2023's Antarctic sea ice extent is the lowest on record

Author(s):
Gilbert, E.; Holmes, C.
Publication title: Weather
2024
2024
DOI: 10.1002/wea.4518
Abstract:
Antarctic sea ice is a vitally important part of the regional and global climate. In 2023, sea ice extent fell to record lows, reaching unprecedented … Antarctic sea ice is a vitally important part of the regional and global climate. In 2023, sea ice extent fell to record lows, reaching unprecedented values for both the summer minimum, winter maximum and intervening freeze-up period. Here, we show that the extreme values observed were truly remarkable within the context of the satellite record, despite the challenge of quantifying how rare such an event might be, and discuss some contributing factors. While this could be part of a decline in sea ice associated with human-caused climate change, it is too early to say conclusively if this is the case. © 2024 The Authors. Weather published by John Wiley & Sons Ltd on behalf of Royal Meteorological Society. more
Application Climate Data Records Cryosphere OSI-SAF

A 16-year global climate data record of total column water vapour generated from OMI observations in the visible blue spectral range

Author(s):
Borger, C.; Beirle, S.; Wagner, T.
Publication title: Earth System Science Data
2023
 | Volume: 15 | Issue: 7
2023
DOI: 10.5194/essd-15-3023-2023
Abstract:
We present a long-term data set of 1×1 monthly mean total column water vapour (TCWV) based on global measurements of the Ozone Monitoring Instrument (… We present a long-term data set of 1×1 monthly mean total column water vapour (TCWV) based on global measurements of the Ozone Monitoring Instrument (OMI) covering the time range from January 2005 to December 2020. In comparison to the retrieval algorithm of , several modifications and filters have been applied accounting for instrumental issues (such as OMI's "row anomaly") or the inferior quality of solar reference spectra. For instance, to overcome issues related to low-quality reference spectra, the daily solar irradiance spectrum is replaced by an annually varying mean earthshine radiance obtained in December over Antarctica. For the TCWV data set, we only consider measurements with an effective cloud fraction less than 20 %, an air mass factor (AMF) greater than 0.1, a snow- and ice-free ground pixel, and an OMI row that is not affected by the row anomaly over the complete time range of the data set. The individual TCWV measurements are then gridded to a regular 1×1 lattice, from which the monthly means are calculated. The investigation of sampling errors in the OMI TCWV data set shows that these are dominated by the clear-sky bias and cause on average deviations of around -10 %, which is consistent with the findings of previous studies. However, the spatiotemporal sampling errors and those due to the row-anomaly filter are negligible. In a comprehensive intercomparison study, we demonstrate that the OMI TCWV data set is in good agreement with the global reference data sets of ERA5 (fifth-generation ECMWF atmospheric reanalysis), RSS SSM/I (Remote Sensing Systems Special Sensor Microwave Imager), and CM SAF/CCI TCWV-global (COMBI): over ocean the orthogonal distance regressions indicate slopes close to unity with very small offsets and high coefficients of determination of around 0.96. However, over land, distinctive positive deviations of more than +10 kg m-2 are obtained for high TCWV values. These overestimations are mainly due to extreme overestimations of high TCWV values in the tropics, likely caused by uncertainties in the retrieval input data (surface albedo, cloud information) due to frequent cloud contamination in these regions. Similar results are found from intercomparisons with in situ radiosonde measurements from version 2 of the Integrated Global Radiosonde Archive (IGRA2) data set. Nevertheless, for TCWV values smaller than 25 kg m-2, the OMI TCWV data set shows very good agreement with the global reference data sets. Furthermore, a temporal stability analysis proves that the OMI TCWV data set is consistent with the temporal changes in the reference data sets and shows no significant deviation trends. As the TCWV retrieval can be easily applied to further satellite missions, additional TCWV data sets can be created from past missions, such as the Global Ozone Monitoring Experiment-1 (GOME-1) or the SCanning Imaging Absorption spectroMeter for Atmospheric CartograpHY (SCIAMACHY); under consideration of systematic differences (e.g. due to different observation times), these data sets can be combined with the OMI TCWV data set in order to create a data record that would cover a time span from 1995 to the present. Moreover, the TCWV retrieval will also work for all missions dedicated to NO2 in the future, such as Sentinel-5 on MetOp-SG. The Max Planck Institute for Chemistry (MPIC) OMI total column water vapour (TCWV) climate data record (CDR) is available at 10.5281/zenodo.7973889 . © 2023 Christian Borger et al. more
Atmosphere CM-SAF Climate Data Records Validation

A 35-year daily global solar radiation dataset reconstruction at the Great Wall Station, Antarctica: First results and comparison with ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products

Author(s):
Zeng, Zhaoliang; Wang, Xin; Wang, Zemin; Zhang, Wenqian; Zhang, Dongqi; Zhu, Kongju; Mai, Xiaoping; Cheng, Wei; Ding, Minghu
Publication title: Frontiers in Earth Science
2022
 | Volume: 10
2022
DOI:
Abstract:
Solar radiation drives many geophysical and biological processes in Antarctica, such as sea ice melting, ice sheet mass balance, and photosynthetic pr… Solar radiation drives many geophysical and biological processes in Antarctica, such as sea ice melting, ice sheet mass balance, and photosynthetic processes of phytoplankton in the polar marine environment. Although reanalysis and satellite products can provide important insight into the global scale of solar radiation in a seamless way, the ground-based radiation in the polar region remains poorly understood due to the harsh Antarctic environment. The present study attempted to evaluate the estimation performance of empirical models and machine learning models, and use the optimal model to establish a 35-year daily global solar radiation (DGSR) dataset at the Great Wall Station, Antarctica using meteorological observation data during 1986–2020. In addition, it then compared against the DGSR derived from ERA5, CRA40 reanalysis, and ICDR (AVHRR) satellite products. For the DGSR historical estimation performance, the machine learning method outperforms the empirical formula method overall. Among them, the Mutli2 model (hindcast test R2, RMSE, and MAE are 0.911, 1.917 MJ/m2, and 1.237 MJ/m2, respectively) for the empirical formula model and XGBoost model (hindcast test R2, RMSE, and MAE are 0.938, 1.617 MJ/m2, and 1.030 MJ/m2, respectively) for the machine learning model were found with the highest accuracy. For the austral summer half-year, the estimated DGSR agrees very well with the observed DGSR, with a mean bias of only −0.47 MJ/m2. However, other monthly DGSR products differ significantly from observations, with mean bias of 1.05 MJ/m2, 3.27 MJ/m2, and 6.90 MJ/m2 for ICDR (AVHRR) satellite, ERA5, and CRA40 reanalysis products, respectively. In addition, the DGSR of the Great Wall Station, Antarctica followed a statistically significant increasing trend at a rate of 0.14 MJ/m2/decade over the past 35 years. To our best knowledge, this study presents the first reconstruction of the Antarctica Great Wall Station DGSR spanning 1986–2020, which will contribute to the research of surface radiation balance in Antarctic Peninsula. more
Atmosphere CM-SAF Climate Data Records Cryosphere Validation

2018 Atmospheric Motion Vector (AMV) Intercomparison Study

Author(s):
Santek, David; Dworak, Richard; Nebuda, Sharon; Wanzong, Steve; Borde, Régis; Genkova, Iliana; García-Pereda, Javier; Galante Negri, Renato; Carranza, Manuel; Nonaka, Kenichi; Shimoji, Kazuki; Oh, Soo Min; Lee, Byung-Il; Chung, Sung-Rae; Daniels, Jaime; Bresky, Wayne
Publication title: Remote Sensing
2019
 | Volume: 11 | Issue: 19
2019
DOI: 10.3390/rs11192240
Abstract:
Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, Europ… Atmospheric Motion Vectors (AMVs) calculated by six different institutions (Brazil Center for Weather Prediction and Climate Studies/CPTEC/INPE, European Organization for the Exploitation of Meteorological Satellites/EUMETSAT, Japan Meteorological Agency/JMA, Korea Meteorological Administration/KMA, Unites States National Oceanic and Atmospheric Administration/NOAA, and the Satellite Application Facility on Support to Nowcasting and Very short range forecasting/NWCSAF) with JMA’s Himawari-8 satellite data and other common input data are here compared. The comparison is based on two different AMV input datasets, calculated with two different image triplets for 21 July 2016, and the use of a prescribed and a specific configuration. The main results of the study are summarized as follows: (1) the differences in the AMV datasets depend very much on the ‘AMV height assignment’ used and much less on the use of a prescribed or specific configuration; (2) the use of the ‘Common Quality Indicator (CQI)’ has a quantified skill in filtering collocated AMVs for an improved statistical agreement between centers; (3) Among the six AMV operational algorithms verified by this AMV Intercomparison, JMA AMV algorithm has the best overall performance considering all validation metrics, mainly due to its new height assignment method: ‘Optimal estimation method considering the observed infrared radiances, the vertical profile of the Numerical Weather Prediction wind, and the estimated brightness temperature using a radiative transfer model’. more
Atmosphere NWC-SAF Software for CDR development Validation

A Climatological Overview of Arctic Clouds

Author(s):
Devasthale, Abhay
2020
2020
DOI:
Abstract:
The Arctic climate system is complex and clouds are one of its least understood components. Since cloud processes occur from micrometer to synoptic sc… The Arctic climate system is complex and clouds are one of its least understood components. Since cloud processes occur from micrometer to synoptic scales, their couplings with the other components of the Arctic climate system and their overall role in modulating the energy budget at different spatio-temporal scales is challenging to quantify. The in-situ measurements, as limited in space and time as they are, still reveal the complex nature of cloud microphysical and thermodynamical processes in the Arctic. However, the synoptic scale variability of cloud systems can only be obtained from the satellite observations. A considerable progress has been made in the last decade in understanding cloud processes in the Arctic due to the availability of valuable data from the multiple campaigns in the Central Arctic and due to the advances in the satellite remote sensing. This chapter provides an overview of this progress. First an overview of the lessons learned from the recent in-situ measurement campaigns in the Arctic is provided. In particular, the importance of supercooled liquid water clouds, their role in the radiation budget and their interaction with the vertical thermodynamical structure is discussed. In the second part of the chapter, a climatological overview of cloud properties using the state-of-the-art satellite based cloud climate datasets is provided. The agreements and disagreements in these datasets are highlighted. The third and the fourth parts of the chapter highlight two most important processes that are currently being researched, namely cloud response to the rapidly changing sea-ice extent and the role of moisture transport in to the Arctic in governing cloud variability. Both of these processes have implications for the cloud feedback in the Arctic. more
Application Atmosphere CM-SAF Climate Data Records

A cloud-by-cloud approach for studying aerosol-cloud interaction in satellite observations

Author(s):
Alexandri, F.; Müller, F.; Choudhury, G.; Achtert, P.; Seelig, T.; Tesche, M.
Publication title: Atmospheric Measurement Techniques
2024
 | Volume: 17 | Issue: 6
2024
DOI: 10.5194/amt-17-1739-2024
Abstract:
The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACIs) and rapid adjustments (ERFaci) still causes the largest uncertainty in … The effective radiative forcing (ERF) due to aerosol-cloud interactions (ACIs) and rapid adjustments (ERFaci) still causes the largest uncertainty in the assessment of climate change. It is understood only with medium confidence and is studied primarily for warm clouds. Here, we present a novel cloud-by-cloud (C×C) approach for studying ACI in satellite observations that combines the concentration of cloud condensation nuclei (nCCN) and ice nucleating particles (nINP) from polar-orbiting lidar measurements with the development of the properties of individual clouds by tracking them in geostationary observations. We present a step-by-step description for obtaining matched aerosol-cloud cases. The application to satellite observations over central Europe and northern Africa during 2014, together with rigorous quality assurance, leads to 399 liquid-only clouds and 95 ice-containing clouds that can be matched to surrounding nCCN and nINP respectively at cloud level. We use this initial data set for assessing the impact of changes in cloud-relevant aerosol concentrations on the cloud droplet number concentration (Nd) and effective radius (reff) of liquid clouds and the phase of clouds in the regime of heterogeneous ice formation. We find a ΔlnNd/ΔlnnCCN of 0.13 to 0.30, which is at the lower end of commonly inferred values of 0.3 to 0.8. The Δlnreff/ΔlnnCCN between -0.09 and -0.21 suggests that reff decreases by -0.81 to -3.78 nm per increase in nCCN of 1 cm-3. We also find a tendency towards more cloud ice and more fully glaciated clouds with increasing nINP that cannot be explained by the increasingly lower cloud top temperature of supercooled-liquid, mixed-phase, and fully glaciated clouds alone. Applied to a larger number of observations, the C×C approach has the potential to enable the systematic investigation of warm and cold clouds. This marks a step change in the quantification of ERFaci from space. © Copyright: more
Application Atmosphere CM-SAF Climate Data Records

A Bayesian machine learning method to explain the error characteristics of global-scale soil moisture products

Author(s):
Kim, Hyunglok; Crow, Wade T.; Wagner, Wolfgang; Li, Xiaojun; Lakshmi, Venkataraman
Publication title: Remote Sensing of Environment
2023
 | Volume: 296
2023
DOI: 10.1016/j.rse.2023.113718
Abstract:
Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance f… Estimating accurate surface soil moisture (SM) dynamics from space, and knowing the error characteristics of these estimates, is of great importance for the application of satellite-based SM data throughout many Earth Science/Environmental Engineering disciplines. Here, we introduce the Bayesian inference approach to analyze the error characteristics of widely used passive and active microwave satellite-derived SM data sets, at different overpass times, acquired from the Soil Moisture Active Passive (SMAP), Soil Moisture and Ocean Salinity (SMOS), and Advanced Scatterometer (ASCAT) missions. In particular, we apply Bayesian hierarchical modeling (BHM) and triple collocation analysis (TCA) to investigate the relative importance of different environmental factors and human activities on the accuracy of satellite-based data. To start, we compare the BHM-based sensitivity analysis method to the classic multiple regression models using a frequentist approach, which includes complete pooling and no-pooling models that have been widely used for sensitivity analysis in the field of remote sensing and demonstrate the BHM's adaptability and great potential for providing insight into sensitivity analysis that can be used by various remote sensing research communities. Next, we conduct an uncertainty analysis on BHM's model parameters using a full range of uncertainties to assess the association of various environmental factors with the accuracy of satellite-derived SM data. We focus on investigating human-induced error sources such as disturbed surface soil layers caused by irrigation activities on microwave satellite systems, naturally introduced error sources such as vegetation and soil organic matter, and errors related to the disregard of SM retrieval algorithmic assumptions - such as the thermal equilibrium passive microwave systems. Based on the BHM-based sensitivity analysis, we find that assessments of SM data quality with a single variable should be avoided, since numerous other factors simultaneously influence their quality. As such, this provides a useful framework for applying Bayesian theory to the investigation of the error characteristics of satellite-based SM data and other time-varying geophysical variables. more
Climate Data Records H-SAF Land Validation
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