Ocean heat content (OHC) is a key variable to understand the role of the oceans in climate regulation (Johnson et al, 2013). Ocean heat transport and budget are fundamental aspects of some climate phenomena such as ENSO, the North Atlantic Oscillation, hurricanes, sea level rise, the global energy budget, and to constrain global warming scenarios.

Levitus et al. (2000) showed that OHC has a significant correlation with global warming and climate indexes, with seawater acting as a sink for atmospheric heat. They employed historical data and in situ data made available in the 90s by CLIVAR. Levitus et al (2001) reported that addition of new data into the existing datasets meant a significant variation of OHC trends respect to previous reports, and found a strong correlation with concentration of atmospheric anthropogenic gases. Levitus et al (2009), however, did a substantial correction over these results, as consequence of incorporating Argo data and performing a better quality control of the historical datasets. Levitus et al (2012) reviewed again the work to add a denser network of Argo data and further quality control. This was necessary after the detection of existing biases within a subset of Argo profiles (Willis et al, 2009), which caused an unexpected cooling of the oceans with respect previous works, for the period 2003-2005. Cheng et al (2014) also proved that incorporation of early Argo data was responsible for the significant OHC jump observed previously for the period 2001-2003, as result of the error sampling.

Hence, OHC estimations are very sensible to observational errors, including issues with the heterogeneity in both spatial distribution and data quality found in Argo measurements. Those are fundamental factors when estimating OHC from in situ data.

Ocean modelling has become a basic tool for the creation of the various scenarios covering climate change. Models are also significantly dependent of the sampling errors biases. Lymann et al (2009) showed the relevance of a careful attention to these aspects. Carton et al (2008) tested nine models and highlighted the difficulty of addressing OHC modelling when biases were presented in the data. As result of that, Palmer et al (2010) informed at OceanObs 2009 that analysis and estimation of OHC requires significant improvements in spatial and temporal observation of both Salinity and Temperature. Proposed spatial and temporal resolutions are only compatible with data acquisition from satellites.

Cheng et al (2017) proved that errors when representing small scales have a significant impact into the OHC obtained from models. The work noted that input in situ data quality control is essential, but subjective and based on knowledge of the ocean circulation and temperature/salinity patterns, which are not necessarily well known at the relevant scales. Authors mention that (sic) “it is difficult to fully address this issue because the Argo network does not fully capture mesoscale signals and lacks observations in coastal areas. High-resolution data, such as output from eddy-resolving ocean models, sea surface temperature, ocean colour, and high-resolution ASL data […] will be helpful in further investigations of the impact of mesoscale signals”. In other words, only real way to constrain OHC through ocean models require assimilation of Earth Observation (EO) data.

The CMEMS ocean model GLORYS12V1 consist on a global ocean reanalyses at eddy-resolving (1/12°) resolution describing ocean circulation, including forcing by satellite altimetry measurements of sea level. Hydrodynamics are based in NEMO3.1. It assimilates CORA4 database of in situ observations, and atmospheric forcing comes from assimilation of ERA-Interim data. To improve performance, GLORYS12V1 also assimilates Sea Surface Temperature (SST) from satellites (AVHRR-only). Ferry et al (2012) showed a considerable benefit in the performance of the model when assimilating SST from EO sources. However, GLORYS does not assimilate Sea Surface Salinity (SSS), an Essential Climate Variable and necessary for the estimation of OHC, as per the fundamental properties of seawater required for its computation (density and temperature).

The ESA Earth Explorer Soil Moisture and Ocean Salinity Mission (SMOS) was launched in 2009 and has capability to provide SSS data from space. SMOS has already accumulated almost 10 years of data, becoming into the largest time series of SSS from space and from a single instrument. The lack of assimilation of satellite-derived SSS measurements in ocean models is due, presumably, to the relative poor accuracy of the measurements with respect to in situ data. However, what satellites lack of accuracy, it can be compensated by data volume and spatial and temporal coverage. In addition, SMOS can definitively help to resolve frontal areas and key SSS structures that Argo floats cannot. Other cause of a lack of SSS assimilation is that S is assumed to have smooth spatial patterns and generally low temporal variability, so in principle, most models could resolve well salinity. Additionally, SSS is not the main driver of OHC, opposite to temperature. Whilst these assumptions can work at short time scales, they can impose strong deviations in OHC at larger temporal scales of interest for climate studies.

In this presentation, the authors have explored the relevance of assimilating EO SSS fields into ocean models, so to get more accurate estimations of OHC, with potential significant impact into OHC studies at climate scales. Superficial OHC (SOHC) estimations computed from the recent CMEMS GLORYS12V1 reanalysis ocean model are compared with those obtained just from satellite data. GLORYS12V1 assimilates altimetry and SST data from satellites, but not SSS. SOHC from satellite is produced by combining an SMOS-based L4 Fused SSS product from Barcelona Expert Centre (BEC) with OSTIA SST products. To include the corresponding ocean dynamics, SOHC from satellites have been complemented with AVISO L4 UV fields derived from altimetry. SOHC obtained from both sources have been analyzed, with special attention to SOHC transport and its evolution at climate-compatible time scales. Aiming to isolate better the impact of SMOS-derived SSS in SOHC estimates, a multiscale Singular Spectral Analysis has been carried out over both OHC datasets.

Results show that models assimilating only in situ salinity do not represent well the SOHC time and spatial scales, inducing significant differences in the SOHC estimations that could impact climate modelling scenarios. This is due to the non-linear effects corresponding to the physical coupling between T and S in seawater, which is not well represented only with in situ data. Hence, assimilation of EO-based SSS fields seems to be a necessary step to improve our capability to model OHC at climate scales and subsequently, for the consideration of the global warming projections.

References

Carton, J. A., & Santorelli, A. (2008). Global decadal upper-ocean heat content as viewed in nine analyses. Journal of Climate, 21(22), 6015-6035.

Cheng, L., & Zhu, J. (2014). Artifacts in variations of ocean heat content induced by the observation system changes. Geophysical Research Letters, 41(20), 7276-7283.

Cheng, L., Trenberth, K. E., Fasullo, J., Boyer, T., Abraham, J., & Zhu, J. (2017). Improved estimates of ocean heat content from 1960 to 2015. Science Advances, 3(3), e1601545.

Ferry, N., Parent, L., Garric, G., Bricaud, C., Testut, C. E., Le Galloudec, O., ... & Molines, J. M. (2012). GLORYS2V1 global ocean reanalysis of the altimetric era (1992–2009) at meso scale. Mercator Ocean–Quaterly Newsletter, 44.

Johnson, G. C., Lyman, J. M., Willis, J. K., Levitus, S., Boyer, T., Antonov, J., ... & Bindoff, N. L. (2013). Ocean heat content [in State of the Climate 2012]. Bulletin of the American Meteorological Society, 94(8), S50-S53.

Levitus, S., Antonov, J. I., Boyer, T. P., & Stephens, C. (2000). Warming of the world ocean. Science, 287(5461), 2225-2229.

Levitus, S., Antonov, J. I., Wang, J., Delworth, T. L., Dixon, K. W., & Broccoli, A. J. (2001). Anthropogenic warming of Earth's climate system. Science, 292(5515), 267-270.

Levitus, S., Antonov, J. I., Boyer, T. P., Locarnini, R. A., Garcia, H. E., & Mishonov, A. V. (2009). Global ocean heat content 1955–2008 in light of recently revealed instrumentation problems. Geophysical Research Letters, 36(7).

Levitus, S., Antonov, J. I., Boyer, T. P., Baranova, O. K., Garcia, H. E., Locarnini, R. A., ... & Zweng, M. M. (2012). World Ocean heat content and thermosteric sea level change (0–2000 m), 1955–2010. Geophysical Research Letters, 39(10).

Lyman, J. M., & Johnson, G. C. (2008). Estimating annual global upper-ocean heat content anomalies despite irregular in situ ocean sampling. Journal of Climate, 21(21), 5629-5641.

Palmer, M. D., Antonov, J., Barker, P., Bindoff, N. L., Boyer, T., Carson, M., ... & Gouretski, V. (2010). Future observations for monitoring global ocean heat content. In OceanObs’ 09: Sustained Ocean Observations and Information for Society (pp. 1-13).

Willis, J. K., Lyman, J. M., Johnson, G. C., & Gilson, J. (2009). In situ data biases and recent ocean heat content variability. Journal of Atmospheric and Oceanic Technology, 26(4), 846-852.

Accurate characterization of the Surface Radiative Energy Budget (SRB) of the Arctic is critically important for understanding its key role in climate regulation. Particularly during the cloudy Arctic summer, the robustness of our satellite-based SRB estimates depends on the robustness of the cloud property retrievals most relevant to radiative transfer calculations. Among these, we know that accurate estimates of the Cloud Optical Thickness (COT) and ice crystal/water droplet Effective Radius (Re) of Arctic clouds are a prerequisite for successful SRB calculations.

Yet we also know that COT & Re retrievals over bright surfaces such as snow or sea ice are very challenging, particularly for legacy instruments such as the Advanced Very High Resolution Radiometer (AVHRR). Yet, utilization of the multi-instrument AVHRR timeseries offers us the single best chance for obtaining a long SRB timeseries if the challenges can be overcome. Thus our research question: How do AVHRR-based COT & Re retrieval methodologies perform over the Arctic, and can we identify specific issues that set limitations for their retrieval accuracy?

We have spatiotemporally collocated a large multi-year dataset (N=270K+) of individual AVHRR-based COT/Re retrievals from the CM SAF CLARA-A2, NOAA APP-x and ESA Cloud-CCI datasets with corresponding observations from both CALIPSO/CALIOP and Cloudsat, based on the SNO method. We show a comparative analysis of the different COT retrievals and their relationship to the reference observations, taking into account the inherent limitations in each (e.g. CALIPSO’s COT saturation over thick clouds, Cloudsat’s insensitivity to very low clouds, etc.). We furthermore present the findings grouped by background surface type and cloud type.

The results show that the classic Nakajima-King COT/Re retrieval for AVHRR observations is particularly sensitive to the characterization of the background surface albedo and the correct delineation between bright ice surfaces and ice clouds. However, we also demonstrate that the sensitivity can be controlled and the impact of misclassifications limited through refinement of the retrieval protocol. As the Optimal Estimation method in the Cloud-CCI dataset provides rigorous pixel-level uncertainty estimates but is somewhat opaque for error attribution purposes, here we limit ourselves only to examining the veracity of the estimates and associated uncertainties against reference datasets and the other AVHRR-based cloud property retrievals.

The shortwave rays from the sun are scattered when they enter clouds and many of them return to space. The resulting cloud albedo thus tends to reduce the amount of solar radiation that reaches the surface of the Earth. This phenomenon known as cloud radiative effect may impact the surface net energy and thus sea ice extent and thickness. Here, we investigate this potential link over last 16 years (2001-2016) using Clouds and the Earth's Radiant Energy System (CERES) satellite data, passive microwave sea ice concentration data from the National Snow and Ice Data Center (NSIDC), and 23 state-of-art-climate models from Coupled Model Intercomparison Project phase 5 (CMIP5) archive. Our data-driven analysis reveals that polar sea ice and clouds interplay in a way that has not been previously reported. Indeed, clouds play an important role on the climate system through two main contrasting effects: (1) cooling the Earth by reflecting to space part of incoming solar radiation; (2) warming the surface by reducing the Earth’s loss of thermal energy to space. Recently, scientists have paid more attention to the warming role of clouds because of the acceleration of Arctic sea ice melting and because of recent studies that did not find any response of cloud cover fraction to reduced sea ice in summer. Here, we find a clear self-mitigation of sea ice melting via clouds radiative feedback signals in both observed and simulated datasets. The magnitude of this effect is substantial and is further enhanced in climate projections under RCP8.5 scenario. This finding challenges the classical view that minimizes the relationship between summer clouds and sea ice concentration, and demonstrates that less sea ice, even during summer, leads to thicker clouds that reduces the fraction of solar energy reaching the surface.

The understanding of this negative feedback in the polar system is crucial to predict the potential climate implication of sea ice loss. In fact, this mechanism may have significant implications on the speed of the polar sea ice retreat, which in turn has a broad impact on the climate system, on the Arctic environment and on potential economic activities in the Arctic regions (resources extraction, new transport pathways). We also show that the relationships derived from our observation-driven analysis match the projected changes in the Arctic and Antarctic surface energy budget in the median CMIP5 model ensemble. However, the large spread amongst climate models indicates that there is still considerable room to improve our reliability in the projections of the fate of the polar regions. A better representation of this feedback in climate model appears thus to be a necessary condition to go into that direction.

Land surface temperature (LST) provides valuable information for quantifying root-zone water availability, evapotranspiration (ET) and crop condition as well as providing useful information for constraining prognostic land surface models. This presentation describes a robust but relatively simple LST-based land surface model called the Two-Source Energy Balance (TSEB) model. The original TSEB modeling framework, published nearly 25 years ago, is one of the developments responsible for revitalizing LST remote sensing research and advancing the capabilities in the use of LST for generating reliable surface energy balance and ET mapping products from field to regional scales. The TSEB algorithms solve for the soil/substrate and canopy temperatures that achieves a balance in the radiation and turbulent heat flux exchange for the soil/substrate and vegetation elements coupled to the lower atmosphere. As a result, the TSEB modeling framework is applicable to a wide range of environmental and canopy cover conditions, which has been a limitation in many other LST-based energy balance approaches. In addition, a modeling system will be described called the Atmosphere-Land Exchange Inverse (ALEXI) that couples the TSEB scheme with an atmospheric boundary layer model in time-differencing mode to routinely map continental-scale daily ET at 5 to 10-km resolution using geostationary satellites. A related algorithm (DisALEXI) spatially disaggregates ALEXI output down to finer spatial resolutions using polar orbiting satellites such as Landsat, which provides pixel resolutions at the scale of human management activities affecting land use\land cover. This talk will focus on ALEXI/DisALEXI/TSEB applications to different environments where there is a critical need for reliable, spatially distributed water use information for management and monitoring activities. Study areas include semiarid and arid landscapes, grasslands, snow-covered and forested areas, rain-fed and irrigated commodity crops, and irrigated specialty crops. In each case, the value added by the thermal LST remote signal will be emphasized as a diagnostic constraint on the surface energy balance.

The Clouds and the Earth’s Radiant Energy System (CERES) provides a suite of data products for monitoring changes in Earth’s radiation budget at the top-of-atmosphere, surface and within the atmosphere together with the associated cloud, aerosol and meteorological properties. The goal is to produce a multidecadal record that is accurate and stable enough to be used for climate model evaluation and reducing the uncertainties in cloud feedback and aerosol forcing. CERES also produces low latency data products known as FLASHFlux, which are primarily intended to address the needs of the applied sciences and education communities. FLASHFlux is comprised of two data products: 1) an overpass swath Level 2 Single Scanner Footprint (SSF) data products separately for both Aqua and Terra observations, and 2) a daily Level 3 Time Interpolated and Spatially Averaged (TISA) 1o x 1o gridded data that combines Aqua and Terra observations. These Level 3 data products are now freely available as daily time series at user specified locations/regions via NASA’s GIS-enable POWER web portal at https://power.larc.nasa.gov. POWER’s focus is to provide satellite and modeled derived data supporting users in the long-term planning, optimization and monitoring of solar energy-related projects, building energy efficiency, and agricultural crop modeling. The clean energy decision support tool, entitled RETScreen Expert, directly connects to and pulls surface solar energy and meteorological quantities from POWER web portal. With nearly 600,000 registered users worldwide and availability in 36 languages, RETScreen Expert, utilizing FLASHFlux and other NASA data products, enables clean energy technology project feasibility, targeting and evaluation to support building energy efficiency improvements worldwide. This presentation provides an overview of FLASHFlux, evaluates FLASHFlux surface radiative fluxes by comparing them with surface radiative flux measurements, highlights specific examples of how these data products are used in clean energy technology projects, and discusses future plans for FLASHFlux data products.

To advance global and regional land surface models at different scales and improve their predictive capabilities, the long-term Global LAnd Surface Satellite (GLASS) products have been generated and released to the public free of charge(Liang et al. 2013a; Liang et al. 2013b). The GLASS products suite includes several surface radiation products, such as broadband shortwave albedo(Feng et al. 2016; Qu et al. 2016; Qu et al. 2014), broadband longwave emissivity(Cheng et al. 2017a; Cheng et al. 2016), downward shortwave radiation and photosynthetically active radiation(Yang et al. 2018; Zhang et al. 2014), land surface skin temperature, longwave net radiation(Cheng et al. 2017b), all-wave net radiation(Jia et al. 2018; Jiang et al. 2016), and evapotranspiration(Yao et al. 2014).

The GLASS products have some unique features, for example, 1) several products span about 35 years from 1981 (see Fig. 1); 2) all radiation products have the spatial resolution of 5km, much finer than the commonly used products (e.g., CERES, GEWEX) ; 3) all products are spatially and temporally continuous with no data gaps and missing values; and 4) those products have been demonstrated to be more accurate.

This presentation will provide the GLASS radiation products characteristics, validation, and some application examples on assessing the climate effects of human activities.

References
Cheng, J., Cheng, X., Liang, S., Niclòs, R., Nie, A., & Liu, Q. (2017a). A Lookup Table-Based Method for Estimating Sea Surface Hemispherical Broadband Emissivity Values (8–13.5 μm). Remote Sensing, 9, 245，doi:210.3390/rs9030245
Cheng, J., Liang, S., Verhoef, W., Shi, L., & Liu, Q. (2016). Estimating the Hemispherical Broadband Longwave Emissivity of Global Vegetated Surfaces Using a Radiative Transfer Model. IEEE Transactions on Geoscience and Remote Sensing, 54, 905-917
Cheng, J., Liang, S., Wang, W., & Guo, Y. (2017b). An efficient hybrid method for estimating clear-sky surface downward longwave radiation from MODIS data. Journal of Geophysical Research: Atmospheres, 122, 2616-2630
Feng, Y., Liu, Q., Qu, Y., & Liang, S. (2016). Estimation of the Ocean Water Albedo From Remote Sensing and Meteorological Reanalysis Data. IEEE Transactions on Geoscience and Remote Sensing, 54, 850-868
Jia, A., Liang, S., Jiang, B., Zhang, X., & Wang, G. (2018). Comprehensive Assessment of Global Surface Net Radiation Products and Uncertainty Analysis. Journal of Geophysical Research: Atmospheres, 123, 1970-1989
Jiang, B., Liang, S., Ma, H., Zhang, X., Xiao, Z., Zhao, X., Jia, K., Yao, Y., & Jia, A. (2016). GLASS Daytime All-Wave Net Radiation Product: Algorithm Development and Preliminary Validation. Remote Sensing, 8, 222
Liang, S., Zhang, X., Xiao, Z., Cheng, J., Liu, Q., & Zhao, X. (2013a). Global LAnd Surface Satellite (GLASS) products: Algorithms, validation and analysis. Springer
Liang, S., Zhao, X., Yuan, W., Liu, S., Cheng, X., Xiao, Z., Zhang, X., Liu, Q., Cheng, J., Tang, H., Qu, Y.H., Bo, Y., Qu, Y., Ren, H., Yu, K., & Townshend, J. (2013b). A Long-term Global LAnd Surface Satellite (GLASS) Dataset for Environmental Studies. International Journal of Digital Earth, 6, 5-33
Qu, Y., Liang, S., Liu, Q., Li, X., Feng, Y., & Liu, S. (2016). Estimating Arctic sea-ice shortwave albedo from MODIS data. Remote Sensing of Environment, 186, 32-46
Qu, Y., Liu, Q., Liang, S., Wang, L., Liu, N., & Liu, S. (2014). Improved direct-estimation algorithm for mapping daily land-surface broadband albedo from MODIS data. IEEE Transactions on Geoscience and Remote Sensing, 52, 907-919
Yang, L., Zhang, X., Liang, S., Yao, Y., Jia, K., & Jia, A. (2018). Estimating Surface Downward Shortwave Radiation over China Based on the Gradient Boosting Decision Tree Method. Remote Sensing, 10, 185
Yao, Y., Liang, S., Li, X., Hong, Y., Fisher, J.B., Zhang, N., Chen, J., Cheng, J., Zhao, S., Zhang, X., Jiang, B., Sun, L., Jia, K., Wang, K., Chen, Y., Mu, Q., & Feng, F. (2014). Bayesian multimodel estimation of global terrestrial latent heat flux from eddy covariance, meteorological, and satellite observations. Journal of Geophysical Research: Atmospheres, 119, 2013JD020864
Zhang, X., Liang, S., Zhou, G., Wu, H., & Zhao, X. (2014). Generating Global LAnd Surface Satellite incident shortwave radiation and photosynthetically active radiation products from multiple satellite data. Remote Sensing of Environment, 152, 318-332