Important changes in the Earth’s water cycle can be assessed by analysing sea surface salinity, as this variable on average reflects the balance between precipitation and evaporation over ocean, being the upper layers the most sensitive to atmosphere-ocean interactions. In situ measurements of salinity are relatively scarce, reduced to a limited number of field campaigns, buoys, and drifters, and typically acquired some meters below the sea surface. Thus, they may not necessarily well represent ocean-atmosphere exchanges. Satellite measurements, on the contrary, are synoptic, repetitive and represent the uppermost surface of the ocean.
In this work, we show that the dynamics captured by satellite-derived sea surface salinity (SSS) measurements differ from the dynamics shown by in situ near SSS (NSS) measurements. We compare a temporal series of 8 years of the Soil Moisture and Ocean Salinity (SMOS) SSS maps with the output of an ocean model that assimilates in situ salinity measurements, which includes salinity in the first meters of the surface (NSS), as well as the mixed layer depth (MLD) and the sea surface temperature (SST).
On the one hand, the satellite SSS measurements present a clear intensification of the water cycle which is somewhat less evident in the NSS. The water cycle is expected to intensify in the context of global warming, according to the Clausius-Clapeyron (CC) relation, which states that the saturation of the water vapor pressure increases at a rate of 7% per degree Celsius of warming. During the analysed 8 years, we observe a positive SST trend ranging between 0.2ºC/year and 0.1ºC/year (depending on the region) and a SSS trend ranging between 0.008 psu/year and 0.015 psu/year, which is consistent with the CC law.
On the other hand, we observe that the largest positive differences between the satellite SSS and the NSS trends are in regions that simultaneously present a large positive SST trend, and a negative MLD trend. This suggests that global warming is inducing a stratification over wide open ocean areas, with potentially significant consequences for the Earth’s thermal model, ocean dynamics and life in our oceans.

The Earth energy imbalance (EEI) at the top of the atmosphere is responsible for the accumulation of energy in the climate system. While necessary to better understand the Earth’s warming climate, measuring the EEI is challenging as it is a globally integrated variable whose variations are small (0.5-1 W.m-²) compared to the amount of energy entering and leaving the climate system (~ 340 W.m-²). Accuracies better than 0.1 W.m-² are needed to evaluate the temporal variations of the EEI at decadal and longer time-scales, characteristic of the response to anthropogenic and natural forcing.
Since the ocean absorbs about 90% of the excess energy stored by the Earth system, estimating the ocean heat content (OHC) provides an accurate proxy of the EEI. Here, the OHC is estimated at global and regional scales based on the combination of space altimetry and space gravimetry measurements. Global changes in the EEI are derived with realistic estimates of its uncertainty. The mean EEI value is estimated at +0.74±0.22 W m-² (90% confidence level) between August 2002 and August 2016 and this value is increasing at a rate of 0.02 ± 0.05 W.m-² (90% confidence level). Comparisons against independent estimates based on Argo data and on CERES measurements show good agreement within the error bars of the global mean and the time variations in EEI. On the other hand, discrepancies are also detected at inter-annual scales indicating that the current accuracy of EEI needs further improvement at these time scales. The space geodetic OHC-EEI product is freely available at https://doi.org/10.24400/527896/a01-2020.003 and is currently being improved, in particular work is underway to better estimate the OHC change at local scales over the Atlantic Ocean.

Live fuel moisture content (LFMC) describes the water content of the living vegetation (leaves, grass) relative to the dry matter content. LFMC is an important feature for the description of the vegetation status and often used in the description and prediction of fire ignitions and spread. LFMC has been widely estimated from medium resolution optical satellite imagery. However, passive microwave satellite observations are also sensitive to LFMC. The long temporal coverage and almost daily availability of these observations allows to observe vegetation properties over long time scales at high temporal resolution but broad scale. Vegetation optical depth (VOD) from passive microwave sensors describes the attenuation of the microwave emission due to the vegetation layer. VOD depends on the vegetation water content (VWC), the mass of vegetation and the structure of the vegetation layer. VWC is a function of above-ground biomass and LFMC and hence LFMC can be potentially estimated from VOD. Here we present a methodology to estimate a new large-scale LFMC dataset from passive microwave VOD and leaf area index (LAI) by using long-term satellite records since 1987.
In this study several model approaches describing the relation between LFMC, VOD and LAI were developed. These models were implemented and tested with three harmonised VOD datasets differing by used wavelengths for retrieval (Ku-band, X-band and C-band) of the vegetation optical depth climate archive (VODCA version 1) as well as with MODIS-LAI product MOD15A2H v006. For model calibration measurements of LFMC obtained from a global database of on-ground destructive LFMC observations (Globe-LFMC database) were utilised. The models were evaluated using spatial cross-validation with Globe-LFMC data as reference and afterwards compared with a MODIS-derived LFMC dataset of Australia and Europe. The evaluation were also conducted for specific vegetation forms. Across all models, the lowest RMSE and highest correlation between in-situ measured and derived LFMC were obtained for grasses, shrublands and broad-leaved deciduous trees. Lower performances were obtained for needle-leaved and broad-leaved evergreen trees. The best performing model approach (correlation of 0.4-0.8 and RMSE of 20%-120% LFMC depending on vegetation type) using Ku-band VOD as input was selected to estimate LFMC for all vegetation types at large scale. Large-scale global patterns of LFMC and the related uncertainties match expected spatial patterns and temporal dynamics. For example, anomalies in LFMC correspond well to known regional drought events. In order to extend the LFMC for the years before 2000, a new harmonised long-term VOD dataset (VODCA version 2) and several long-term LAI datasets were used as alternative input for the best performing model, specifically multisensory LAI from SPOT/PROBA-V/Sentinel-3 and GIMMS. The distinct derived LFMC datasets differing by the used LAI dataset as input were evaluated with global cross validation and compared with other LFMC datasets. These models provide a comparable performance to the original MODIS-based model and enables the estimation of long-term changes in LFMC at global scale. The use of new Sentinel-based datasets allows continuing this LFMC data record into the future which will be beneficial for analyses of vegetation water status and wild fire.

The ESA CCI Soil Moisture dataset (ESA CCI SM, Dorigo et al., 2017) is a well-established Essential Climate Variable dataset within the scientific climate community, providing global merged surface soil moisture that is highly regarded for its quality and long historical coverage, ranging from November 1978 to December 31st 2021. For example in 2020 alone, over 100+ scientific publications were recorded that included the use of the ESA CCI SM, and this number has been continuously growing in 2021. The dataset has already been used for more than 10 years as the baseline for the annual evaluation and interpretation of global SSM conditions as reported in the leading BAMS' "State of the Climate" reports (Van der Schalie et al., 2021).

The ESA CCI SM consists of three products: “ACTIVE” and “PASSIVE” were created by fusing scatterometer and radiometer soil moisture products, respectively; the “COMBINED” product is a blended product based on the former two data sets. Early 2022, the latest version of the dataset will be released, which is the ESA CCI SM version 7. Next to an extension of the dataset up to December 2021, there are several other improvements that have been implemented into the latest version of the products, of which we will give an overview.

Observations from two new passive microwave satellites have been added to the merged PASSIVE product, which are FengYun-3C and FengYun-3D, making a total of 14 satellites in the complete record. The temporal coverage of the PASSIVE dataset has been strongly improved further by the inclusion of daytime retrievals, which is based on a brightness temperature dataset that is calibrated to best match the nighttime/early-morning observations, before running the Land Parameter Retrieval Model (LPRM; Owe et al., 2008; Van der Schalie et al., 2017) for retrieving SM. LPRM has also been further fine-tuned, among others introducing a new barren soils flag based on passive microwave observations (follow-up of Van der Vliet et al., 2020) that can help remove false retrievals over dry and barren soils, like in deserts. The ACTIVE dataset has also seen the integration of a new satellite, i.e. ASCAT-C on MetOp-C, and its consistency has been improved by the new rescaling of ASCAT-B to ASCAT-A. ASCAT data within this project is generated through the EUMETSAT HSAF soil moisture project, (H-SAF, 2019). The CDF-Matching, as used in the merging of all three products, is updated with a dynamic CDF-parameter estimation that minimises inner-annual biases after scaling. The effect of these changes on the quality of the dataset are internally evaluated and its results will be briefly presented.

Besides data production, there are continued research efforts made to improve our understanding of the SM data and to ensure that new science and improvements are available for future product evolutions. Some examples:

(1) Assessing the possibility of using remote sensing data from an L-band sensor as the reference in order to remove model dependency in the final scaling of the COMBINED product (Madelon et al., in review).

(2) A global, long term (2002-2020), root-zone soil moisture product is being developed and evaluated, which focuses on the assimilation of both soil moisture and (microwave based) vegetation information.

(3) Develop and evaluate an operational methodology to retrieve a 1-km-scale spatial resolution soil moisture product from Sentinel data and assess the quality of this high-resolution soil moisture product.

Concerning future research directions, based on the obtained knowledge from these and related research activities, we have defined an updated roadmap for the ESA CCI SM datasets. Main improvements that are foreseen include a spatial resolution increase from 0.25° to 0.10°, sub-daily SM with ~6 hourly timesteps, improved (model-independent) scaling, improved uncertainty estimates and more.


References
Dorigo, W., Wagner, W., Albergel, C., Albrecht, F., Balsamo, G., Brocca, L., ... & Lecomte, P. (2017). ESA CCI Soil Moisture for improved Earth system understanding: State-of-the art and future directions. Remote Sensing of Environment, 203, 185-215.

H-SAF (2019) ASCAT Surface Soil Moisture Climate Data Record v5 12.5 km sampling - Metop (H115), EUMETSAT SAF on Support to Operational Hydrology and Water Management, DOI: 10.15770/EUM_SAF_H_0006.

Owe, M., de Jeu, R., & Holmes, T. (2008). Multisensor historical climatology of satellite‐derived global land surface moisture. Journal of Geophysical Research: Earth Surface, 113(F1).

Van der Schalie, R., de Jeu, R. A., Kerr, Y. H., Wigneron, J. P., Rodríguez-Fernández, N. J., Al-Yaari, A., ... & Drusch, M. (2017). The merging of radiative transfer based surface soil moisture data from SMOS and AMSR-E. Remote Sensing of Environment, 189, 180-193.

Van der Schalie, R., Scanlon, T.M., Preimesberger W., Pasik, A.J., Van der Vliet, M., Mösinger, L., Rodríguez-Fernández, N.J., Madelon, R., Hahn, S., Hirschi, M., Kidd, R., De Jeu, R.A.M., and Dorigo, W.A., 2021: Soil moisture [in “State of the Climate in 2020”]. Bull. Amer. Meteor., 102 (8), S67, https://doi.org/10.1175/BAMS-D-21-0098.1.

van der Vliet, M., van der Schalie, R., Rodriguez-Fernandez, N., Colliander, A., de Jeu, R., Preimesberger, W., Scanlon, T. & Dorigo, W. (2020). Reconciling Flagging Strategies for Multi-Sensor Satellite Soil Moisture Climate Data Records. Remote Sensing, 12(20), 3439.

Madelon, R., Rodriguez-Fernandez, N., Van der Schalie, R., Scanlon, T., Al Bitar, A., Kerr, Y. H., de Jeu, R., and Dorigo, W. (In review). Towards the Removal of Model Dependency in Soil Moisture Climate Data Records by Using an L-Band Scaling Reference. IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing.

One characteristic feature of the Arctic climate system is the net radiative energy loss to space throughout most of the year, which is balanced by sustained poleward oceanic and atmospheric heat transports. Atlantic water inflow into the Artic ocean is an important contributor to the energy budget of the high North, while the outflow of cold and dense waters into the North Atlantic feeds back to the Atlantic Meridional Overturning Circulation. Through this strong link, variability and trends in the heat budget of the Arctic Ocean have regional and global implications. Quantitative assessments of the involved processes are needed for improved process understanding and as reference data for model validation and development. However, data paucity has been a limiting factor in the past.
Here we draw on novel observational products such as satellite-based radiative fluxes, mooring-derived oceanic transports, and state-of-the-art reanalyses to assess the heat budget of the Arctic Mediterranean (the ocean bounded by Greenland-Scotland-Ridge, Davis Strait, and Bering Strait), with a focus on the oceanic component. Inter alia, we use an ensemble ocean reanalysis product provided by the Copernicus Marine Service (CMEMS), an atmospheric reanalysis (ERA5) provided by the Copernicus Climate Change Service (C3S), and satellite-based radiative fluxes from the NASA Clouds and Earth’s Radiant Energy Systems (CERES). Assessment of budget closure and validation with independent observation-based oceanic transport data demonstrates that the employed reanalysis products along with satellite data are useful tools for exploring variability and trends in the Arctic Mediterranean.
The 1993-2019 full-depth ocean heat uptake averaged over the Arctic Mediterranean amounts to 0.8 (0.4,1.0) Wm-2 (bracketed values give the minimum-to-maximum range of estimates based on the employed datasets). Ocean heat transport across the Greenland-Scotland-Ridge is found to be a pacemaker of ocean warming in the Arctic Mediterranean, with pronounced interannual and decadal variability. In addition to liquid ocean warming, energy used for sea ice melt amounts to 0.2 (0.2,0.3) Wm-2 when averaged over the Arctic Mediterranean. Together with ocean warming, this adds to a regional energy imbalance of ~1Wm-2, which is similar to the global ocean average warming reported in various other studies. Thus, there is no clear signal of Arctic amplification in the regionally averaged energy imbalance. The reason for this is the strong contrast in oceanic warming in ice-free compared to ice-covered parts of the Arctic Mediterranean. While oceanic warming in the ice-free regions, most notably the Nordic Seas, is substantially higher [1.9 (1.2,2.6) Wm-2] than the global average, the warming in the ice covered parts is relatively weak [0.2 (0.0,0.4) Wm-2] and thus masks the excessive warming in the ice-free areas.
The generally favourable agreement between observation-based and reanalysis-derived oceanic transport estimates builds confidence in the reanalysis products for usage in addition to observations in order to get a spatially and temporally more complete picture of the Arctic Mediterranean heat budget, as well as for near-real-time climate monitoring of this region.

Retrieving forest moisture content in western USA using a microwave-LiDAR synergy

David Chaparro1, Thomas Jagdhuber2,3, María Piles4, François Jonard5, Anke Fluhrer2,3, Mercè Vall-llossera1, Adriano Camps1, Carlos López-Martínez1, Andrew Feldman6, Dara Entekhabi7

1Universitat Politècnica de Catalunya, CommSensLab & IEEC/UPC, Jordi Girona 1-3, E-08034 Barcelona, Spain
2German Aerospace Center (DLR), Microwaves and Radar Institute, 82234 Weßling, Germany
3Institute of Geography, University of Augsburg, Alter Postweg 118, 86159 Augsburg, Germany
4Image Processing Laboratory, Universitat de València, Catedrático José Beltrán, 2, 46980, Paterna, València, Spain
5Geomatics Unit, SPHERES research unit, University of Liège (ULiege), Allée du Six Août 19, 4000 Liège, Belgium
6Biospheric Sciences Laboratory, NASA Goddard Space Flight Center, Greenbelt, MD, USA.
7Massachusetts Institute of Technology, Cambridge, MA02139, USA


Climate change is increasing drought frequency and intensity in many regions around the world [1]. This may reduce water availability for vegetation and, consequently, impact vegetation growth and thus terrestrial carbon uptake. It is therefore crucial to estimate globally and regularly the water status of vegetation. This enables modelling the water flux in the soil-plant-atmosphere continuum and understanding how it links the water and carbon cycles. In this way, passive microwave L-band missions are employed to retrieve the parameter vegetation optical depth (VOD), which is directly proportional to the vegetation water content (VWC). For this reason, the VOD is becoming a prevalent metric of recent research studying the soil-plant-atmosphere continuum [2]. Nevertheless, the VOD is jointly dependent on plant water, biomass and structure. Moreover, VOD-derived VWC estimates are obtained in units of mass by surface (kg/m2). Hence, although the VWC is useful to quantify the total water content and plant biomass, it is not providing information on the water content of plants relative to their biomass (i.e., water per unit biomass). To overcome this limitation, disentangling the VOD signal to isolate its water component, separate from biomass/structure components, is essential. The goal of this work is twofold: (i) to evaluate different approaches (i.e., algorithms and dielectric models) to unravel the water component of VOD in terms of gravimetric vegetation moisture (Mg; kg water/kg biomass), and (ii) to provide preliminary Mg results in woodlands located in the western part of the United States, including a validation with in situ stations. Our study spans from April 2015 to March 2020.

First, a model attenuation-based approach is applied to retrieve Mg. Following [3] and [4], Mg estimates can be obtained by minimizing the differences between radiometer-based and modeled VOD. The latter requires two inputs: the vegetation volume fraction (δ) derived from the Aquarius scatterometer as in [5] and vegetation height data derived from the ICESat-2 LiDAR sensor. It is important to highlight that these inputs are applied to model the canopy dielectric constant (εcan) and the vegetation dielectric constant (εveg) as part of the retrieving process (see [3] for a detailed explanation). Our results show that the estimation of εcan requires to use a random-needles inclusions model with a wider δ dynamic range (0 to 5·10-4) than that proposed in [5] (0 to 3.35·10-4) in order to avoid overestimation of Mg. Also, we will present a comparison among different vegetation dielectric models [6,7] and datasets [8] to show how Mg results vary as a function of the model/dataset chosen. We will finally apply the model proposed by Ulaby & El Rayes [6] to retrieve Mg.

Second, regional Mg retrievals will be presented for the study period in the western part of United States. This region has been chosen due to the availability of life fuel moisture content (LFMC) data from in situ measurements [9] that are applied for validation. Results show that the retrieved Mg ranges between 0.4 and 0.7 kg/kg in regions with homogeneous forest cover. This is similar to the dynamic range shown by the in-situ stations (0.4 to 0.6 kg/kg). Our current research is focused on: (i) the study of the Mg seasonality, (ii) the validation of the Mg time-series by a comparison between in situ and satellite estimates, and (iii) converting Mg to relative water content (RWC) estimates. Mg and RWC dynamics as well as an in-situ validation will be presented at the conference.

Acknowledgements
This work was supported by the “la Caixa” Foundation (ID 100010434) under Grant LCF/PR/MIT19/51840001 (MIT-Spain Seed Fund), and by the Spanish Ministry of Science and Innovation and the European Regional Development Fund through projects RTI2018-096765-A-I00 and PID2020-114623RB-C32 (funded by MCIN/AEI/10.13039/501100011033). Also, the authors are grateful to MIT for supporting this research with the MIT-Germany Seed Fund (D. Entekhabi, T. Jagdhuber).

References
[1] IPCC. (2013). Annex I: Atlas of global and regional climate projections. In: van Oldenborgh, et al. (Eds.) Climate Change 2013: The Physical Science Basis (pp. 1311-1393). Cambridge University Press.
[2] Feldman, A. F., Gianotti, D. J. S., Konings, A. G., McColl, K. A., Akbar, R., Salvucci, G. D., & Entekhabi, D. (2018). Moisture pulse-reserve in the soil-plant continuum observed across biomes. Nature Plants, 4(12), 1026-1033.
[3] Fink, A., et al. (2018). Estimating Gravimetric Moisture of Vegetation Using an Attenuation-Based Multi-Sensor Approach. In IGARSS 2018 (pp. 353-356). IEEE.
[4] Meyer, T., et al. Estimating Gravimetric Water Content of a Winter Wheat Field from L-Band Vegetation Optical Depth, Remote Sens. 2019, 11(20), 2353.
[5] Chaparro, D., Jagdhuber, T., Piles, M., Entekhabi, D., Jonard, F., Fluhrer, A., ... & Camps, A. (2021, July). Global L-band vegetation volume fraction estimates for modeling vegetation optical depth. In 2021 IEEE International Geoscience and Remote Sensing Symposium IGARSS (pp. 6399-6402). IEEE.
[6] Ulaby F.T., El-Rayes M.A. (1987): Microwave Dielectric Spectrum of Vegetation – Part II: Dual Dispersion Model. IEEE Transactions on Geoscience and Remote Sensing, Vol. Ge-25, No. 5, 550 – 557.
[7] Li, Z., Zeng, J., Chen, Q., & Bi, H. (2014). The measurement and model construction of complex permittivity of vegetation. Science China Earth Sciences, 57(4), 729-740.
[8] Koubaa, A., Perré, P., Hutcheon, R. M., & Lessard, J. (2008). Complex dielectric properties of the sapwood of aspen, white birch, yellow birch, and sugar maple. Drying Technology, 26(5), 568-578.
[9] Yebra, M., Scortechini, G., Badi, A., Beget, M. E., Boer, M. M., Bradstock, R., ... & Ustin, S. (2019). Globe-LFMC, a global plant water status database for vegetation ecophysiology and wildfire applications. Scientific data, 6(1), 1-8.