AR6 Climate Change 2021, The Physical Science Basis - chapter 9: ocean, cryosphere and sea levele change

Author(s):
IPCC
2021
2021
DOI:
Abstract:
This chapter assesses past and projected changes in the ocean, cryosphere and sea level using paleo reconstructions, instrumental observations and mod… This chapter assesses past and projected changes in the ocean, cryosphere and sea level using paleo reconstructions, instrumental observations and model simulations. In the following summary, we update and expand the related assessments from the IPCC Fifth Assessment Report (AR5), the Special Report on Global Warming of 1.5ºC (SR1.5) and the Special Report on Ocean and Cryosphere in a Changing Climate (SROCC). Major advances in this chapter since the SROCC include the synthesis of extended and new observations, which allows for improved assessment of past change, processes and budgets for the last century, and the use of a hierarchy of models and emulators, which provide improved projections and uncertainty estimates of future change. In addition, the systematic use of model emulators makes our projections of ocean heat content, land-ice loss and sea level rise fully consistent both with each other and with the assessed equilibrium climate sensitivity and projections of global surface air temperature across the entire report. In this executive summary, uncertainty ranges are reported as very likely ranges and expressed by square brackets, unless otherwise noted. more
Application CDR Climate Data Records Cryosphere OSI-SAF Ocean

Arctic and Antarctic Sea Ice Thickness and Volume Changes From Observations Between 1994 and 2023

Author(s):
Bocquet, M.; Fleury, S.; Rémy, F.; Piras, F.
Publication title: Journal of Geophysical Research: Oceans
2024
 | Volume: 129 | Issue: 11
2024
DOI: 10.1029/2023JC020848
Abstract:
Both Arctic and Antarctic sea ice are affected by climate change. While Arctic sea ice has been declining for several decades, Antarctic sea ice exten… Both Arctic and Antarctic sea ice are affected by climate change. While Arctic sea ice has been declining for several decades, Antarctic sea ice extent slowly increased until 2015, followed by a sharp drop in 2016. Quantifying sea ice changes is essential to assess their impacts on the ocean, atmosphere, ecosystems and Arctic communities. In this study, we combine sea ice thickness estimates from four satellite radar altimeters to derive the longest time series of homogeneous sea ice thickness for both hemispheres over 30 years (1994–2023). The record supports the rapid loss of sea ice in the Arctic for each month of the year and the heterogeneous changes in sea ice thickness in the Antarctic. The study confirms that most of the volume variability is due to the thickness variability, which holds true for both hemispheres. The sea ice thickness time series presented here offer new insights for models, in particular the possibility to evaluate sea ice reanalyses and to initialize forecasts, especially in the Antarctic, where the data set presented here has no equivalent in terms of spatial and temporal coverage. © 2024. The Author(s). more
Application CDR Climate Data Records Cryosphere OSI-SAF

Arctic Sea Ice in CMIP6

Author(s):
Notz, Dirk
Publication title: Geophysical Research Letters
2020
 | Volume: 47 | Issue: 10
2020
DOI: 10.1029/2019GL086749
Abstract:
We examine CMIP6 simulations of Arctic sea-ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea-ice area, capturing… We examine CMIP6 simulations of Arctic sea-ice area and volume. We find that CMIP6 models produce a wide spread of mean Arctic sea-ice area, capturing the observational estimate within the multimodel ensemble spread. The CMIP6 multimodel ensemble mean provides a more realistic estimate of the sensitivity of September Arctic sea-ice area to a given amount of anthropogenic CO2 emissions and to a given amount of global warming, compared with earlier CMIP experiments. Still, most CMIP6 models fail to simulate at the same time a plausible evolution of sea-ice area and of global mean surface temperature. In the vast majority of the available CMIP6 simulations, the Arctic Ocean becomes practically sea-ice free (sea-ice area <1 × 106 km2) in September for the first time before the Year 2050 in each of the four emission scenarios SSP1-1.9, SSP1-2.6, SSP2-4.5, and SSP5-8.5 examined here. © 2020. The Authors. more
Application CDR Climate Data Records Cryosphere OSI-SAF

Arctic sea-ice change tied to its mean state through thermodynamic processes

Author(s):
Massonnet, François; Vancoppenolle, Martin; Goosse, Hugues; Docquier, David; Fichefet, Thierry; Blanchard-Wrigglesworth, Edward
Publication title: Nature Climate Change
2018
 | Volume: 8 | Issue: 7
2018
DOI: 10.1038/s41558-018-0204-z
Abstract:
One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover1. While all state… One of the clearest manifestations of ongoing global climate change is the dramatic retreat and thinning of the Arctic sea-ice cover1. While all state-of-the-art climate models consistently reproduce the sign of these changes, they largely disagree on their magnitude1,2,3,4, the reasons for which remain contentious3,5,6,7. As such, consensual methods to reduce uncertainty in projections are lacking7. Here, using the CMIP5 ensemble, we propose a process-oriented approach to revisit this issue. We show that intermodel differences in sea-ice loss and, more generally, in simulated sea-ice variability, can be traced to differences in the simulation of seasonal growth and melt. The way these processes are simulated is relatively independent of the complexity of the sea-ice model used, but rather a strong function of the background thickness. The larger role played by thermodynamic processes as sea ice thins8,9 further suggests that the recent10 and projected11 reductions in sea-ice thickness induce a transition of the Arctic towards a state with enhanced volume seasonality but reduced interannual volume variability and persistence, before summer ice-free conditions eventually occur. These results prompt modelling groups to focus their priorities on the reduction of sea-ice thickness biases. more
Application CDR Climate Data Records Cryosphere OSI-SAF

Arctic Sea-Ice Surface Elevation Distribution from NASA’s Operation IceBridge ATM Data

Author(s):
Yi, Donghui; Egido, Alejandro; Smith, Walter H. F.; Connor, Laurence; Buchhaupt, Christopher; Zhang, Dexin
Publication title: Remote Sensing
2022
 | Volume: 14 | Issue: 13
2022
DOI: 10.3390/rs14133011
Abstract:
In this paper, we characterize the sea-ice elevation distribution by using NASA’s Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) L1B data… In this paper, we characterize the sea-ice elevation distribution by using NASA’s Operation IceBridge (OIB) Airborne Topographic Mapper (ATM) L1B data over the Arctic Ocean during 94 Spring campaigns between 2009 and 2019. The ultimate objective of this analysis is to better understand sea-ice topography to improve the estimation of the sea-ice freeboard for nadir-looking altimeters. We first introduce the use of an exponentially modified Gaussian (EMG) distribution to fit the surface elevation probability density function (PDF). The characteristic function of the EMG distribution can be integrated in the modeling of radar altimeter waveforms. Our results indicate that the Arctic sea-ice elevation PDF is dominantly positively skewed and the EMG distribution is better suited to fit the PDFs than the classical Gaussian or lognormal PDFs. We characterize the elevation correlation characteristics by computing the autocorrelation function (ACF) and correlation length (CL) of the ATM measurements. To support the radar altimeter waveform retracking over sea ice, we perform this study typically on 1.5 km ATM along-track segments that reflect the footprint diameter size of radar altimeters. During the studied period, the mean CL values range from 20 to 30 m, which is about 2% of the radar altimeter footprint diameter (1.5 km). more
Application CDR Climate Data Records Cryosphere OSI-SAF

Artificial intelligence (AI)-derived 3D cloud tomography from geostationary 2D satellite data

Author(s):
Brüning, S.; Niebler, S.; Tost, H.
Publication title: Atmospheric Measurement Techniques
2024
 | Volume: 17 | Issue: 3
2024
DOI: 10.5194/amt-17-961-2024
Abstract:
Satellite instruments provide high-temporal-resolution data on a global scale, but extracting 3D information from current instruments remains a challe… Satellite instruments provide high-temporal-resolution data on a global scale, but extracting 3D information from current instruments remains a challenge. Most observational data are two-dimensional (2D), offering either cloud top information or vertical profiles. We trained a neural network (Res-UNet) to merge high-resolution satellite images from the Meteosat Second Generation (MSG) Spinning Enhanced Visible and InfraRed Imager (SEVIRI) with 2D CloudSat radar reflectivities to generate 3D cloud structures. The Res-UNet extrapolates the 2D reflectivities across the full disk of MSG SEVIRI, enabling a reconstruction of the cloud intensity, height, and shape in three dimensions. The imbalance between cloudy and clear-sky CloudSat profiles results in an overestimation of cloud-free pixels. Our root mean square error (RMSE) accounts for 2.99dBZ. This corresponds to 6.6% error on a reflectivity scale between -25 and 20dBZ. While the model aligns well with CloudSat data, it simplifies multi-level and mesoscale clouds in particular. Despite these limitations, the results can bridge data gaps and support research in climate science such as the analysis of deep convection over time and space. © Copyright: more
Application Atmosphere CDR CM-SAF Climate Data Records EUM-Secr Validation

Artificial Neural Networks to Retrieve Land and Sea Skin Temperature from IASI

Author(s):
Safieddine, Sarah; Parracho, Ana Claudia; George, Maya; Aires, Filipe; Pellet, Victor; Clarisse, Lieven; Whitburn, Simon; Lezeaux, Olivier; Thépaut, Jean-Noël; Hersbach, Hans; Radnoti, Gabor; Goettsche, Frank; Martin, Maria; Doutriaux-Boucher, Marie; Coppens, Dorothée; August, Thomas; Zhou, Daniel K.; Clerbaux, Cathy
Publication title: Remote Sensing
2020
 | Volume: 12 | Issue: 17
2020
DOI: 10.3390/rs12172777
Abstract:
Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface… Surface skin temperature (Tskin) derived from infrared remote sensors mounted on board satellites provides a continuous observation of Earth’s surface and allows the monitoring of global temperature change relevant to climate trends. In this study, we present a fast retrieval method for retrieving Tskin based on an artificial neural network (ANN) from a set of spectral channels selected from the Infrared Atmospheric Sounding Interferometer (IASI) using the information theory/entropy reduction technique. Our IASI Tskin product (i.e., TANN) is evaluated against Tskin from EUMETSAT Level 2 product, ECMWF Reanalysis (ERA5), SEVIRI observations, and ground in situ measurements. Good correlations between IASI TANN and the Tskin from other datasets are shown by their statistic data, such as a mean bias and standard deviation (i.e., [bias, STDE]) of [0.55, 1.86 °C], [0.19, 2.10 °C], [−1.5, 3.56 °C], from EUMETSAT IASI L-2 product, ERA5, and SEVIRI. When compared to ground station data, we found that all datasets did not achieve the needed accuracy at several months of the year, and better results were achieved at nighttime. Therefore, comparison with ground-based measurements should be done with care to achieve the ±2 °C accuracy needed, by choosing, for example, a validation site near the station location. On average, this accuracy is achieved, in particular at night, leading to the ability to construct a robust Tskin dataset suitable for Tskin long-term spatio-temporal variability and trend analysis. more
Application CDR Climate Data Records EUM-Secr

Assessing climatic benefits from forestation potential in semi-arid lands

Author(s):
Rohatyn, Shani; Rotenberg, Eyal; Yakir, Dan; Carmel, Yohay
Publication title: ENVIRONMENTAL RESEARCH LETTERS
2021
 | Volume: 16 | Issue: 10
2021
DOI: 10.1088/1748-9326/ac29e9
Abstract:
Forestation actions are a major tool for both climate-change mitigation and biodiversity conservation. We address two weaknesses in this approach: the… Forestation actions are a major tool for both climate-change mitigation and biodiversity conservation. We address two weaknesses in this approach: the little attention given to the negative effects of reduced albedo associated with forestation in many regions, and ignoring the potential of drylands that account for 40% of the global potential land area for forestation. We propose an approach to identify suitable land for forestation and quantify its `net equivalent carbon stock change' over 80 years of forest lifetime (NESC), accounting for both carbon sequestration and albedo changes. We combined remote-sensing tools with data-based estimates of surface parameters and with published climate matrices, to identify suitable land for forestation actions. We then calculated the cumulative (over 80 years) `net sequestration potential' (Delta SP), the `emission equivalent of shortwave radiation forcing' (EESF) due to changes in surface albedo, and, in turn, the combined NESC = Delta SP-EESF, of planting forests with >40% tree-cover. Demonstrating our approach in a large climatically diverse state (Queensland), we identified 14.5 million hectares of potential forestation land in its semi-arid land and show that accounting for the EESF, reduces the climatic benefits of the Delta SP by almost 50%. Nevertheless, it results in a total NESC of 0.72 Gt C accumulated by the end of the century, and 80 years of forestation cycle. This estimated NESC is equivalent to 15% of the projected carbon emissions for the same period in Queensland, for a scenario of no change in emission rates during that period. Our approach extends restoration efforts by identifying new land for forestation and carbon sequestration but also demonstrates the importance of quantifying the climatic value of forestation in drylands. more
Application CDR CM-SAF Climate Data Records Land

Assessing Crop Water Requirement and Yield by Combining ERA5-Land Reanalysis Data with CM-SAF Satellite-Based Radiation Data and Sentinel-2 Satellite Imagery

Author(s):
Pelosi, Anna; Belfiore, Oscar Rosario; D’Urso, Guido; Chirico, Giovanni Battista
Publication title: Remote Sensing
2022
 | Volume: 14 | Issue: 24
2022
DOI: 10.3390/rs14246233
Abstract:
The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest da… The widespread development of Earth Observation (EO) systems and advances in numerical atmospheric modeling have made it possible to use the newest data sources as input for crop–water balance models, thereby improving the crop water requirements (CWR) and yield estimates from the field to the regional scale. Satellite imagery and numerical weather prediction outputs offer high resolution (in time and space) gridded data that can compensate for the paucity of crop parameter field measurements and ground weather observations, as required for assessments of CWR and yield. In this study, the AquaCrop model was used to assess CWR and yield of tomato on a farm in Southern Italy by assimilating Sentinel-2 (S2) canopy cover imagery and using CM-SAF satellite-based radiation data and ERA5-Land reanalysis as forcing weather data. The prediction accuracy was evaluated with field data collected during the irrigation season (April–July) of 2021. Satellite estimates of canopy cover differed from ground observations, with a RMSE of about 11%. CWR and yield predictions were compared with actual data regarding irrigation volumes and harvested yield. The results showed that S2 estimates of crop parameters represent added value, since their assimilation into crop growth models improved CWR and yield estimates. Reliable CWR and yield estimates can be achieved by combining the ERA5-Land and CM-SAF weather databases with S2 imagery for assimilation into the AquaCrop model. more
Application Atmosphere CDR CM-SAF Climate Data Records Land

Assessing sea ice microwave emissivity up to submillimeter waves from airborne and satellite observations

Author(s):
Risse, N.; Mech, M.; Prigent, C.; Spreen, G.; Crewell, S.
Publication title: Cryosphere
2024
 | Volume: 18 | Issue: 9
2024
DOI: 10.5194/tc-18-4137-2024
Abstract:
Upcoming submillimeter wave satellite missions require an improved understanding of sea ice emissivity to separate atmospheric and surface microwave s… Upcoming submillimeter wave satellite missions require an improved understanding of sea ice emissivity to separate atmospheric and surface microwave signals under dry polar conditions. This work investigates hectometer-scale observations of airborne sea ice emissivity between 89 and 340 GHz, combined with high-resolution visual imagery from two Arctic airborne field campaigns that took place in summer 2017 and spring 2019 northwest of Svalbard, Norway. Using k-means clustering, we identify four distinct sea ice emissivity spectra that occur predominantly across multiyear ice, first-year ice, young ice, and nilas. Nilas features the highest emissivity, and multiyear ice features the lowest emissivity among the clusters. Each cluster exhibits similar nadir emissivity distributions from 183 to 340 GHz. To relate hectometer-scale airborne measurements to kilometer-scale satellite footprints, we quantify the reduction in the variability of airborne emissivity as footprint size increases. At 340 GHz, the emissivity interquartile range decreases by almost half when moving from the hectometer scale to a footprint of 16 km, typical of satellite instruments. Furthermore, we collocate the airborne observations with polar-orbiting satellite observations. After resampling, the absolute relative bias between airborne and satellite emissivities at similar channels lies below 3 %. Additionally, spectral variations in emissivity at nadir on the satellite scale are low, with slightly decreasing emissivity from 183 to 243 GHz, which occurs for all hectometer-scale clusters except those predominantly composed of multiyear ice. Our results will enable the development of microwave retrievals and assimilation over sea ice in current and future satellite missions, such as the Ice Cloud Imager (ICI) and EUMETSAT Polar System - Sterna (EPS-Sterna). © 2024 Nils Risse et al. more
Application Atmosphere NWP-SAF Software for CDR development
Subscribe to