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The altimetry data record (1993-2022) has enabled the global mean sea level (GMSL) rise to be quantified. Not only is the sea level rising, but it is accelerating (Dieng et al., 2017; Nerem et al., 2018; Ablain et al. 2019) and understanding this acceleration is necessary both to support society’s response to climate change and to inform climate research on the sea level budget, greenhouse gas forcing and the Earth energy imbalance. Here we describe work on quantifying the uncertainty on the sea level climate data record, and identifying the uncertainty needed to provide new answers to the climate research questions. Work by Ablain et al. (2019) and updated in Guérou et al. (2022) at a global scale, has quantified GMSL rise as +3.3 mm/yr (90% confidence uncertainties 0.3 mm/yr) over 1993-2021. Its acceleration has been quantified at 0.12 ± 0.05 mm/yr². At local scales, (Prandi et al. 2021), the sea level is rising almost everywhere over the globe, at rates ranging between 0 and 6 mm/yr, with uncertainties ranging from 0.8 to 1.2 mm/yr depending on the location. The local sea level accelerations are ranging between -1 mm/yr² and +1 mm/yr² with uncertainties between 0.057 and 0.12 mm/yr² (Prandi et al., 2021). Here we present more recent work refining these uncertainty estimates through a metrological approach that propagates uncertainties, and error covariance structures, through the full processing chain of the altimeter measurement from the raw waveform to GMSL, using methods described in Mittaz et al. (2019). We review the assumptions in the derivation of the retracking model from the radar equation, consider error correlation structures in the corrections, and provide fresh consideration of the error covariance matrix, building on the earlier research. The work was developed in the ASELSU project funded by ESA. The project has also reviewed the climate science research questions to understand the need for improved altimeter observations. There are three major science questions that require accuracies greater than those currently achieved. These questions are the closure of the sea level budget, the detection and attribution of the signal in sea level that is forced by greenhouse gas emissions (GHG) and the estimate of the Earth energy imbalance (EEI). Dieng et al. 2017: https://doi.org/10.1002/joc.4996. Nerem et al. 2018: https://doi.org/10.1073/pnas.1717312115 Ablain et al 2019: https://doi.org/10.5194/essd-2019-10 Guérou et al. 2022: https://doi.org/10.5194/egusphere-2022-330 Prandi et al. 2021: https://doi.org/10.1038/s41597-020-00786-7 Mittaz et al. 2019: https://doi.org/10.1088/1681-7575/ab1705

The GHRSST task team on Coral Heat Stress Task Team updated the report to include other uses of SST regarding coral reef science and management beyond the quantification of heat stress. To requirements for stability, geo-location accuracy, SST accuracy (including high SSTs), SST uncertainty, and timeliness, the update adds requirements for diurnal variation, increased spatial resolution, preservation of oceanographic features such as upwelling, and a measure of effective spatial resolution. Find more about this task team:https://www.ghrsst.org/about-ghrsst/task-teams/task-team-on-coral-heat-stress-user-sst-requirements/

Fundamental and thematic climate data records derived from satellite observations provide unique information for climate monitoring and research. Since any satellite operates over a limited period of time only, creating a climate data record requires the combination of space-born measurements from a series of several (often similar) satellites. Simple combination of measurements from several sensors, however, will produce an inconsistent climate data record because the behaviour of historical satellites in space was often different from their behaviour during pre-launch calibration in the laboratory. More scientific value can be derived from considering the series of historical and present satellites as a whole. Here we consider harmonisation as a process that obtains new calibration coefficients and a revised calibration model for each sensor by comparing the output of each satellite to radiometrically more accurate sensors using appropriate match-ups, such as simultaneous nadir overpasses. When we perform a comparison of two sensors using match- ups, we must take into account the fact that those sensors are not observing exactly the same Earth radiance. This is in part due to uncertainties in the collocation process itself, but also due to differences in the spectral response functions of the two instruments, even when nominally observing the same spectral band. We do not aim to correct for spectral response function differences, but to reconcile the calibration of different sensors given their estimated spectral response function differences. Here we present the concept of a framework that establishes calibration coefficients for several sensors simultaneously and rigorously with respect to their uncertainty and error covariance. We present the harmonisation and its mathematical formulation as a large-structured inverse problem. Solving this problem is a challenge because it involves some hundred million of match-ups and has significant error correlation in the measured data. We sketch different approaches to solve the harmonisation problem and present our first attempt to recalibrate AVHRR radiance from a series of nine NOAA and MetOp satellites. Presented at the EUMETSAT Meteorological Satellite Conference, Rome, October 2017.

The WMAP Q, V, and W band radial profiles of temperature deviation of the cosmic microwave background (CMB) were constructed for a sample of 31 randomly selected nearby clusters of galaxies in directions of Galactic latitude $|b| >$ 30$^o$. The profiles were compared in detail with the expected CMB Sunyaev-Zel'dovich effect (SZE) caused by these clusters, with the hot gas properties of each cluster inferred observationally by applying gas temperatures as measured by ASCA to isothermal $\beta$ models of the ROSAT X-ray surface brightness profiles, and with the WMAP point spread function fully taken into consideration. After co-adding the 31 cluster field, it appears that WMAP detected the SZE in all three bands. Quantitatively, however, the observed SZE only accounts for about 1/4 of the expected decrement. The discrepancy represents too much unexplained extra flux: in the W band, the detected SZE corresponds on average to 5.6 times less X-ray gas mass within a 10 arcmin radius than the mass value given by the ROSAT $\beta$ model. We examined critically how the X-ray prediction of the SZE may depend on our uncertainties in the density and temperature of the hot intracluster plasma, and emission by cluster radio sources. Although our comparison between the detected and expected SZE levels is subject to a margin of error, the fact remains that the average observed SZE depth and profile are consistent with those of the primary CMB anisotropy, i.e. in principle the average WMAP temperature decrement among the 31 rich clusters is too shallow to accomodate any extra effect like the SZE. A unique aspect of this SZE investigation is that because all the data being analyzed are in the public domain, our work is readily open to the scrutiny of others., Comment: ApJ v648, p176 (2006). This is the final version to appear on Sept. 1st

Since all of the five clusters of galaxies observed by the Extreme Ultraviolet Explorer deep survey telescope are found to possess a diffuse EUV emitting component which is unrelated to the virial gas at X-ray temperatures, the question concerning the nature of this new component has been a subject of controversy. Here we present results of an EUV and soft X-ray spatial analysis of the rich clusters Abell 2199 and 1795. The EUV emission does not resemble the X-ray morphology of clusters: at the cluster core the EUV contours are organized, but at larger radii they are anisotropic, and therefore need not have originated from a hydrostatically stable medium. The ratio of EUV to soft X-ray intensity rises with respect to cluster radius, to reach values ~10 times higher than that expected from the virial gas. The strong EUV excess which exists in the absence of soft X-ray excess poses formidable problems to the non-thermal (inverse-Compton) scenario, but may readily be explained as due to emission lines present only in the EUV range. In particular, warm gas produced by shock heating could account for such lines without proliferation of bolometric luminosities and mass budgets., Comment: 13 pages, 7 figures (4 in color), minor typos corrected; ApJL accepted

We report the detection of both soft and hard excess X-ray emission in the cluster of galaxies A 2199, based upon spatially resolved spectroscopy with data from the BeppoSAX, EUVE and ROSAT missions. The excess emission is visible at radii larger than 300 kpc and increases in strength relative to the isothermal component. The total 0.1-100 keV luminosity of this component is 15 % of the cluster luminosity, but it dominates the cluster luminosity at high and low energies. We argue that the most plausible interpretation of the excess emission is an inverse Compton interaction between the cosmic microwave background and relativistic electrons in the cluster. The observed spatial distribution of the non-thermal component implies that there is a large halo of cosmic ray electrons between 0.5-1.5 Mpc surrounding the cluster core. The prominent existence of this component has cosmological implications, as it is significantly changing our picture of a clusters's particle acceleration history, dynamics between the thermal and relativistic media, and total mass budgets., Comment: Accepted for publication in Astrophysical Journal, Letters

Presentation on the comparison of errors-in-variables and marginalised errors-in-variables harmonisation methods given by R. Quast during the FIDUCEO science meeting at NPL in January 2019. This presentation supplements the journal article: Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. Remote Sens. 2019, 11, 1002. doi:10.3390/rs11091002., {"references":["Giering, R.; Quast, R.; Mittaz, J.P.D.; Hunt, S.E.; Harris, P.M.; Woolliams, E.R.; Merchant, C.J. A Novel Framework to Harmonise Satellite Data Series for Climate Applications. Remote Sens. 2019, 11, 1002"]}