EQC Quality Assessment: "Long-term global Eddy Kinetic Energy trends from satellite observations"
Dataset: satellite-sea-level-global [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Define tropical region (reindexing to avoid discontinuity at 180° when plotting) > ℹ If you want to know more > Key resources
---
* [[5]](https://doi.org/10.1038/s41598-025-06149-9) Barceló-Llull, B., Rosselló, P., Combes, V., Sánchez-Román, A., Pujol, M. I., and Pascual, A. (2025). Kuroshio Extension and Gulf Stream dominate the Eddy Kinetic Energy intensification observed in the global ocean. Scientific Reports, 15(1), 21754. doi: 10.1038/s41598-025-06149-9
* [CDS entry](https://cds.climate.copernicus.eu/datasets/satellite-sea-level-global?tab=overview): Sea level gridded data from satellite observations for the global ocean from 1993 to present.
* All the documentation for each version is available [here](https://cds.climate.copernicus.eu/datasets/satellite-sea-level-global?tab=documentation).

Further information:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/).


EQC Quality Assessment: "Long-term global Eddy Kinetic Energy trends from satellite observations"
Dataset: satellite-sea-level-global [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Define tropical region (reindexing to avoid discontinuity at 180° when plotting) > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1146/annurev.fluid.40.111406.102139) Ferrari, R., & Wunsch, C. (2009). Ocean circulation kinetic energy: Reservoirs, sources, and sinks. Annual Review of Fluid Mechanics, 41(1), 253-282. doi: 10.1146/annurev.fluid.40.111406.102139

[[2]](https://doi.org/10.1126/sciadv.aax7727) Hu, S., Sprintall, J., Guan, C., McPhaden, M. J., Wang, F., Hu, D., & Cai, W. (2020). Deep-reaching acceleration of global mean ocean circulation over the past two decades. Science advances, 6(6), eaax7727. doi: 10.1126/sciadv.aax7727

[[3]](https://doi.org/10.1175/1520-0485%281998%29028<0433:GVOTFB>2.0.CO;2) Chelton, D. B., DeSzoeke, R. A., Schlax, M. G., El Naggar, K., & Siwertz, N. (1998). Geographical variability of the first baroclinic Rossby radius of deformation. Journal of Physical Oceanography, 28(3), 433-460. doi: 10.1175/1520-0485(1998)028<0433:GVOTFB>2.0.CO;2

[[4]](https://doi.org/10.1029/2005GL024633) Pascual, A., Faugère, Y., Larnicol, G., & Le Traon, P. Y. (2006). Improved description of the ocean mesoscale variability by combining four satellite altimeters. Geophysical Research Letters, 33(2). doi: 10.1029/2005GL024633

[[5]](https://doi.org/10.1038/s41598-025-06149-9) Barceló-Llull, B., Rosselló, P., Combes, V., Sánchez-Román, A., Pujol, M. I., & Pascual, A. (2025). Kuroshio Extension and Gulf Stream dominate the Eddy Kinetic Energy intensification observed in the global ocean. Scientific Reports, 15(1), 21754. doi: 10.1038/s41598-025-06149-9

[[6]](https://doi.org/10.1029/2007GL030812) Chelton, D. B., Schlax, M. G., Samelson, R. M., & de Szoeke, R. A. (2007). Global observations of large oceanic eddies. Geophysical Research Letters, 34(15). doi: 10.1029/2007GL030812

[[7]](https://doi.org/10.1175/JTECH-D-17-0010.1) Le Vu, B., Stegner, A., & Arsouze, T. (2018). Angular momentum eddy detection and tracking algorithm (AMEDA) and its application to coastal eddy formation. Journal of Atmospheric and Oceanic Technology, 35(4), 739-762. doi: 10.1175/JTECH-D-17-0010.1

[[8]](https://doi.org/10.3389/fmars.2019.00703) Amores, A., Jordà, G., & Monserrat, S. (2019). Ocean eddies in the Mediterranean Sea from satellite altimetry: Sensitivity to satellite track location. Frontiers in Marine Science, 6, 703. doi: 10.3389/fmars.2019.00703

[[9]](https://doi.org/10.1016/j.asr.2021.01.022) Abdalla, S., Kolahchi, A. A., Ablain, M., Adusumilli, S., Bhowmick, S. A., Alou-Font, E., ... & Hamon, M. (2021). Altimetry for the future: Building on 25 years of progress. Advances in Space Research, 68(2), 319-363. doi: 10.1016/j.asr.2021.01.022


EQC Quality Assessment: "Long-term global Eddy Kinetic Energy trends from satellite observations"
Dataset: satellite-sea-level-global [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Define tropical region (reindexing to avoid discontinuity at 180° when plotting) > ℹ If you want to know more > References
---
). Altimetry for the future: Building on 25 years of progress. Advances in Space Research, 68(2), 319-363. doi: 10.1016/j.asr.2021.01.022

[[10]](https://doi.org/10.1016/j.asr.2011.09.033) Morrow, R., & Le Traon, P. Y. (2012). Recent advances in observing mesoscale ocean dynamics with satellite altimetry. Advances in Space Research, 50(8), 1062-1076. doi: 10.1016/j.asr.2011.09.033

[[11]](https://doi.org/10.5194/egusphere-2025-4651) Hargous, P., Combes, V., Barceló-Llull, B., & Pascual, A. (2025). Eddy Kinetic Energy Variability From 30 Years of Altimetry in the Mediterranean Sea. EGUsphere, 2025, 1-22. doi: 10.5194/egusphere-2025-4651

[[12]](https://doi.org/10.1038/s41558-021-01006-9) Martínez-Moreno, J., Hogg, A. M., England, M. H., Constantinou, N. C., Kiss, A. E., & Morrison, A. K. (2021). Global changes in oceanic mesoscale currents over the satellite altimetry record. Nature Climate Change, 11(5), 397-403. doi: 10.1038/s41558-021-01006-9

[[13]](https://doi.org/10.1007/s10712-023-09778-9) Morrow, R., Fu, L. L., Rio, M. H., Ray, R., Prandi, P., Le Traon, P. Y., & Benveniste, J. (2023). Ocean circulation from space. Surveys in Geophysics, 44(5), 1243-1286. doi: 10.1007/s10712-023-09778-9

[[14]](https://doi.org/10.1038/NCLIMATE2635) Watson, C. S., White, N. J., Church, J. A., King, M. A., Burgette, R. J., & Legresy, B. (2015). Unabated global mean sea-level rise over the satellite altimeter era. Nature Climate Change, 5(6), 565-568. doi: 10.1038/NCLIMATE2635

[[15]](https://doi.org/10.1029/2011JC007367) Arbic, B. K., Scott, R. B., Chelton, D. B., Richman, J. G., & Shriver, J. F. (2012). Effects of stencil width on surface ocean geostrophic velocity and vorticity estimation from gridded satellite altimeter data. Journal of Geophysical Research: Oceans, 117(C3). doi: 10.1029/2011JC007367

[[16]](https://doi.org/10.1029/1999JC900197) Lagerloef, G. S., Mitchum, G. T., Lukas, R. B., & Niiler, P. P. (1999). Tropical Pacific near‐surface currents estimated from altimeter, wind, and drifter data. Journal of Geophysical Research: Oceans, 104(C10), 23313-23326. doi: 10.1029/1999JC900197

[[17]](https://doi.org/10.1023/B:WARM.0000043140.61082.60) Yue, S., & Wang, C. (2004). The Mann-Kendall test modified by effective sample size to detect trend in serially correlated hydrological series. Water resources management, 18(3), 201-218. doi: 10.1023/B:WARM.0000043140.61082.60

[[18]](https://doi.org/10.1016/j.dynatmoce.2024.101497) Xiaodong, M., Lei, Z., Weishuai, X., Qinghong, L., & Maolin, L. (2024). Analysis and prediction of mesoscale eddy kinetic energy variations in the Kuroshio extension. Dynamics of Atmospheres and Oceans, 108, 101497. doi: 10.1016/j.dynatmoce.2024.101497


EQC Quality Assessment: "Long-term global Eddy Kinetic Energy trends from satellite observations"
Dataset: satellite-sea-level-global [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Define tropical region (reindexing to avoid discontinuity at 180° when plotting) > ℹ If you want to know more > References
---
cale eddy kinetic energy variations in the Kuroshio extension. Dynamics of Atmospheres and Oceans, 108, 101497. doi: 10.1016/j.dynatmoce.2024.101497

[[19]](https://doi.org/10.1007/s10236-023-01565-9) Yang, C., Yang, H., Chen, Z., Gan, B., Liu, Y., & Wu, L. (2023). Seasonal variability of eddy characteristics and energetics in the Kuroshio Extension. Ocean Dynamics, 73(8), 531-544. doi: 10.1007/s10236-023-01565-9

[[20]](https://doi.org/10.1002/2015RG000493) Buckley, M. W., & Marshall, J. (2016). Observations, inferences, and mechanisms of the Atlantic Meridional Overturning Circulation: A review. Reviews of Geophysics, 54(1), 5-63. doi: 10.1002/2015RG000493

[[21]](https://doi.org/10.1175/2010JCLI3310.1) Joyce, T. M., & Zhang, R. (2010). On the path of the Gulf Stream and the Atlantic meridional overturning circulation. Journal of Climate, 23(11), 3146-3154. doi: 10.1175/2010JCLI3310.1


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale
---
Production date: 27-08-2024

Produced by: IMEDEA (CSIC-UIB) [[webpage]](https://imedea.uib-csic.es/)


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > Quality assessment question
---
* **Can we use satellite-derived sea level anomalies to detect sea level trend and its spatial variability over the Mediterranean Sea?**

Sea level variation is an important indicator of climate change, as it integrates changes of almost all the components in the climate system [[1]](https://doi.org/10.5194/essd-10-281-2018). It specially impacts coastal societies and environments [[2]](https://doi.org/10.3389/fmars.2023.1150488). However, sea level change at the global scale is not uniform varying on a regional to local scale from global mean rates (e.g.,[[3]](https://doi.org/10.1016/j.gloplacha.2013.12.004), [[4]](https://doi.org/10.5194/esd-5-243-2014)). For instance, sea level trends in the Mediterranean differ from global mean sea level trends, mainly because of its semi-enclosed conditions [[5]](https://doi.org/10.1175/JCLI-D-13-00139.1). In addition,  within the Mediterranean, sea level trends can differ from the basin mean [[6]](https://doi.org/10.1007/s00382-016-3001-2) due to non-linear local oceanographic processes [[7]](https://doi.org/10.1016/j.gloplacha.2009.04.002). The Mediterranean Sea has been classified as one of the most susceptible climate change zones worldwide [[8]](https://doi.org/10.1029/2006GL025734). Thus, sea level rise  may significantly impact coastal populations and activities due, among others, to the local vertical land motion processes in the basin that can cause land subsidence amplifying the effects of rising sea levels in the region [[9]](https://doi.org/10.1029/2011JC007469), [[10]](https://doi.org/10.1016/j.jog.2019.05.007). This makes necessary the understanding of sea level change at the scale of the Mediterranean Sea [[2]](https://doi.org/10.3389/fmars.2023.1150488). In this context,  this notebook aims to evaluate the long-term sea level variability in the Mediterranean at both regional and sub-basin scales over the period 1993-2023. This assessment is based on the analysis conducted in [[2]](https://doi.org/10.3389/fmars.2023.1150488) from satellite sea level data.


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > Quality assessment statement
---
These are the key outcomes of this assessment

* This dataset provides statistically significant long-term sea level trends at regional scale in the Mediterranean Sea. The average sea level trend in the basin is 2.1 ± 0.5 mm/yr (95% conﬁdence interval) and presents a large spatial variability since 1993
* At sub-basin scale, the highest positive rates are observed over the Aegean Sea (3.1 ± 1.0 mm/yr) and Levantine Basin (2.6 ± 0.9 mm/yr), especially in regions where recurring gyres and eddies in the ocean circulation are present
* The sea level trends obtained and their variability at regional and sub-basin scales are consistent with previous studies based on both altimetry and in situ measurements [[6]](https://doi.org/10.1007/s00382-016-3001-2), [[11]](https://doi.org/10.1007/s00024-019-02156-w). Thus, this dataset can be used to estimate long-term sea level trends in the Mediterranean Sea
* However, there are regions where the signiﬁcance of the rate obtained from the trend analysis cannot be statistically conﬁrmed due to local dynamics, such as the inﬂuence of gyres/eddies and/or currents. These zones are: the Balearic Islands area in the Western Mediterranean, in a very large portion of the Ionian Peninsula, and also over Southern Crete.
```

![MedSea_currents](https://www.frontiersin.org/files/Articles/1150488/fmars-10-1150488-HTML/image_m/fmars-10-1150488-g001.jpg)

Figure 1 from [[2]](https://doi.org/10.3389/fmars.2023.1150488). Panel A shows the bathymetry (km) of the Mediterranean Sea with the sub-regions used to compute sea level trends according to [[17]](https://doi.org/10.1007/s00382-012-1369-1) and [[18]](https://doi.org/10.1016/j.gloplacha.2014.10.007). Panel B displays the mean velocity currents (m s⁻¹) in the basin at 15 m depth together with a schematic representation of the surface (black arrows) and intermediate (red arrows) circulation in the Mediterranean. The labels of currents are in italics white, while gyres are in bold cyan. Acronyms: AIS, Atlantic Ionian Stream; AMC, Asia Minor Current; EAG, Eastern Alboran Gyre; EIC, Eastern Ionian Current; IG, Ierapetra Gyre; LGG, Gulf of Lion Gyre; LPCC, Liguro-Provencal-Catalan Current; MIJ, Mid-Ionian Jet; MMGS, Mersa Matruh Gyre System; MMJ, Mid-Mediterranean Jet; NIG, Northern Ionian Gyre; PG, Pelops Gyre; RG, Rhodes Gyre; SAG, Southern Adriatic Gyre; SG, Sirte Gyre; SGS; Shikmona Gyre System; SSTC, Sicily Strait Tunisian Current; WAG, Western Alboran Gyre.


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > Methodology
---
This notebook provides an assessment of long-term sea level trends in the Mediterranean Sea from satellite-derived sea level observations at both regional and sub-basin scales. The Climate Data Store (CDS) catalogue entry used is the following:

* Sea level gridded data from satellite observations for the global ocean from 1993 to present [[CDS entry]](https://doi.org/10.24381/cds.4c328c78)

In a first step, these observations are corrected for glacial isostatic adjustment (GIA) using the geoid height estimates (dGeoid) from the ICE-6G_C (VM5a) model [[12]](https://doi.org/10.1002/2014JB011176) as a correction for the local vertical land movements. In a second step, a TOPEX-A instrumental drift correction, derived from altimetry and tide gauge global comparisons, is added to the sea level dataset to account for altimeter instrumental drift that influences the accuracy and uncertainty of the records between 1993-1998 (e.g., [[13]](https://doi.org/10.1038/nclimate2635)). Although this correction is computed for the global mean sea level, it can also be used at regional or local scales as a best available estimate. This is still preferable to not correcting at all, given that the regional variation of the instrumental drift is currently unknown [[2]](https://doi.org/10.3389/fmars.2023.1150488). In addition, the mean seasonal cycle is removed from the time series. Finally, a non-parametric Mann–Kendall test ([[14]](https://doi.org/10.2307/1907187), [[15]](https://psycnet.apa.org/record/1948-15040-000)) modified for auto-correlated data [[16]](https://doi.org/10.1016/S0022-1694%2897%2900125-X) is used to assess statistical significance of the trend at the 95% confidence interval. Sea level trends are computed over the period 1993-2023 at basin scale and for the following sub-regions: Western Mediterranean basin, Adriatic Sea, Aegean Sea, Ionian Sea, Levantine Basin, Southern Central Mediterranean, Southern Crete and Tyrrhenian Sea. They agree with the dynamical regions described in [[17]](https://doi.org/10.1007/s00382-012-1369-1), [[18]](https://doi.org/10.1016/j.gloplacha.2014.10.007)

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](results)**


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > Analysis and results
---
This notebook does not include code. It is a literature review based on the results reported in [[2]](https://doi.org/10.3389/fmars.2023.1150488)

(results)=


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > Analysis and results > 1. Results
---
The average sea level trend in the basin is 2.1 ± 0.5 mm/yr (95% conﬁdence interval) and presents a large spatial variability since 1993. The sea level trends for the different subregions investigated are the following: Adriatic Sea (2.6 ± 0.8 mm/yr); Aegean Sea (3.1 ± 1.0 mm/yr); Ionian Sea (1.6 ± 1.6 mm/yr); Levantine Basin (2.6 ± 0.9 mm/yr); Southern Central Mediterranean (2.1 ± 0.8 mm/yr); Southern Crete (0.3 ± 1.3 mm/yr); Tyrrhenian Sea (2.5 ± 0.6 mm/yr) and Western Mediterranean (1.8 ± 0.6 mm/yr). The following figure (Figure 5 from [[2]](https://doi.org/10.3389/fmars.2023.1150488)) shows the temporal evolution of the sea level in the Mediterranean Sea at basin and sub-basin scale. The time series of average annual total mean sea level from altimetry for the entire Mediterranean Sea (panel A, solid black line) highlights how the sea level is rising in the basin.

![sub-basin trends](https://www.frontiersin.org/files/Articles/1150488/fmars-10-1150488-HTML/image_m/fmars-10-1150488-g005.jpg)

Figure 5 from [[2]](https://doi.org/10.3389/fmars.2023.1150488). Annual total sea level time series from altimetry (black solid line) and steric (dashed yellow), thermosteric (dotted red), and halosteric (dash-dotted blue) components over different spatial scales (see also map at the top of figure): Mediterranean Sea (A), Adriatic Sea (B), Aegean Sea (C), Ionian Sea (D), Levantine Basin (E), Southern Central Mediterranean (F), Southern Crete (G), Tyrrhenian Sea (H) and Western Mediterranean Basin (I). Vertical lines denote significant changepoint in the related component (same color and line style) at a specific time. Trends refer to the period 1993–2019. Satellite observations provide total sea level, whereas the steric component of sea level due to the thermosteric and halosteric effects are computed in [[2]](https://doi.org/10.3389/fmars.2023.1150488) using ocean temperature and salinity profiles from a reanalysis product. This notebook focuses on the use of satellite observations to compute trends of total sea level. Thus, the steric component of sea level is not assessed.

The sea level trends obtained and their variability at regional and sub-basin scales are consistent with previous studies based on both satellite and in situ measurements ([[6]](https://doi.org/10.1007/s00382-016-3001-2), [[11]](https://doi.org/10.1007/s00024-019-02156-w)). Thus, this dataset can be used to estimate long-term sea level trends in the Mediterranean Sea.
The highest positive rates are observed over the Aegean (3.1 ± 1.0 mm/yr) and Levantine (2.6 ± 0.9 mm/yr), especially in regions where recurring gyres and eddies in the circulation are present (see Figure 1 from [[2]](https://doi.org/10.3389/fmars.2023.1150488)). However, there are zones of non-signiﬁcance in the sea level trend that reﬂect areas of critical passages of different water masses with related complex circulation at surface and intermediate depths. It is the case of the Balearic Islands sector in the Southern Central Mediterranean and the Southern Crete region. Also the Ionian Sea lacks a signiﬁcant trend over the whole period considered.


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > ℹ If you want to know more > Key resources
---
* CDS entry: [Sea level gridded data from satellite observations for the global ocean from 1993 to present](https://cds.climate.copernicus.eu/datasets/satellite-sea-level-global?tab=overview)
* Product User Guide and Specification (PUGS): [vDT2018](https://dast.copernicus-climate.eu/documents/satellite-sea-level/vDT2018/D3.SL.1-v1.2_PUGS_of_v1DT2018_SeaLevel_products_v2.4.pdf) and [vDT2021](https://confluence.ecmwf.int/pages/viewpage.action?pageId=333790908)

Further information:
* Ocean Indicator: [Sea Level](https://climate.copernicus.eu/climate-indicators/sea-level)
* Understanding Sea Level: [Sea Level Change by NASA](https://sealevel.nasa.gov/understanding-sea-level/global-sea-level/overview/)


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.5194/essd-10-281-2018) Legeais, J.-F., Ablain, M., Zawadzki, L., Zuo, H., Johannessen, J. A., Scharffenberg, M.G., et al. (2018). An improved and homogeneous altimeter sea level record from the ESA climate change initiative. Earth System Sci. Data 10 (1), 281–301. doi: 10.5194/essd-10-281-2018

[[2]](https://doi.org/10.3389/fmars.2023.1150488) Meli M, Camargo CML, Olivieri M, Slangen ABA and Romagnoli C (2023) Sea-level trend variability in the Mediterranean during the 1993–2019 period. Front. Mar. Sci. 10:1150488. doi: 10.3389/fmars.2023.1150488

[[3]](https://doi.org/10.1016/j.gloplacha.2013.12.004) Jevrejeva, S., Moore, J. C., Grinsted, A., Matthews, A. P., and Spada, G. (2014). Trends and acceleration in global and regional sea levels since 1807. Glob. Planet. Change 113, 11–22. doi: 10.1016/j.gloplacha.2013.12.004

[[4]](https://doi.org/10.5194/esd-5-243-2014) Slangen, A. B. A., van de Wal, R. S. W., Wada, Y. , and Vermeersen, L. L. A. (2014). Comparing tide gauge observations to regional patterns of sea-level change, (1961-2003). Earth Syst. Dynam. 5, 243–255. doi: 10.5194/esd-5-243-2014

[[5]](https://doi.org/10.1175/JCLI-D-13-00139.1) Pinardi, N., Bonaduce, A., Navarra, A., Dobricic, S., and Oddo, P. (2014). The mean Sea level equation and its application to the Mediterranean Sea. J. Clim. 27, 442–447. doi: 10.1175/JCLI-D-13-00139.1

[[6]](https://doi.org/10.1007/s00382-016-3001-2) Bonaduce, A., Pinardi, N., Oddo, P., Spada, G., and Larnicol, G. (2016). Sea-Level variability in the Mediterranean Sea from altimetry and tide gauges. Clim. Dyn. 47, 2851–2866. doi: 10.1007/s00382-016-3001-2

[[7]](https://doi.org/10.1016/j.gloplacha.2009.04.002) Vera, J. D. R., Criado-Aldeanueva, F., Garcı́a-Lafuente, J., and Soto-Navarro, F. J.(2009). A new insight on the decreasing sea level trend over the Ionian basin in the last decades. Global Planetary Change 68, 232–235. doi: 10.1016/j.gloplacha.2009.04.002

[[8]](https://doi.org/10.1029/2006GL025734) Giorgi, F. (2006). Climate change hot-spots. Geophys. Res. Lett. 33, L08707. doi: 10.1029/2006GL025734

[[9]](https://doi.org/10.1029/2011JC007469) Wöppelmann, G., and Marcos, M. (2012). Coastal sea level rise in southern Europe and the nonclimate contribution of vertical land motion. J. Geophys. Res. 117, C01007. doi: 10.1029/2011JC007469


EQC Quality Assessment: "Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale"
Dataset: satellite-sea-level-global [CDS]
Aspect: trend-assessment_q01  |  Category: Satellite_ECVs
Section: Regional sea level trend assessment in the Mediterranean Sea from Satellite (observations) at basin and sub-basin scale > ℹ If you want to know more > References
---
). Coastal sea level rise in southern Europe and the nonclimate contribution of vertical land motion. J. Geophys. Res. 117, C01007. doi: 10.1029/2011JC007469

[[10]](https://doi.org/10.1016/j.jog.2019.05.007) Mohamed, B., Mohamed, A., Alam El-Din, K., Nagy, H., and Elsherbiny, A. (2019a). Sea Level changes and vertical land motion from altimetry and tide gauges in the southern levantine basin. J. Geodyn. 128, 1–10. doi: 10.1016/j.jog.2019.05.007

[[11]](https://doi.org/10.1007/s00024-019-02156-w) Mohamed, B., Abdallah, A. M., Alam El-Din, K., Nagy, H., and Shaltout, M. (2019a). Inter-annual variability and trends of Sea level and Sea surface temperature in the Mediterranean Sea over the last 25 years. Pure Appl. Geophys. 176, 3787–3810. doi: 10.1007/s00024-019-02156-w

[[12]](https://doi.org/10.1002/2014JB011176) Peltier, W. R., Argus, D. F., and Drummond, R. (2015). Space geodesy constrains ice age terminal deglaciation: The global ICE-6G-C (VM5a) model. J. Geophys. Res. Solid Earth 120, 450–487. doi: 10.1002/2014JB011176

[[13]](https://doi.org/10.1038/nclimate2635) Watson, C. S., White, N. J., Church, J. A., King, M. A., Burgette, R. J., and Legresy, B.(2015). Unabated global mean sea-level rise over the satellite altimeter era. Nat. Clim. Change 5, 565–568. doi: 10.1038/nclimate2635

[[14]](https://doi.org/10.2307/1907187) Mann, H. B. (1945). Non-parametric tests against trend. Econometrica 13, 163–171. doi: 10.2307/1907187

[[15]](https://psycnet.apa.org/record/1948-15040-000) Kendall, M. G. (1975). Rank correlation methods. 4th ed. Ed. C. Grifﬁn (London, UK:Charles Grifﬁn)

[[16]](https://doi.org/10.1016/S0022-1694%2897%2900125-X) Hamed, K. H., and Rao, A. R. (1998). A modiﬁed Mann-Kendall trend test for autocorrelated data. J. Hydrol. 204, 182–196. doi: 10.1016/S0022-1694(97)00125-X

[[17]](https://doi.org/10.1007/s00382-012-1369-1) Carillo, A., Sannino, G., Artale, V., Ruti, P., Calmanti, S., and Dell'Aquila, A. (2012).
Steric sea level rise over the Mediterranean Sea: present climate and scenario
simulations. Clim. Dyn. 39, 2167–2184. doi: 10.1007/s00382-012-1369-1

[[18]](https://doi.org/10.1016/j.gloplacha.2014.10.007) Galassi, G., and Spada, G. (2014). Sea-Level rise in the Mediterranean Sea by 2050:
Roles of terrestrial ice melt, steric effects and glacial isostatic adjustment. Glob. Planet.
Change 123, 55–66. doi: 10.1016/j.gloplacha.2014.10.007


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone
---
Production date: 29-08-2024

Produced by: Antonio Sánchez-Román [IMEDEA (CSIC-UIB)](https://imedea.uib-csic.es/)


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Quality assessment questions
---
* **How accurate are satellite-derived sea level anomaly measurements in the coastal band of the European Seas?**
* **Can we use satellite-derived sea level anomalies to monitor the long-term evolution of sea level in the coastal band of the European Seas?**

Traditional altimetry has been often unable to produce meaningful signals of sea level change in the coastal zone due to the typically shallower water, bathymetric gradients and shoreline shapes, among others [[1]](https://doi.org/10.1007/s10712-019-09569-1), [[2]](https://doi.org/10.3390/rs12233970). Actually, satellite sea level products are not optimized for the coastal band promoting larger errors in the retrieval of sea level with regard to the open ocean. Nevertheless, the monitoring of sea level changes in coastal areas is an important societal issue [[3]](https://doi.org/10.3390/rs15030793). Thus, most of the efforts of the international community in the recent past have been focused on the research and development of techniques for coastal altimetry, with substantial support from space agencies such as the European Space Agency (ESA), the Centre National d’Études Spatiales (CNES) and other research institutions [[4]](https://doi.org/10.1007/s10712-016-9392-0). Efforts of the coastal altimetry community are aimed at extending the capabilities of current altimeters closer to the coastal zone. This includes the application of improved geophysical corrections, data recovery strategies near the coast using new editing criteria and high-frequency along-track sampling associated with updated quality control procedures [[1]](https://doi.org/10.1007/s10712-019-09569-1).
As a result, regional altimeter products focused on the coastal zone have been developed over the last years [[3]](https://doi.org/10.3390/rs15030793). These products are disseminated to both the international scientific community and society through regular specific coastal altimetry workshops.
Different metrics are used to assess the quality of satellite sea level data. They mainly consist in the analysis of the sea level anomaly (SLA) field at different steps of the processing, checks of the consistency of the SLA along the tracks of different altimeters and between gridded and along-track products, and comparisons with external in situ measurements [[5]](https://documentation.marine.copernicus.eu/QUID/CMEMS-SL-QUID-008-032-068.pdf).
In situ and satellite observations are complementary and are often assumed to observe the same signals [[6]](https://doi.org/10.1002/2015RG000502). In coastal areas, tide gauge measurements are commonly used (e.g., [[7]](https://doi.org/10.5194/os-15-1207-2019)).
In this context, this notebook aims to evaluate the performance of satellite global sea level gridded products in the retrieval of sea level in the coastal band of the European Seas over the period 1993-2020. This assessment is based on the analysis conducted in [[8]](https://doi.org/10.5194/os-19-793-2023) from satellite sea level data.


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Quality assessment statement
---
These are the key outcomes of this assessment

* The satellite dataset shows good agreement with tide gauge sea level observations in the European Seas, with a mean RMS difference of about 4.35 cm for the vDT2021 version. This difference shows spatial variability, likely due to inaccuracies in corrections applied to the satellite data and greater variability in non-tidal sea level changes.
* The satellite data indicates a consistent sea level rise across the study region, with a mean trend of about 3.13 mm/year. These trends appear more spatially consistent than those derived from tide gauges; however, differences between datasets suggest that regional estimates, particularly in coastal areas, should be interpreted with caution.
* Differences between vDT2021 and vDT2018 trend estimates are small (4.35 cm and 4.41 cm RMS difference with the tide gauges, respectively) and fall within the expected uncertainty range [[20]](https://doi.org/10.1038/s41597-020-00786-7), meaning no clear improvement can be established between the two versions. This assessment also does not include the newer vDT2024 dataset.
```

![consistency](https://raw.githubusercontent.com/Blanca-Fdez/c3s2-JN-assets/refs/heads/main/Assets/compTG_2sat_DT2021_tot_%25var_JN_BIEN.png)

Figure 1. Location of the tide gauges along the European coasts and the western Mediterranean Sea used for a comparison with satellite data. Colours indicate the mean square differences between the tide gauge and satellite sea level (vDT2021 version). Units are the percentage of the tide gauge variance.


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Methodology
---
We provide an assessment of the accuracy of satellite-derived sea level observations in the coastal zone of the European Seas over the period 1993-2020 through their comparison with in situ tide gauge records located along the European coasts. The Climate Data Store (CDS) catalogue entry used is the following:

* [Sea level gridded data from satellite observations for the global ocean from 1993 to present](https://doi.org/10.24381/cds.4c328c78)

On the time of podruction, two data versions are available in the CDS: vDT2018 and vDT2021. The improvements in the latest (vDT2021) dataset in the retrieval of sea level in the coastal band of the European Seas respect to the previous version (vDT2018) is investigated. SLA L4 satellite-derived from the CDS are provided on regular grids with nominal spatial resolutions of 0.25 x 0.25 degrees.

Other entries used for the assessment:

* [Tide gauge entry](http://www.marineinsitu.eu)
* [DAC entry](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/dynamic-atmospheric-correction.html)

Before they can be compared with satellite data, tide gauge measurements have to be processed to remove oceanographic signals whose temporal periods are not resolved by satellite sea level data, thus avoiding important aliasing errors [[1]](https://doi.org/10.1007/s10712-019-09569-1). In the following there are summarised the corrections applied to the tide gauge records:

* Correction of oceanic tidal effects by filtering tidal components (mainly diurnal and semidiurnal tidal constituents). The u-tide software [[12]](https://www.po.gso.uri.edu/codiga/utide/2011Codiga-UTide-Report.pdf) is used. The annual and semiannual frequencies, mainly driven by steric effect, are kept in the tidal residuals since they are included in the satellite data.
* Removal of the atmospheric induced sea level variability caused by the action of atmospheric pressure and wind [[13]](https://doi.org/10.1175/1520-0426%281999%29016<1279:EOGMAP>2.0.CO;2), [[14]](https://doi.org/10.1029/2002GL016473). The same dynamic atmospheric correction (DAC) as for satellite data is applied for the sake of consistency. The 6-hourly fields of this correction, available from the Archiving, Validation and Interpretation of Satellite Oceanographic Data (AVISO) website [[DAC entry]](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/dynamic-atmospheric-correction.html), are used. For each tide gauge site, the nearest grid point was selected and used to remove the atmospherically induced sea level from observations, previously converted into 6-hourly records [[15]](https://doi.org/10.1016/j.asr.2015.04.027).
* Correction of vertical movements associated with glacial isostatic adjustment (GIA). GIA is regarded as the only source of vertical land motions. Its effects are removed from the sea surface height (SSH) records, previously averaged into daily data, by using the Peltier mantle viscosity model (VM2) [[16]](https://doi.org/10.1029/98RG02638), [[17]](https://doi.org/10.1146/annurev.earth.32.082503.144359).


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Methodology
---
.1029/98RG02638), [[17]](https://doi.org/10.1146/annurev.earth.32.082503.144359).

The comparison method of satellite data with tide gauges consists in collocating both datasets in time and space. As a first step, a 15 day low-pass LOESS filter is applied to satellite and tide gauge time series to remove the high frequencies that cannot be resolved by the satellite data [[2]](https://doi.org/10.3390/rs12233970), [[18]](https://doi.org/10.1175/2008JTECHO556.1), [[19]](https://doi.org/10.5194/os-15-1091-2019). Then, the correlations between each tide gauge record and SLA time series corresponding to grid points within a radius of 1 degree around the tide gauge site is computed and the most correlated altimetry point is chosen. Only long-term monitoring stations with a lifetime of more than 3 years are used to allow statistical significance. Statistical analyses are performed using all available data pairs (satellite – tide gauge) in terms of the root mean square (rms) difference and variance of the time series.  Consistency between the satellite dataset and the tide gauge data is computed from Eq. (1) in [[2]](https://doi.org/10.3390/rs12233970). It is expressed as the mean square differences between both datasets, computed as the variance of the differences (satellite – tide gauge), in terms of percentage of the tide gauge variance.

The results are organised in the following steps, which are detailed in the sections below:

**[](section-1)**

**[](section-2)**

**[](section-3)**

**[](section-4)**


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Analysis and results
---
This notebook does not include code. It is a literature review based on the results reported in [[8]](https://doi.org/10.5194/os-19-793-2023).

(section-1)=


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Analysis and results > 1.  Performance of vDT2021 dataset in the retreival of sea level in the coastal band
---
The mean value (Table 1) of the rms difference between the vDT2021 satellite dataset and tide gauges is 4.35 cm, the variance of the differences (satellite – tide gauge) is 19 cm², whereas the variance of the satellite and tide gauge datasets are 79 cm² and 89 cm², respectively. Thus, this dataset properly captures the sea level in the coastal region and its variability when compared with in-situ tide gauge data. In addition, the mean distance between the location of the tide gauge and the corresponding satellite data with the highest correlation is 87 km. Overall, consistency (Figure 1) between the vDT2021 satellite dataset and the tide gauge data (expressed according to [[2]](https://doi.org/10.3390/rs12233970) as the mean square differences between both datasets in terms of percentage of the tide gauge variance) shows spatial variability with values lower than 5 % in the central and eastern parts of the Baltic Sea, emphasizing the precision of the correction applied to the satellite data in the basin that provides accurate sea level measurements when compared with tide gauges. However, they reach poorer values between 20 % and 30 % for stations located in the connection region with the North Atlantic Ocean. In addition, consistency values are between 20 % and 50 % for most of the stations located along the Atlantic shore; this includes the Strait of Gibraltar area. Such a large error could be related to imprecisions in the correction applied (i.e. ocean tide and DAC) to the satellite data [[2]](https://doi.org/10.3390/rs12233970), [[9]](https://doi.org/10.1029/2007jc004459), [[10]](https://doi.org/10.1016/j.jmarsys.2016.03.006) and to a larger non-tidal variance with respect to that found in the Baltic Sea [[11]](https://doi.org/10.1080/1755876X.2018.1489208). Finally, the Norwegian Sea shows consistency values better than those obtained for the Atlantic shore ranging between 15 % and 30 %, except for the south-western part of Norway where values between 5 % and 15% are found providing accurate sea level measurements when compared with tide gauges.

Table 1. Intercomparison of vDT2021 and vDT2018 satellite dataset (SAT) and tide gauge (TG) data from the European coasts in terms of the rms differences (cm) and variance (cm²) of the differences between the datasets. The mean distance between tide gauges and the most correlated gridded satellite points used in the computation is displayed. The common tide gauge stations for the vDT2021 and vDT2018 datasets were used. Finally, the improvement (%) of the vDT2021 dataset with respect to the previous vDT2018 version is also displayed.

|                | vDT2021  | vDT2018 | vDT2021 improvement |
| :- | :-: | :-: | :-: |
| rms diff. (cm) | 4.35 | 4.41 | 1% |
| var. TG (cm²) | 89 | 89 | - |
| var. SAT (cm²) | 79 | 78 | no improvement |
| var. TG-SAT (cm²) | 19 | 19 | no improvement |
| distance TG (km) | 87 | 90 | 3% |

(section-2)=


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Analysis and results > 2. Improvement in vDT2021 version over vDT2018 dataset
---
The mean value of the rms difference between the vDT2018 satellite dataset and tide gauges (Table 1) is 4.41 cm, the variance of the differences (satellite – tide gauge) is 19 cm² , and the mean distance between the location of the tide gauge and the corresponding satellite data is 90 km. If these results are compared with those reported above for the comparison using the vDT2021 satellite dataset, it can be observed that the latter only improves the previous vDT2018 version in terms of the errors with tide gauges that are reduced by 1 % and the mean distance between the most correlated satellite point and tide gauges, reduced by 3 %, whereas the variance of the differences between the datasets is quite similar.
This fact is reflected in the spatial distribution of the differences between vDT2021 and vDT2018 consistency with tide gauges (Figure 2). A better performance of the vDT2021 dataset is obtained at the connection region between the Baltic Sea and the eastern North Atlantic Ocean and in parts of the Atlantic shore (coasts of United Kingdom and France). There is a degradation of the vDT2021 dataset in most of the stations located in the western Mediterranean Sea and the southern coasts of Spain; and in some stations located
on the coasts of France, England and Ireland. Also, negligible discrepancies between the two versions are found in the Baltic Sea. A degradation of the vDT2021 dataset is observed in most of the stations located in both the North-West Shelf (NWS) area (southern coasts of the North Sea, see Figure 2 in [[2]](https://doi.org/10.3390/rs12233970) for the location of this region) and the Norwegian Sea.

![vardiff_new](https://raw.githubusercontent.com/Blanca-Fdez/c3s2-JN-assets/refs/heads/main/Assets/compTG_2sat_DT21_DT18_tot_%25vardiff_JN_BIEN-1.png?)

Figure 2. Spatial distribution of the differences (vDT2021 minus vDT2018) for the mean square differences between the tide gauge and satellite sea level data. Units are the percentage of the tidal variance. Blue colours denote an improvement in the vDT2021 dataset, whilst red colours indicate its degradation with respect to the vDT2018 version.

(section-3)=


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Analysis and results > 3. Performance of vDT2021 dataset in monitoring the long-term evolution of sea level
---
The analyses described above were repeated for a specific time period spanning 20 years: from January 2000 to December 2019. Tide gauge time series with valid data within such a time interval were considered, allowing of the intercomparison satellite  – tide gauges for long-term time series with the same length. Moreover, only tide gauge time series with at least 99 % of valid data were used in order to allow the analysis of linear trends. This reduced the original tide gauge dataset to a subset of 27 stations mainly located in the northern half of the Baltic Sea (70 % of stations) with sparse stations distributed along the coasts of France and Spain (Figure 3).
Linear trends based on monthly observations at each tide gauge site computed from the vDT2021 satellite dataset (Figure 3, upper panel) show a homogeneous spatial pattern with overall values ranging from 2.60 to 3.80 mm yr⁻¹ in the Baltic and Mediterranean seas and between 2.40 and 3.40 mm yr⁻¹ along the North Atlantic European coasts. This promotes a mean trend for the whole domain of 3.13 mm yr⁻¹. Linear trends computed from tide gauges (Figure 3, lower panel) exhibit a more heterogeneous spatial pattern with values ranging between less than 1 mm yr⁻¹ for some stations located in the Baltic Sea and 5.06 mm yr⁻¹ for a station located in the western Mediterranean Sea. However, most of the tide gauge stations present trend values ranging from 1.30 to 3 mm yr⁻¹ with a mean trend for the whole domain of 1.96 mm yr⁻¹.

![trend_TG_SAT_new](https://raw.githubusercontent.com/Blanca-Fdez/c3s2-JN-assets/refs/heads/main/Assets/trends_TG_vDT2021_JN_BIEN-2.png?)

Figure 3. Spatial distribution of linear trends (mm yr⁻¹) for satellite (a) and tide gauge (b) data computed from monthly averaged data for the 20-year time period from January 2000 to December 2019. The vDT2021 dataset has been used.

Trends computed from vDT2021 dataset are on average around 1.2 mm yr⁻¹ larger than those obtained from tide gauges. These discrepancies could be attributed to the heterogeneous distribution of both datasets and also the crustal land uplift due to postglacial rebound resulting from the last glacial age affecting the Baltic Basin, where most of the tide gauge stations are located. This translates into vDT2021 satellite measurements not being accurate enough in the coastal zone. These results provide further evidence, if needed, of the European seas coastal sea level rise, including the westernmost part of the Mediterranean Sea.
Overall, linear trend differences (satellite – tide gauge) for the vDT2021 dataset varying, in absolute value, from 0.03 to 2.65 mm yr⁻¹, with an average of 1.40 mm yr⁻¹ are obtained (Figure 4). These discrepancies are lower than 1.5 mm yr⁻¹ on average and corroborate the agreement and complementarity of the two techniques to measure sea level variability in the coastal zone. However, Figure 4 displays an overall overestimation of trends from vDT2021 satellite data in the whole domain except for three tide gauge sites located on the Atlantic Spanish coast, the eastern side of the Baltic Sea, and the western Mediterranean Sea. The differences in trend could be attributed to the aforementioned factors rendering satellite measurements not accurate enough in the coastal zone.

![trend_diff_DT21_TG](https://github.com/antonjes/C3S2/blob/main/trend_diff_vDT2021_TG_JN.png?raw=true)

Figure 4. Spatial distribution of the differences in linear trends (mm yr⁻¹) between the vDT2021 satellite and tide gauge sea level computed for the 20-year time period from January 2000 to December 2019. Blue (red) colours denote a larger (lower) satellite linear trend.


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Analysis and results > 3. Performance of vDT2021 dataset in monitoring the long-term evolution of sea level
---
2021 satellite and tide gauge sea level computed for the 20-year time period from January 2000 to December 2019. Blue (red) colours denote a larger (lower) satellite linear trend.

Linear trends computed from the vDT2018 dataset (figure not shown) exhibit quite a similar spatial pattern to that reported for the vDT2021 version with overall values ranging from 2.40 to 3.60 mm yr⁻¹ in the Baltic and Mediterranean seas and between 2.10 and 2.85 mm yr⁻¹ along the North Atlantic European coasts. This promotes a mean trend for the whole domain of 2.85 mm yr⁻¹. Thus, some differences in range are observed between the two versions, with a lower variability observed for the vDT2018 dataset.
This fact has an impact on the spatial distribution of the differences between the vDT2021 and vDT2018 versions (Figure 5). It depicts a homogeneous spatial pattern with overall larger trends for the vDT2021 dataset except for a tide gauge station located in the connection region between the Baltic Sea and the eastern North Atlantic Ocean. Figure 5 also reveals, when making a comparison with linear trends from tide gauges, that the vDT2021 dataset presents larger differences with tide gauges with respect to the vDT2018 version in the whole domain. Nevertheless, conclusions should be drawn with caution, as these differences are not statistically significant and fall within the margin of 0.8 mm yr⁻¹ error of local sea level trends in Europe obtained by [[20]](https://doi.org/10.1038/s41597-020-00786-7).

![trend_diff_DT21_DT18](https://github.com/antonjes/C3S2/blob/main/trend_diff_vDT2021_vDT2018.png?raw=true)

Figure 5. Panel b of Figure 5 from [[8]](https://doi.org/10.5194/os-19-793-2023). Spatial distribution of the differences (vDT2021 minus vDT2018 satellite dataset) for linear trends (mm yr⁻¹) for satellite data. Monthly averaged data for the 20-year time period from January 2000 to December 2019 have been used. Blue (red) colours denote lower (larger) trends for the vDT2021 dataset.

(section-4)=


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > Analysis and results > 4. Conclusions
---
The mean value of the rms difference between the vDT2021 satellite dataset and tide gauges along the coastal band of the European Seas is 4.35 cm, the variance of the differences (satellite – tide gauge) is 19 cm² whilst the variance of the satellite and tide gauge datasets are 89 cm² and 79 cm², respectively. Thus, this dataset properly captures the sea level in the coastal region of the European Seas and its variability when compared with in-situ tide gauge data. In addition, the mean distance between the location of the tide gauges and the corresponding satellite data with the highest correlation is 87 km.

The mean value of the rms difference between the vDT2018 version and tide gauges is 4.41 cm, the variance of the differences (altimetry–tide gauge) is 19 cm², and the mean distance between the location of the tide gauge and the corresponding satellite data is 90 km. Thus, compared to its predecessor, vDT2018, the vDT201 dataset shows only slight improvements. The average difference with tide gauges decreased by just 1%, and the mean distance between tide gauges and the most correlated satellite data points was reduced by 3% (from 90 km to 87 km), whereas the variance of the differences between the datasets is quite similar.

Consistency between the satellite dataset vDT2021 and the tide gauge data shows spatial variability. In the central and eastern Baltic Sea, differences are under 5%, confirming the accuracy of the satellite corrections applied in this area. However, in regions connected to the North Atlantic, such as the Strait of Gibraltar and along the Atlantic shore, discrepancies increase, ranging between 20% and 50%. This could be due to inaccuracies in corrections (i.e. ocean tide and DAC) applied to the satellite data, as well as greater variability in non-tidal sea level changes. The Norwegian Sea shows better results than those obtained for the Atlantic shore with consistency values ranging between 15 % and 30 %, except for the south-western part of Norway where even better values ranging between 5 % and 15% are obtained providing accurate sea level measurements when compared with tide gauges.

Linear trends derived from the vDT2021 satellite dataset indicate a consistent sea level rise along the European coasts and the western Mediterranean, with values ranging from 2.60 to 3.80 mm yr⁻¹ and a mean trend for the whole domain of 3.13 mm yr⁻¹. In contrast, trends computed from tide gauge records display a more heterogeneous spatial pattern, with values ranging between 1.30 and 3 mm yr⁻¹ and a mean trend for the whole domain of 1.96 mm yr⁻¹. These results provide further evidence of the European seas coastal sea level rise, including the westernmost part of the Mediterranean Sea. Overall, linear trend differences (satellite – tide gauge) for the vDT2021 dataset vary, in absolute value, from 0.03 to 2.65 mm yr⁻¹, with an average of 1.40 mm yr⁻¹. These low discrepancies corroborate the agreement and complementarity of the two techniques to measure sea level variability in the coastal zone. For vDT2018 dataset, the mean trend for the whole domain (2.85 mm yr⁻¹) shows smaller differences with respect to the tide gauges than vDT2021. This suggests that the vDT2021 satellite measurements contain nearshore errors that may affect the accuracy of regional mean sea level estimates in coastal zones, leading to discrepancies with tide gauge trends. For studies focused on long-term trends, [[20]](https://doi.org/10.1038/s41597-020-00786-7) shows that over the altimetric period (1993–2019), the average uncertainty of local sea level trends in Europe was 0.8 mm yr⁻¹. In this study, the differences between the vDT2021 and vDT2018 trends are generally < 0.8 mm yr⁻¹. Therefore, conclusions should be drawn with caution, because these differences are not statistically significant and fall within the margin of error obtained by [[20]](https://doi.org/10.1038/s41597-020-00786-7). Consequently, we cannot say with complete certainty which version is better.

This assessment is mostly based on published material comparing versions vDT2018 and vDT2021, and it does not include the newest version vDT2024.


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > ℹ If you want to know more > Key resources
---
* CDS entry: ["Sea level gridded data from satellite observations for the global ocean from 1993 to present"](https://cds.climate.copernicus.eu/datasets/satellite-sea-level-global?tab=overview)
* Product User Guide and Specification (PUGS): [vDT2018](https://dast.copernicus-climate.eu/documents/satellite-sea-level/vDT2018/D3.SL.1-v1.2_PUGS_of_v1DT2018_SeaLevel_products_v2.4.pdf) and [vDT2021](https://confluence.ecmwf.int/pages/viewpage.action?pageId=333790908)
* [Tide gauge dataset](http://www.marineinsitu.eu)
* [Dynamic Atmospheric Correction dataset (DAC)](https://www.aviso.altimetry.fr/en/data/products/auxiliary-products/dynamic-atmospheric-correction.html)
* Copernicus climate change indicators: [sea level](https://climate.copernicus.eu/climate-indicators/sea-level)


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1007/s10712-019-09569-1) Vignudelli, S., Birol, F., Benveniste, J., Fu, L.-L., Picot, N., Raynal, M., and Roinard, H.: Satellite Altimetry Measurements of Sea Level in the Coastal Zone, Surv. Geophys., 40, 1319–1349, https://doi.org/10.1007/s10712-019-09569-1, 2019

[[2]](https://doi.org/10.3390/rs12233970) Sánchez-Román, A., Pascual, A., Pujol, M.-I., Taburet, G., Marcos, M., and Faugère, Y.: Assessment of DUACS Sentinel-3A Altimetry Data in the Coastal Band of the European Seas: Comparison with Tide Gauge Measurements, Remote Sens., 12, 3970, https://doi.org/10.3390/rs12233970, 2020

[[3]](https://doi.org/10.3390/rs15030793) Pujol, M.-I., Dupuy, S., Vergara, O., Sánchez Román, A., Faugère, Y., Prandi, P., Dabat, M.-L., Dagneaux, Q., Lievin, M., Cadier, E., Dibarboure, G., and Picot, N.: Refining the Resolution of DUACS Along-Track Level-3 Sea Level Altimetry Products, Remote Sens., 15, 1–30, https://doi.org/10.3390/rs15030793, 2023

[[4]](https://doi.org/10.1007/s10712-016-9392-0) Cipollini, P., Calafat, F.-M., Jevrejeva, S., Melet, A., and Prandi, P.: Monitoring sea level in the coastal zone with satellite altimetry and tide gauges, Surv. Geophys., 38, 33–57, https://doi.org/10.1007/s10712-016-9392-0, 2017

[[5]](https://documentation.marine.copernicus.eu/QUID/CMEMS-SL-QUID-008-032-068.pdf) CMEMS-SL-QUID: QUID document for Sea Level TAC DUACS products CMEMS-SL-QUID-008-032-068, https://documentation.marine.copernicus.eu/QUID/CMEMS-SL-QUID-008-032-068.pdf , 2022

[[6]](https://doi.org/10.1002/2015RG000502) Wöppelmann, G. and Marcos, M.: Vertical land motion as a key to understanding sea level change and variability, Rev. Geophys., 54, 64–92, https://doi.org/10.1002/2015RG000502, 2016

[[7]](https://doi.org/10.5194/os-15-1207-2019) Taburet, G., Sanchez-Roman, A., Ballarotta, M., Pujol, M.-I., Legeais, J.-F., Fournier, F., Faugere, Y., and Dibarboure, G.: DUACS DT2018: 25 years of reprocessed sea level altimetry products, Ocean Sci., 15, 1207–1224, https://doi.org/10.5194/os-15-1207-2019, 2019

[[8]](https://doi.org/10.5194/os-19-793-2023) Sánchez-Román, A., Pujol, M. I., Faugère, Y., and Pascual, A.: DUACS DT2021 reprocessed altimetry improves sea level retrieval in the coastal band of the European seas, Ocean Sci., 19, 793–809, https://doi.org/10.5194/os-19-793-2023, 2023

[[9]](https://doi.org/10.1029/2007jc004459) Pascual, A., Marcos, M., and Gomis, D.: Comparing the sea level response to pressure and wind forcing of two barotropic models: validation with tide gauge and altimetry data, J. Geophys. Res., 113, C07011, https://doi.org/10.1029/2007jc004459, 2008


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > ℹ If you want to know more > References
---
forcing of two barotropic models: validation with tide gauge and altimetry data, J. Geophys. Res., 113, C07011, https://doi.org/10.1029/2007jc004459, 2008

[[10]](https://doi.org/10.1016/j.jmarsys.2016.03.006) Laíz, I., Tejedor, B., Gómez-Enri, J., Aboitiz, A., and Villares, P.: Contributions to the sea level seasonal cycle within the Gulf of Cadiz (Southwestern Iberian Peninsula), J. Mar. Syst., 159, 55– 66, https://doi.org/10.1016/j.jmarsys.2016.03.006, 2016

[[11]](https://doi.org/10.1080/1755876X.2018.1489208) Von Schuckmann, K., Le Traon, P.-Y., Smith, N., et al.: Copernicus Marine Service Ocean State Report, J. Oper. Ocean., 11, S1– S142, https://doi.org/10.1080/1755876X.2018.1489208, 2018

[[12]](https://www.po.gso.uri.edu/codiga/utide/2011Codiga-UTide-Report.pdf) Codiga, D. L.: Unified Tidal Analysis and Prediction Using the UTide Matlab Functions, Technical Report 2011-01, Graduate School of Oceanography, University of Rhode Island, Narragansett, RI, 59 pp., https://www.po.gso.uri.edu/codiga/utide/2011Codiga-UTide-Report.pdf , 2011

[[13]](https://doi.org/10.1175/1520-0426%281999%29016<1279:EOGMAP>2.0.CO;2) Dorandeu, J. and Le Traon, P.-Y.: Effects of global mean atmospheric pressure variations on mean sea level changes from Topex/Poseidon, J. Atmos. Ocean. Technol., 16, 1279–1283, [](https://doi.org/10.1175/1520-0426%281999%29016<1279:EOGMAP>2.0.CO;2), 1999

[[14]](https://doi.org/10.1029/2002GL016473) Carrère, L. and Lyard, F.: Modeling the barotropic response of the global ocean to atmospheric wind and pressure forcing–comparisons with observations, Geophys. Res. Lett., 30, 1275, https://doi.org/10.1029/2002GL016473, 2003

[[15]](https://doi.org/10.1016/j.asr.2015.04.027) Marcos, M., Pascual, A., and Pujol, I.: Improved satellite altimeter mapped sea level anomalies in the Mediterranean Sea: A comparison with tide gauges, Adv. Space Res., 56, 596–604, https://doi.org/10.1016/j.asr.2015.04.027, 2015

[[16]](https://doi.org/10.1029/98RG02638) Peltier, W. R.: Postglacial Variations in the Level of the Sea: Implications for Climate Dynamics and Solid-Earth Geophysics, Rev. Geophys., 36, 603–689, https://doi.org/10.1029/98RG02638, 1998

[[17]](https://doi.org/10.1146/annurev.earth.32.082503.144359) Peltier, W. R.: Global Glacial Isostasy and the Surface of the Ice-Age Earth: The ICE-5G(VM2) model and GRACE, Ann. Rev. Earth. Planet. Sci., 32, 111–149, https://doi.org/10.1146/annurev.earth.32.082503.144359, 2004

[[18]](https://doi.org/10.1175/2008JTECHO556.1) Pascual, A., Boone, C., Larnicol, G., and Le Traon, P. Y.: On the quality of real-time altimeter gridded fields: comparison with in situ data, J. Atmos. Ocean. Technol., 26, 556–569, https://doi.org/10.1175/2008JTECHO556.1, 2009


EQC Quality Assessment: "Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone"
Dataset: satellite-sea-level-global [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Sea level measurements accuracy assessment from Satellite (observations) in the coastal zone > ℹ If you want to know more > References
---
fields: comparison with in situ data, J. Atmos. Ocean. Technol., 26, 556–569, https://doi.org/10.1175/2008JTECHO556.1, 2009

[[19]](https://doi.org/10.5194/os-15-1091-2019) Ballarotta, M., Ubelmann, C., Pujol, M.-I., Taburet, G., Fournier, F., Legeais, J.-F., Faugère, Y., Delepoulle, A., Chelton, D., Dibarboure, G., and Picot, N.: On the resolutions of ocean altimetry maps, Ocean Sci., 15, 1091–1109, https://doi.org/10.5194/os-15-1091-2019, 2019

[[20]](https://doi.org/10.1038/s41597-020-00786-7) Prandi, P., Meyssignac, B., Ablain, M., Spada, G., Ribes, A., and Benveniste, J.: Local sea level trends, accelerations and uncertainties over 1993–2019. Scientific Data, 8(1), 1, https://doi.org/10.1038/s41597-020-00786-7, 2021


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Quality assessment question(s)
---
* **Do the ESA CCI SST L4 v2.1 and GHRSST Multi-Product Ensemble (GMPE) SST datasets provide consistent representations of SST climatology and its variability?**

We aim here to evaluate the consistency and representativeness of sea surface temperature (SST) climatology and its variability in satellite-based long-term climate data records (CDRs).
To this end, two different SST CDRs have been intercompared on a 30-year long (1982-2011) period.
The full period of common coverage (1982-2016) was used when extracting time series, to have more information on each dataset's capability to properly represent large scale climatic oscillations, such as the El-Nino Southern Oscillation (ENSO).


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Quality assessment statement
---
These are the key outcomes of this assessment

* Both datasets provide consistent results, well reproducing SST mean values and the main patterns of SST variability on a variety of scales, ranging from seasonal to (multi-)annual time scales;
* Both datasets can be exploited for climate studies. As an example, El-Niño/La-Niña events are well characterized by both GMPE and ESA CCI SST L4 v2.1 products
;
* ESA CCI SST L4 v2.1 dataset should be used with caution in the first decade of the CDR. Significant discrepancies with respect to the ensemble mean GMPE dataset were indeed observed in the 1982-1992 period;
```

attachment:bb2e502f-9d8a-4404-8c19-418886e6e483.png
---
height: 400px
width: 550px
---
*Nino 3.4 Time series calculated according to [[6]](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni)*
```


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Methodology
---
We intercompare the performances of the European Space Agency Climate Change Initiative (ESA CCI) SST dataset v2.1 and the GHRSST multi product ensemble (GMPE) SST.

Respectively, catalogue entries available from the CDS are the following:
* Sea surface temperature daily gridded data from 1981 to 2016 derived from a multi-product satellite-based ensemble - from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency SST Climate Change Initiative (ESA CCI SST) (GMPE in the following). The GMPE SST is obtained as the ensemble median of 16 global contributing Level-4 (L4) analyses (including the ESA CCI v2.0 and v1.1) [[1]](https://doi.org/10.1016/j.rse.2018.12.015);
* Sea Surface Temperature daily data from 1981 to present derived from satellite observations, ESA CCI SST L4 dataset v2.1 (ESA CCI SST in the following). This L4 datasets provides daily global SST fields from the merging  of space born Infrared SSTs through a variational assimilation algorithm [[2]](https://doi.org/10.1038/s41597-019-0236-x);

The methodology employed is suitable for investigating the representation of SST variability over climatological time scales.

-The climatology has been defined over a common, standard 30-year long period 1982-2011.

-Annual and seasonal mean, as well as standard deviation, have been computed over the time axis.

-Time series of globally averaged SST anomalies were derived by subtracting the annual cycle from the product’s monthly mean time series, representing the mean of each month over the entire period (1982-2016).

This processing aims to minimize the impact of the seasonal component, thereby enhancing the contribution of higher and lower frequency components.

The analysis and results are organised in the following steps, which are detailed in the sections below:'

**[](section-1)**

**[](section-2)**

Functions for rechunking to optimize caching of intermediate results, get nan values on land and convert to degree celsius in the ocean are defined in this section. Note that chunking is employed to handle more efficiently the large data volume.
Notice that while in GMPE SST sea-ice covered points have nan values, this is not the case in ESA CCI SST, which has an additional variable called "mask". We use this ancillary information to mask out sea-ice covered regions (the variable mask is valued-1 for ocean only points).

**[](section-3)**

Tuples and dictionaries containing infomations such as reduction operations (mean and standard deviation) are defined in this section, and the request is set up, transforming the data while downloading them, according to the proposed diagnostics.

**[](section-4)**
 * 4.1 Plot Annual Maps
 * 4.2 Plot Bias maps
 * 4.3 Plot Seasonal Maps
 * 4.4 Plot of global monthly mean time series, Eastern Tropical Pacific SST (El Niño 3), and East Central Tropical Pacific SST (El Niño 3.4)


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Analysis and results > 1. Import packages and define parameters for the requests to the CDS
---
In the following cell the necessary packages are imported, defining the request to the CDS to download ESA CCI and GMPE SST from 1982 to 2011. Regions to calculate nino3 and nino3.4, and global timeseries are defined.

Parameters to speed up I/O. For more information, see https://docs.xarray.dev/en/stable/generated/xarray.open_mfdataset.html

(section-2)=


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Analysis and results > 3. Download and transform step
---
Initialize variables
Note: year from December year-1 to November year
Seasonal and spatial

```text
product='ESACCI'
```

```text
annual: 100%|██████████| 30/30 [00:13<00:00,  2.17it/s]
spatial reduction: 100%|██████████| 3/3 [00:00<00:00,  5.23it/s]
season: 100%|██████████| 4/4 [00:00<00:00, 27.77it/s]
season: 100%|██████████| 4/4 [00:00<00:00, 12.79it/s]
```

```text
product='GMPE'
```

```text
annual: 100%|██████████| 30/30 [00:14<00:00,  2.07it/s]
spatial reduction: 100%|██████████| 3/3 [00:00<00:00,  4.14it/s]
season: 100%|██████████| 4/4 [00:00<00:00,  6.46it/s]
season: 100%|██████████| 4/4 [00:01<00:00,  3.41it/s]
```

(section-4)=


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Analysis and results > 4. Plot Annual Maps, Bias, Seasonal Maps and Timeseries, with discussions > 4.1 Plot Annual Maps
---
The global distribution of the mean SST during the complete 30 years from 1982 to 2011 is shown in the top panel. The climatology correctly reveals the dominant latitudinal spatial SST pattern: higher at the tropics, milder at middle latitudes and lower in the polar regions (see, e.g., [[3]](https://doi.org/10.1146/annurev-marine-120408-151453)). The globally averaged SST value is estimated in 20.11 $\pm$ 0.39 °C in ESA CCI and  20.23 $\pm$ 0.37 °C in GMPE.
The standard deviation map of annual mean SST is shown in the bottom panel. The annual average removes the seasonal variability and better quantifies the magnitude and spatial distribution of the nonseasonal SST variability. The standard deviation map evidences some patterns of the main current systems such as the Gulf Current and the Kuroshio Current, as well as regions such El-Nino in the tropical pacific, where the SST anomaly standard deviation exceeds 1.5°C. The main upwelling systems as, e.g., Perù-Chili, Benguela, NW-African coast and along the southern Saudi Arabia coast are also evident, as well as regions of strong mixing, like the Antarctic Circumpolar Current and the Agulhas Leakage.


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Analysis and results > 4. Plot Annual Maps, Bias, Seasonal Maps and Timeseries, with discussions > 4.2 Plot Bias Maps
---
Top panel depicts the difference between climatologies in GMPE and ESA CCI SSTs. In general, positive (negative) values stand for GMPE (ESA CCI) being warmer than ESA CCI (GMPE) SSTs. The maximum mean difference is about +1.7°C, especially over the Tropical Eastern Atlantic, Northern Indian Ocean, Western South American and South African coasts. The Gulf Stream presents positive and negative mean SST differences, while the Kuroshio current is mainly characterized by higher GMPE SSTs. On average, GMPE is about 0.02°C warmer than ESA CCI SST. Regarding the standard deviation (bottom panel), SST annual variability is slightly higher in ESA CCI than in GMPE over the tropical Pacific, the North Atlantic, the southern Saudi Arabia coast, and in the Agulhas system. The converse holds true in the Gulf Stream, Antarctic Circumpolar Current, and Indonesian Throughflow, resulting in GMPE having a variance close to 0.2°C greater than the one in ESA CCI. Overall, these results are consistent with the literature (see e.g. [[4]](https://doi.org/10.1175/JCLI-D-20-0793.1), [[5]](https://doi.org/10.1080/1755876X.2016.1273446)).

It is important to notice that some of the major discrepancies in the mean difference map (e.g. around the African continent and in the Northern Indian Ocean), are likely due to known issues of the ESA CCI dataset, as reported in [[2]](https://doi.org/10.1038/s41597-019-0236-x), being attributed to unscreened dust events in the Advanced Very High Resolution Radiometer (AVHRR) SSTs. This bias is strongly mitigated by GMPE SST, which is the median of an ensemble of satellite SST products.


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Analysis and results > 4. Plot Annual Maps, Bias, Seasonal Maps and Timeseries, with discussions > 4.3 Plot seasonal maps
---
Seasonal SST climatologies show similar patterns among the two datasets, with a warmer (cooler) Northern Hemisphere during boreal summer (winter), as expected; the converse holds true for the Southern Hemisphere. It can be noticed the strong influence of major western boundary currents, such as the Gulf Stream and the Kuroshio in determining seasonal oscillations. Overall, from this pictures the two dataset are found in good agreement.

Right after, seasonal standard deviation maps for each product are shown. For example, the DJF map is expressed by the following formula
 $\sqrt{\frac{1}{N}\sum_{i=1}^{N}\left(SST^i_{DJF} - \langle SST_{DJF}\rangle\right)^2},$ where $N$ is the number of observations (i.e. 30 winter seasons in the present case) and $\langle SST_{DJF} \rangle$ is the average wintertime map.
Seasonal standard deviation maps are very similar between the two products, revealing that on average the Northern Hemisphere have a much higher variability than the Southern one, with the Gulf Stream and Kuroshio dominating the patterns year round. Also, signs of high variability are noticeable in both dataset in El-Niño Region and around the Agulhas. While the first region is a reflection of a dominant climate mode, the second one arises as a result of strong mixing and turbulence.

We also plot difference maps to highlight discrepancies in the two datasets. We can clearly notice that major differences are located within the tropical belt, mainly near Africa and in the Northern Indian Ocean. As stated above, these discrepancies can be partly attributed to unresolved dust events in ESA CCI SST.


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > Analysis and results > 4. Plot Annual Maps, Bias, Seasonal Maps and Timeseries, with discussions > 4.4 Plot of global monthly mean time series, Eastern Tropical Pacific SST (El Niño 3), and East Central Tropical Pacific SST (El Niño 3.4)
---
The time series illustrating the globally averaged monthly mean SST anomalies from 1982 to 2016 is presented. These anomalies were derived by subtracting the annual cycle from the product’s monthly mean time series, representing the mean of each month over the entire period (1982-2016). This processing aims to minimize the impact of the seasonal component, thereby enhancing the contribution of higher and lower frequency components. Notably, variations at an interannual time scale are prominently displayed. These changes are mostly related to strong signatures of El Niño Southern Oscillation (ENSO) variability, with a particular strong and sharp increase (warming) in SST during e.g. the 1982/3, 1987/88, 1991/92, 1994/95, 1997/8 and 2009/10 El Niño events, and negative (cooling) peaks during the 1983/5, 1988/9, 1995/6 and 2006/7 La Niña events. Differences between the two time series which exceed 0.5 degrees characterize the first decade of the period, before 1990. The large and sharp peak in 1982/3 reveals apparently erroneous SSTs, as also evidenced in the SST CCI Climate Assessment Report (CAR, available at https://climate.esa.int/media/documents/SST_CCI_D5.1_CAR_v1.1-signed.pdf).
The time series of SST anomalies within the El Niño 3.4 and El Niño 3 regions are also shown (see also [[6]](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni) for a definition of El Niño Southern Oscillation indices). These time series are used as indexes to monitor the occurrence and variability of El Niño and la Niña events. The El Niño 3.4 index is defined as the average equatorial SST anomalies across the Pacific in the region 5°S-5°N, 170W°-120W°, while El Niño 3 between 150°W-90°W. Peaks corresponding to El-Niño/La-Niña events are well represented by both GMPE and ESA CCI SST products. See [[6]](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni) for reference.
For both the global mean timeseries and El Niño indexes, there are discrepancies in ESA CCI compared to GMPE SST (GMPE is here used as intercomparison benchmark due to its high accuracy, following [[1]](https://doi.org/10.1016/j.rse.2018.12.015), [[7]](https://doi.org/10.1016/j.dsr2.2012.04.013)),  in the period 1982 to 1992. Such discrepancies are highly attenuated from 1992 onwards, suggesting that ESA CCI data should be used with caution in the first decade of the CDR.


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > ℹ If you want to know more > Key resources
---
SST CCI Climate Assessment Report (CAR): https://climate.esa.int/media/documents/SST_CCI_D5.1_CAR_v1.1-signed.pdf

GHRSST Website: https://www.ghrsst.org/

Additional sources for SST ensemble intercomparison statistics: https://ghrsst-pp.metoffice.gov.uk/ostia-website/gmpe-monitoring.html

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)
* python packages: matplotlib, xarray, cartopy, numpy, tqdm


EQC Quality Assessment: "Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Consistency Assessment of Satellite Sea Surface Temperature for Climate Monitoring > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1016/j.rse.2018.12.015) Fiedler, E. K., McLaren, A., Banzon, V., Brasnett, B., Ishizaki, S., Kennedy, J., ... & Donlon, C. (2019). Intercomparison of long-term sea surface temperature analyses using the GHRSST Multi-Product Ensemble (GMPE) system. Remote sensing of environment, 222, 18-33

[[2]](https://doi.org/10.1038/s41597-019-0236-x) Merchant, C. J., Embury, O., Bulgin, C. E., Block, T., Corlett, G. K., Fiedler, E., ... & Donlon, C. (2019). Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific data, 6(1), 223. https://doi.org/10.1038/s41597-019-0236-x

[[3]](https://doi.org/10.1146/annurev-marine-120408-151453) Deser, C., Alexander, M. A., Xie, S. P., & Phillips, A. S. (2010). Sea surface temperature variability: Patterns and mechanisms. Annual review of marine science, 2, 115-143.

[[4]](https://doi.org/10.1175/JCLI-D-20-0793.1) Yang, C., Leonelli, F. E., Marullo, S., Artale, V., Beggs, H., Nardelli, B. B., ... & Pisano, A. (2021). Sea surface temperature intercomparison in the framework of the Copernicus Climate Change Service (C3S). Journal of Climate, 34(13), 5257-5283

[[5]](https://doi.org/10.1080/1755876X.2016.1273446) Von Schuckmann et al. (2016). The Copernicus Marine Environment Monitoring Service Ocean State Report. Jour. Operational Ocean., vol. 9, 2016, suppl. 2. doi: https://doi.org/10.1080/1755876X.2016.1273446

[[6]](https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni) Trenberth, Kevin & National Center for Atmospheric Research Staff (Eds). Last modified 21 Jan 2020. "The Climate Data Guide: Nino SST Indices (Nino 1+2, 3, 3.4, 4; ONI and TNI)." Retrieved from https://climatedataguide.ucar.edu/climate-data/nino-sst-indices-nino-12-3-34-4-oni-and-tni

[[7]](https://doi.org/10.1016/j.dsr2.2012.04.013) Martin, M., Dash, P., Ignatov, A., Banzon, V., Beggs, H., Brasnett, B., ... & Roberts-Jones, J. (2012). Group for High Resolution Sea Surface temperature (GHRSST) analysis fields inter-comparisons. Part 1: A GHRSST multi-product ensemble (GMPE). Deep Sea Research Part II: Topical Studies in Oceanography, 77, 21-30


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Quality assessment question
---
* **How well do SST trend estimates obtained from ESA CCI L4 v2.1 and GHRSST Multi-Product Ensemble (GMPE) compare with each other?**

Our aim here is to evaluate the reliability and representativeness of sea surface temperature (SST) linear trends in satellite-based long-term climate data records (CDRs). To this end, two different SST CDRs have been intercompared over a common 30-year long period (1982-2011).


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Quality assessment statement
---
These are the key outcomes of this assessment

* Both datasets correctly capture the long-term SST trend at both global and regional scale;
* The spatial pattern of the SST trend evidences that the Northern Hemisphere is experiencing a more intense warming compared to  the Southern Hemisphere, as reported in literature ([[1]](https://doi.org/10.1007/s00382-014-2147-z), [[2]](https://doi.org/10.1175/JCLI-D-20-0793.1));
* ESA CCI L4 v2.1 tends to exhibit higher trend estimates compared to GMPE, which serves as a reference dataset due to its nature, particularly in certain ocean basins like the Tropical Atlantic and Northern Indian Ocean;
* On average, the global mean SST trend estimates provided by the two datasets are consistent within the 95% confidence interval;
```

attachment:c3c17d69-0c57-4673-a04b-f10787dc8333.png
---
height: 400px
width: 800px
---
*Linear trends (1982-2011) of ESA CCI and GMPE SSTs*
```


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Methodology
---
We evaluate the reliability and representativeness of Sea Surface Temperature (SST) linear trends in satellite-based long-term climate data records (CDRs). To this end, two different SST CDRs have been intercompared over a common 30-year long period (1982-2011).
Respectively, catalogue entries available from the CDS are the following:

* Sea surface temperature daily gridded data from 1981 to 2016 derived from a multi-product satellite-based ensemble - from the Group for High Resolution Sea Surface Temperature (GHRSST) multi-product ensemble (GMPE) produced by the European Space Agency Climate Change Initiative SST (ESA CCI SST) (GMPE in the following). The GMPE SST is obtained as the ensemble median of 16 global contributing Level-4 (L4) analyses (including the ESA CCI v2.0 and v1.1)  [[3]](https://doi.org/10.1016/j.rse.2018.12.015);
* Sea Surface Temperature daily data from 1981 to present derived from satellite observations, ESA CCI SST L4 dataset v2.1 (ESA CCI SST in the following). This L4 datasets provides daily global SST fields from the merging  of space born Infrared SSTs through a variational assimilation algorithm [[4]](https://doi.org/10.1038/s41597-019-0236-x);

The methodology employed is suitable to monitor surface ocean warming over a climatological standard period.

-Annual and seasonal reductions (linear trend with Mann-Kendall test to estimate its significance at 95% confidence level) have been computed over the time axis, and shown as maps. In these maps, patches denote grid points in which there’s no significant trend;

-To have a closer look at areas of major trend discrepancies between the two datasets, time series of spatially averaged annual mean SST, with their trends, have been computed over Tropical Atlantic and Northern Indian Ocean;

-Linear trend estimates were retrieved with the same method used for producing trend maps, described above;

-Superimposed to the time series, also the absolute differences plot on a twin axis illustrate how discrepancies are not homogenous in time.

The analysis and results are organised in the following steps, which are detailed in the sections below:

**[](section-1)**

**[](section-2)**

**[](section-3)**

**[](section-4)**
 * 4.1 Plot Annual trend maps
 * 4.2 Seasonal Trends
 * 4.3 SST Time series and trends over Tropical Atlantic and Northern Indian Ocean.


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Analysis and results > 1. Import packages and define parameters for the requests to the CDS
---
In the following cell the necessary packages are imported, defining parameters for the request and trend calculation.

Choose timeseries
Parameters to speed up I/O

(section-2)=


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Analysis and results > 2. Define functions to cache
---
Functions for rechunking to optimize caching of intermediate results, get nan values on land, convert to degree Celsius in the ocean and calculate the trend are defined below.

(section-3)=


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Analysis and results > 3. Download and transform
---
In this step the data are requested, retrieved and processed in one step.

print(f"{product=}")
Mann Kendall settings

(section-4)=


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Analysis and results > 4. Plot Annual and Seasonal trends, SST Time series and trends over Tropical Atlantic and Northern Indian Ocean, with discussions > 4.1 Plot Annual trend maps
---
In this section we plot the annual trend maps for each product, discussing differences.

```text
ESACCI in Mediterranean Sea: 0.042 ± 0.014 °C/year
ESACCI in Baltic Sea: 0.044 ± 0.016 °C/year
ESACCI in Black Sea: 0.058 ± 0.018 °C/year
ESACCI in Global: 0.022 ± 0.013 °C/year
ESACCI in Tropical Atlantic: 0.033 ± 0.01 °C/year
ESACCI in Northern Indian Ocean: 0.03 ± 0.008 °C/year
GMPE in Mediterranean Sea: 0.037 ± 0.012 °C/year
GMPE in Baltic Sea: 0.053 ± 0.015 °C/year
GMPE in Black Sea: 0.056 ± 0.015 °C/year
GMPE in Global: 0.018 ± 0.01 °C/year
GMPE in Tropical Atlantic: 0.025 ± 0.0095 °C/year
GMPE in Northern Indian Ocean: 0.019 ± 0.0083 °C/year
```

The global SST trend map has been computed over 30 years from 1982 to 2011 at the 95% confidence level (grey dots mark non-significant trend areas). SST exhibits an overall positive (warming) trend over the Northern Hemisphere, where it can reach $\simeq$0.05 °C/year, while the Southern Hemisphere is characterized by less significant and negative values. A large area covering the Eastern Pacific Ocean is characterized by no significant trends (i.e., p $\geq$ 0.05) with few sparse significant values in both ESA CCI and GMPE. The North Atlantic ocean and the European seas stand out showing the most intense SST trend values, as e.g. the Mediterranean Sea (GMPE = 0.04 $\pm$ 0.01 °C/year, ESA CCI = 0.04 $\pm$ 0.01 °C/year), Baltic Sea (GMPE = 0.05 $\pm$ 0.02 °C/year, ESA CCI = 0.04 $\pm$ 0.02 °C/year) and Black Sea (GMPE = 0.06 $\pm$ 0.01 °C/year, ESA CCI = 0.06 $\pm$ 0.02 °C/year). The Mediterranean SST trend is in good agreement with the one reported in [[5]](https://doi.org/10.3390/rs12010132), while Baltic Sea and Black Sea trends are similar to those found in [[6]](https://doi.org/10.1080/1755876X.2018.1489208), but over a shorter period. In the Black and Mediterranean Sea, trend estimates between ESA CCI and GMPE are more in agreement than in the Baltic Sea.


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Analysis and results > 4. Plot Annual and Seasonal trends, SST Time series and trends over Tropical Atlantic and Northern Indian Ocean, with discussions > 4.2 Seasonal Trends
---
In this section we plot seasonal trends for each product, discussing the differences.

Choose timeseries
print(f"{product=}")
Mann Kendall settings
Plot seasonal trends

Seasonal maps show an intensification of the trend from Spring to Autumn and an attenuation during winter. Overall the agreement between GMPE and ESA CCI SST is quite good though the former dataset provides less intense trend estimates. Both datasets’ estimates yield a Northern Hemisphere which has warmed up at a higher rate than the Southern Hemisphere, especially in Boreal Summer and Fall.

The globally averaged mean trend is estimated in 0.018 $\pm$ 0.01 °C/year for GMPE, while for ESA CCI we have 0.022 $\pm$ 0.013 °C/year, which corresponds to an average total increase of about 0.54 °C in GMPE and 0.66 °C in ESA CCI over this 30-year period (1982-2011). Within the 95% confidence interval, both datasets provide consistent estimates of global warming trends. These values are also reasonably consistent (within respective errors) with the mean global trend estimate of 0.011 °C/year from 1980 to 2005 as reported in one of the last IPCC reports [[7]](http://hdl.handle.net/10013/epic.45156.d001), given the different time period on which the trend is calculated. Regionally, estimates of trend slopes yield values for the Northern Indian Ocean (GMPE=0.019 $\pm$ 0.008 °C/year, ESA CCI=0.030 $\pm$ 0.008 °C/year) and Tropical Atlantic (GMPE= 0.025 $\pm$ 0.009 °C/year, ESA CCI=0.003 $\pm$ 0.01 °C/year), which are consistent with each others in the limits of their respective uncertainties, though are areas of main disagreement for trend estimates.


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > Analysis and results > 4. Plot Annual and Seasonal trends, SST Time series and trends over Tropical Atlantic and Northern Indian Ocean, with discussions > 4.3 SST Time series and trends over Tropical Atlantic and Northern Indian Ocean
---
Trend analyses shown in the previous section evidenced the existence of disagreement areas between ESA CCI and GMPE SST datasets. In particular, the Northern Indian Ocean and the Tropical Atlantic exhibit a larger SST trend for the ESA CCI dataset, when compared to GMPE. The ESA CCI and GMPE SST monthly time series were then intercompared in those areas, in order to have further insights on these discrepancies.

```text
product='ESACCI' region='global'
```

```text
year: 100%|██████████| 35/35 [00:09<00:00,  3.72it/s]
```

```text
product='ESACCI' region='med sea'
```

```text
year: 100%|██████████| 35/35 [00:09<00:00,  3.58it/s]
```

```text
product='ESACCI' region='baltic sea'
```

```text
year: 100%|██████████| 35/35 [00:05<00:00,  6.27it/s]
```

```text
product='ESACCI' region='black sea'
```

```text
year: 100%|██████████| 35/35 [00:05<00:00,  6.41it/s]
```

```text
product='ESACCI' region='Tropical Atlantic'
```

```text
year: 100%|██████████| 35/35 [00:03<00:00,  9.27it/s]
```

```text
product='ESACCI' region='Northern Indian Ocean'
```

```text
year: 100%|██████████| 35/35 [00:03<00:00,  8.81it/s]
```

```text
product='GMPE' region='global'
```

```text
year: 100%|██████████| 35/35 [00:06<00:00,  5.48it/s]
```

```text
product='GMPE' region='med sea'
```

```text
year: 100%|██████████| 35/35 [00:04<00:00,  7.60it/s]
```

```text
product='GMPE' region='baltic sea'
```

```text
year: 100%|██████████| 35/35 [00:04<00:00,  7.88it/s]
```

```text
product='GMPE' region='black sea'
```

```text
year: 100%|██████████| 35/35 [00:05<00:00,  6.97it/s]
```

```text
product='GMPE' region='Tropical Atlantic'
```

```text
year: 100%|██████████| 35/35 [00:07<00:00,  4.54it/s]
```

```text
product='GMPE' region='Northern Indian Ocean'
```

```text
year: 100%|██████████| 35/35 [00:54<00:00,  1.56s/it]
```

```text
ESACCI in Northern Indian Ocean: 0.0018 ± 0.0004 °C/year
GMPE in Northern Indian Ocean: 0.0012 ± 0.0005 °C/year
```

```text
ESACCI in Tropical Atlantic: 0.0024 ± 0.0004 °C/year
GMPE in Tropical Atlantic: 0.002 ± 0.0004 °C/year
```

In the 1982-1991 decade, the ESA CCI SSTs are generally colder than the GMPE ones, as sketched by the monthly time series presented in this section. This is also highlighted by the ∆SST time series (given in yellow), whose values can reach 1°C/0.6°C in the Indian Ocean and Tropical Atlantic, respectively, in the aforementioned temporal window. Such discrepancies are significantly reduced afterwards, thus resulting in a larger SST slope from the ESA CCI CDR, when compared with GMPE.  This is a known issue of the ESA CCI dataset, as reported in [[4]](https://doi.org/10.1038/s41597-019-0236-x): unscreened dust events caused negative biases in the Advanced Very High Resolution Radiometer (AVHRR) SSTs in the north East Tropical Atlantic and in the Arabian/Red Sea. This issue  covers the first decade of the CDR, resulting in anomalously high positive SST trends in these regions.


EQC Quality Assessment: "Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends"
Dataset: satellite-sea-surface-temperature [CDS]
Aspect: consistency_q02  |  Category: Satellite_ECVs
Section: Satellite Sea Surface Temperature Consistency in representing Ocean Warming Trends > ℹ If you want to know more > Key resources
---
SST CCI Climate Assessment Report (CAR): https://climate.esa.int/media/documents/SST_CCI_D5.1_CAR_v1.1-signed.pdf

GHRSST Website: https://www.ghrsst.org/

Additional sources for SST ensemble intercomparison statistics: https://ghrsst-pp.metoffice.gov.uk/ostia-website/gmpe-monitoring.html

Code libraries used:
* [C3S EQC custom functions](https://github.com/bopen/c3s-eqc-automatic-quality-control/tree/main/c3s_eqc_automatic_quality_control), `c3s_eqc_automatic_quality_control`,  prepared by [B-Open](https://www.bopen.eu/)
* python packages: matplotlib, xarray, cartopy, numpy, tqdm