the intensity of the tropical cyclone, the adaptive method also produces smaller errors of the minimum dry air mass and maximal wind speed at different levels, compared to the ER-based selection method. © 2024 American Meteorological Society. more
Application Atmosphere NWP-SAF Software for CDR development

An Assessment of Subseasonal Prediction Skill of the Antarctic Sea Ice Edge

Author(s):
Gao, Y.; Xiu, Y.; Nie, Y.; Luo, H.; Yang, Q.; Zampieri, L.; Lv, X.; Uotila, P.
Publication title: Journal of Geophysical Research: Oceans
2024
 | Volume: 129 | Issue: 11
2024
DOI: 10.1029/2024JC021499
Abstract:
In this study, the subseasonal Antarctic sea ice edge prediction skill of the Copernicus Climate Change Service (C3S) and Subseasonal to Seasonal (S2S… In this study, the subseasonal Antarctic sea ice edge prediction skill of the Copernicus Climate Change Service (C3S) and Subseasonal to Seasonal (S2S) projects was evaluated by a probabilistic metric, the spatial probability score (SPS). Both projects provide subseasonal to seasonal scale forecasts of multiple coupled dynamical systems. We found that predictions by individual dynamical systems remain skillful for up to 38 days (i.e., the ECMWF system). Regionally, dynamical systems are better at predicting the sea ice edge in the West Antarctic than in the East Antarctic. However, the seasonal variations of the prediction skill are partly system-dependent as some systems have a freezing-season bias, some had a melting-season bias, and some had a season-independent bias. Further analysis reveals that the model initialization is the crucial prerequisite for skillful subseasonal sea ice prediction. For those systems with the most realistic initialization, the model physics dictates the propagation of initialization errors and, consequently, the temporal length of predictive skill. Additionally, we found that the SPS-characterized prediction skill could be improved by increasing the ensemble size to gain a more realistic ensemble spread. Based on the C3S systems, we constructed a multi-model forecast from the above principles. This forecast consistently demonstrated a superior prediction skill compared to individual dynamical systems or statistical observation-based benchmarks. In summary, our results elucidate the most important factors (i.e., the model initialization and the model physics) affecting the currently available subseasonal Antarctic sea ice prediction systems and highlighting the opportunities to improve them significantly. © 2024 The Author(s). more
Application Climate Data Records Cryosphere OSI-SAF
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