EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Plot and description of the results > Scatter plots: comparison of daily and monthly scales
---
Before evaluating the contribution of water vapor to the clear sky greenhouse effect, a sanity check is done by comparing the rate of change of G$_\text{CS}$ for a given change in sea surface temperature. This sanity check allows to compare with reference studies using satellite data.

Plot linear fit
Plot linear fit

*Figure 1: Distributions of the clear sky greenhouse effect G$_{CS}$ according to SST for (a) daily data and (b) monthly data. The colorscales represent the density of points. The red dashed line are the linear regressions, with the slope and its uncertainty reported on the top left of each figures.*

The slopes of the regression lines slightly differ at the two scales: 5.94 W/m$^2$/K for daily data (Fig. 1a) and 5.86 W/m$^2$/K for monthly data (Fig. 1b).

Despite these slight differences, the value of the slopes $d \ G_{CS}/d T_{surf}$ are consistent with the findings of [[15]](https://doi.org/10.1175/JCLI3611.1), [[16]](https://doi.org/10.1038/351027a0) and [[17]](https://doi.org/epdf/10.1029/92JD00729), with reported values for given months ranging from 5.6 W/m$^2$/K to 6.3 W/m$^2$/K (monthly averages).

The contribution by water vapor to this sensitivity is estimated by looking at the linear regression $d \ G_{CS}/d \ TCWV$, and the slope of this regression is estimated using daily data and monthly data to have a sensitivity of this value to the temporal scale.

Plot linear fit
Plot linear fit

*Figures 2 (top) and 4 (bottom) : Density diagrams of the clear sky greenhouse effect G$_{CS}$ according to bins of TCWV for daily data (top) and monthly data (bottom). The colorscales represent the density of points. The red dashed line are the linear regressions, with the slope and its uncertainty reported on the bottom left of each figures.*

The results show a linear relationship between the clear sky greenhouse effect and the Total Columns of Water Vapour, in agreement with previous research (see [[5]](https://doi.org/10.1073/pnas.1809868115), [[6]](https://doi.org/full/10.1029/2000JD000040) and [[7]](https://doi.org/10.1002/2013JD020184)).

According to the section '[](method-section-2)' in the __Methodology__ Chapter, those results can be used to estimate $f_{wv}$:

$$
\begin{align}
\frac{d \ \text{G}_{CS wv}}{d \ TCWV} \ & \ \text{is estimated from the slope of the linear regression G$_{CS}$=f(TCWV) in W/m$^2$/mm } \\
\overline{TCWV}  & \ \text{is the areal average of TCWV over the tropical belt in mm} \\
CC & = 0.07 \ \text{K$^{-1}$} \ \ \text{is the Clausius-Clapeyron rate of 7\%/K}
\end{align}
$$

As stated in the [](Method-section-1), the contribution of water vapor to the clear sky greenhouse effect is estimated to be $\sim$ 60\%. Therefore, we can reasonably consider that 60\% of the slope G$_{CS}$=f(TCWV) corresponds to the water vapor contribution $\text{G}_{CS wv}$

Variables to estimate the water vapor feedback, from the equation in the Methodology section

```text
slope: 1.75 W/m^2/mm
tropical mean TCWV: 33.81 mm
tropical mean SST: 299.69 K
feedback: 2.48 W/m^2/K
```

This result falls within the range of uncertainty of the water vapor feedback reported in the IPCC report Chapter 7 ([[9]](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf)) and summarized in Figure 7.20 (inset on the 'Global warming contributions from individual radiative feedbacks'). The reported values range between 2.2 and 3.5 W/m$^2$/K.


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > Analysis and results > Plot and description of the results > Scatter plots: comparison of daily and monthly scales
---
.pdf)) and summarized in Figure 7.20 (inset on the 'Global warming contributions from individual radiative feedbacks'). The reported values range between 2.2 and 3.5 W/m$^2$/K.

Here we have looked at the relationship between clear sky greenhouse effect and TCWV is studied on the time scale of one month. However, although the findings are consistent with the literature, climate feedbacks are generally estimated at longer time scale in order to take into account the role of natural climate variability (including El Nino events for instance).


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

The CDS catalogue entries for the data used in this assessment are:
- Monthly and 6-hourly total column water vapour over ocean from 1988 to 2020 derived from satellite observations:
https://cds.climate.copernicus.eu/datasets/satellite-total-column-water-vapour-ocean

- Cloud properties global gridded monthly and daily data from 1979 to present derived from satellite observations:
https://cds.climate.copernicus.eu/datasets/satellite-cloud-properties

- Sea surface temperature daily data from 1981 to present derived from satellite observations:
https://cds.climate.copernicus.eu/datasets/satellite-sea-surface-temperature

- Earth's radiation budget from 1979 to present derived from satellite observations:
https://cds.climate.copernicus.eu/datasets/satellite-earth-radiation-budget

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/)

And some resources on the greenhouse effect of water vapor and its feedback :
- https://science.nasa.gov/earth/climate-change/steamy-relationships-how-atmospheric-water-vapor-amplifies-earths-greenhouse-effect/

- https://wmo.int/media/magazine-article/observing-water-vapour


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1029/2010JD014287) Schmidt G. A., Ruedy R. A., Miller R. L., and A. A. Lacis (2010), Attribution of the present‐day total greenhouse effect,J. Geophys. Res.,115, D20106, doi:10.1029/2010JD014287

[[2]](https://doi.org/10.1175/1520-0477%281997%29078<0197:EAGMEB>2.0.CO;2) Kiehl J. T. and K. Trenberth (1997), Earth's Annual Global Mean Energy Budget, Bull. Am. Meteorol. Soc., 78, 197-208, https://doi.org/10.1175/1520-0477%281997%29078<0197:EAGMEB>2.0.CO;2

[[3]](https://doi.org/10.1175/1520-0469%281967%29024<0241:TEOTAW>2.0.CO;2) Manabe S., and R. T. Wetherald (1967), Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity, J. Atmos. Sci., 24, 241–259, https://doi.org/10.1175/1520-0469%281967%29024<0241:TEOTAW>2.0.CO;2

[[4]](https://doi.org/10.1146/annurev.energy.25.1.441) Held I. and B. Soden (2000), Water vapor feedback and global warming, Annu. Rev. Energy Environn., 25:441-75, doi:10.1146/annurev.energy.25.1.441

[[5]](https://doi.org/10.1073/pnas.1809868115), Koll D. and T. Cronin (2018) Earth’s outgoing longwave radiation linear due to H2O greenhouse effect, Proc. Nat. Ac. Sci., 115 (41) 10293-10298, doi:10.1073/pnas.1809868115

[[6]](https://doi.org/full/10.1029/2000JD000040)  Roca R., Viollier M., Picon L., and M. Desbois (2002), A multisatellite analysis of deep convection and its moist environment over the Indian Ocean during the winter monsoon, J. Geophys. Res., 107(D19), doi:10.1029/2000JD000040.

[[7]](https://doi.org/10.1002/2013JD020184) Gordon N., A. Jonko, P. Forster and K. Shell (2013), An observationally based constraint on the water-vapor feedback, J. Geophys. Res., 118, 12,435-12,443, doi:10.1002/2013JD020184.

[[8]](https://doi.org/10.1029/2019JD031017) Raghuraman S. P., D. Paynter and V. Ramaswamy (2019), Quantifying the drivers of the clear sky greenhouse effect, 2000-2016, J. Geophys. Res, 124, 11,354-11,371, https://doi.org/10.1029/2019JD031017

[[9]](https://www.ipcc.ch/report/ar6/wg1/downloads/report/IPCC_AR6_WGI_Chapter07.pdf) Forster P., Storelvmo T., Armour K., Collins W., Dufresne J-L, Frame D., Lunt D.J., Mauritsen T., Palmer M.D., Watanabe M., Wild M., and H. Zhang (2021), Chapter 7 The Earth’s Energy Budget, Climate Feedbacks, and Climate Sensitivity. In Climate Change 2021: The Physical Science Basis. Contribution of Working Group I to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi: 10.1017/9781009157896.009.


EQC Quality Assessment: "Water vapor amplification of Earth's Greenhouse Effect"
Dataset: satellite-total-column-water-vapour-ocean [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Water vapor amplification of Earth's Greenhouse Effect > ℹ If you want to know more > References
---
al Panel on Climate Change. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, pp. 923–1054, doi: 10.1017/9781009157896.009.

[[10]](https://doi.org/abs/10.1029/1998JD900007) Inamdar A. and V. Ramanathan (1998), Tropical and global scale interactions among water vapor, greenhouse effect and surface temperature, J. Geophys. Res, 103, D24, 32,177-32,194, doi: https://doi.org/10.1029/1998JD900007

[[11]](https://doi.org/10.1175/JCLI3990.1) Held I. and B. Soden (2006), Robust responses of the hydrological cycle to global warming, J. Clim., 19, 5686–5699, https://doi.org/10.1175/JCLI3990.1

[[12]](https://essd.copernicus.org/articles/2/215/2010/essd-2-215-2010.html) Andersson A., Fennig K., Klepp C., Bakan S., Graßl H., and J. Schulz (2010), The Hamburg Ocean Atmosphere Parameters and Fluxes from Satellite Data – HOAPS-3, Earth Syst. Sci. Data, 2, 215-234, doi:10.5194/essd-2-215-2010

[[13]](https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003) Karlsson K., Riihelä A., Trentmann J., Stengel M., Solodovnik I., Meirink J., Devasthale A., Jääskeläinen E., Kallio-Myers V., Eliasson S., Benas N., Johansson E., Stein D., Finkensieper S., Håkansson N., Akkermans T., Clerbaux N., Selbach N., Schröder M. and R. Hollmann (2023), CLARA-A3: CM SAF cLoud, Albedo and surface RAdiation dataset from AVHRR data - Edition 3, Satellite Application Facility on Climate Monitoring
DOI:10.5676/EUM_SAF_CM/CLARA_AVHRR/V003, https://doi.org/10.5676/EUM_SAF_CM/CLARA_AVHRR/V003

[[14]](https://doi.org/10.1038/s41597-019-0236-x) Merchant C. J., Embury O., Bulgin C. E., Block T., Corlett G. K., Fiedler E., Good S. A., Mittaz J., Rayner N. A., Berry D., Eastwood S., Taylor M., Tsushima Y., Waterfall A., Wilson R. and C. Donlon (2019), Satellite-based time-series of sea-surface temperature since 1981 for climate applications. Scientific Data, 6. 223. doi:10.1038/s41597-019-0236-x

[[15]](https://doi.org/10.1175/JCLI3611.1) Forster P. and J. Gregory (2006), the climate sensitivity and its components diagnosed from earth radiation budget data, J. Clim., 19, 39-52, doi:10.1175/JCLI3611.1.

[[16]](https://doi.org/10.1038/351027a0) Ramanathan V. and W. Collins (1991), Thermodynamic regulation of ocean warming by cirrus clouds deduced from observations of the 1987 El Nino, Nature, 351, 27-32.

[[17]](https://doi.org/epdf/10.1029/92JD00729) Kiehl J. and B. Briegleb (1992), Comparison of the observed and calculated sky sky greenhouse effect: implications for climate studies, J. Geophys. Res, 97, 10,037-10,049.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity
---
Production date: 04-04-2025

Produced by : UVSQ/LSCE-IPSL (Hélène Brogniez) ; CNRS/LMD-IPSL (Giulio Mandorli, Claudia Stubenrauch)


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Quality assessment question:
---
- __How well can satellite measurements reproduce the known relationship between large-scale circulation and upper tropospheric humidity (UTH)?__

Water vapor in the mid- to upper troposphere, while representing a small fraction of the total column, has
a significant impact on the Earth's radiative budget thanks to its radiative and thermodynamical properties.  In the tropics, the distribution of upper tropospheric humidity (UTH) is primarily controlled by large-scale transport, with influences by storm systems in the deep tropics [[1]](https://doi.org/10.1029/97GL03563).

The atmospheric circulation is the large-scale movement of air through the Earth's atmosphere, driven by the uneven heating of the planet's surface by the sun. This circulation is responsible for distributing heat and moisture around the globe, shaping weather patterns and climate.
The movement of the air masses influences the distribution of humidity in the upper troposphere. In regions where air rises, such as the  Inter Tropical Convergence Zone, moisture from the lower atmosphere is transported upward, increasing humidity in the upper troposphere.
Conversely, in regions where air sinks, the upper troposphere becomes drier.

This notebook aims to examine the dependency of upper tropospheric humidity on the global atmospheric circulation. Specifically, it seeks to verify if the UTH dataset can reproduce this dependency in agreement with [[2]](https://doi.org/10.1029/2006GL029118). The analysis will compare the dataset's findings with established results to ensure consistency and reliability in representing the atmospheric dynamics.

The analysis is performed using the dataset __Upper tropospheric humidity gridded data from 1999 to present derived from satellite observations__  [[described here]](https://cds.climate.copernicus.eu/datasets/satellite-upper-troposphere-humidity) available on the Climate Data Store of the Copernicus Climate Change Service.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Quality assessment statement
---
These are the key outcomes of this assessment

- The analysis is done with year 2001 using monthly data over the tropical regions. The UTH dataset reproduces the known relationship between the atmospheric moisture and the tropical large-scale atmospheric circulation: ascending motions moisten the upper troposphere, whereas subsiding motions dry it. The amplitude and frequency of moist and dry situations are consistent with the literature [[2]](https://doi.org/10.1029/2006GL029118).

- The tested subset of the UTH dataset shows little month-to-month variability over the year, in agreement with the known link between atmospheric circulation regimes and UTH which is independent on the season.

- Some slight discrepancies with the literature arise and can be explained :

- differences in the amplitude and frequency of the vertical velocity at 500hPa : the quality assessment uses ERA5 reanalyses while the literature relied on former versions of reanalysis (ERA40 and NCEP/NCAR reanalyses, see [[2]](https://doi.org/10.1029/2006GL029118) and [[3]](https://doi.org/10.1007/s00382-003-0369-6)). Differences in the results may arise from differences in their horizontal resolutions, in their vertical description of the atmosphere, in the version of the assimilation system and in the atmospheric model.

- differences in the amplitude of UTH : the retrieval method of the UTH of the CDS data record [[see the ATBD]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=288339045) is not strictly identical to the reference paper [[2]](https://doi.org/10.1029/2006GL029118).

```


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Methodology
---
The link between upper tropospheric humidity (UTH) and large-scale circulation is examined using only the vertical atmospheric motion in the mid-troposphere at 500hPa (noted $\omega_{500}$) available from the Climate Data Store and detailed below.

The UTH data over the __tropics__ is downloaded for the full year 2001.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Methodology > Upper Tropospheric Humidity
---
The Upper Tropospheric Humidity (UTH) is a direct interpretation of the satellite measurements in strong water vapor absorption lines, such as those in the infra-red band at 6.2µm and in the micro-wave in the 183.31GHz line. Indeed, at these wavelengths, the emission to space is explained by water vapor concentration and temperature (see early studies by [[4]](https://doi.org/10.1175/1520-0450%281988%29027%3C0889:EOTUTR%3E2.0.CO;2) and [[5]](https://doi.org/10.1029/93JD01283)).
Upon some simple assumptions on the local vertical lapse rate of the troposphere and on the behaviour of the absorption spectral lines, the UTH is obtained via a log-linear equation from the satellite measurement (brightness temperature, $TB$ in Kelvins):

$$
  \text{ln}( \ UTH \ ) = a + b \cdot TB
  $$

Physically, the UTH is a vertical integration of the relative humidity, weighted by the vertical sensitivity of the measurement to water vapor distribution. It is therefore expressed in the unit of %, the S.I. unit of relative humidity, and, as such, is interpreted in the same way.

The computation of the regression parameters $a$ and $b$ is done thanks to a statistically representative dataset (see [[5]](https://doi.org/10.1029/93JD01283), [[6]](https://doi.org/10.1029/2007JD009314), [[7]](https://doi.org/10.1175/2009JCLI2963.1), [[8]](https://doi.org/10.5194/acp-14-11129-2014)), and the values depend on the definition of UTH itself. It also depends on the spectral domain (infra-red of microwave).

UTH may be prefered over vertically resolved relative humidity profiles, provided for example by reanalyses such as ERA5, when the vertical resolution is not a key parameter and when independence from a modeling framework is sought ([[9]](https://doi.org/10.5194/acp-7-2489-2007), [[10]](https://doi.org/10.1175/JCLI-D-19-0046.1)).


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Methodology > Method
---
Both variables (UTH and $\omega_{500}$) are averaged monthly to smooth out short-term fluctuations caused by convection. The parameter $\omega_{500}$ is extracted from ERA5 reanalyses (see below). ERA5 data resolution is reduced from 0.25 degree to 1 degree to match the resolution of the UTH data. After collocating the monthly values at this resolution, the UTH values are categorized into bins based on the vertical velocities. The distribution of UTH within each bin is presented as a boxplot profile, providing a clear visualization of how UTH varies with changes in vertical velocity.

The analysis comprises the following steps:

__1. [](data-section-1)__
 - Import the relevant packages.
 - Define the parameters of the analysis and set the dataset requests

__2. [](data-section-2)__
 - Download the variables of interest: Upper tropospheric Humidity is obtained from __Upper tropospheric humidity gridded data from 1999 to present derived from satellite observations__  [[UTH]](https://cds.climate.copernicus.eu/datasets/satellite-upper-troposphere-humidity), Vertical component of wind $\omega_{500}$ is obtained from __ERA5 hourly data on pressure levels from 1940 to present__ [[ERA5]](https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview).
 -  ERA5 $\omega_{500}$ is regridded to align with the resolution of UTH and its unit is converted from hPa/s to hPa/day to be consistent with published references.
 -  The datasets are colocated in space and time over the Tropics.
 -  Monthly averages are used.

__3. [](Analysis-section-1)__
 -  Maps and histograms of UTH and $\omega_{500}$ are described at the monthly scale for the whole year 2001.

__4. [](Plot-and-results)__
 -  The composite of UTH is shown as a function of $\omega_{500}$. The final results are compared with the appropriate references and the sources of differences are discussed.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Choose the parameters to use and setup code > Define parameters
---
This use case is developed for one year of data (2001) and for the tropical belt, restricted to 30$^\circ$S-30$^\circ$N.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Choose the parameters to use and setup code > Set the data request
---
Two datasets are requested :
- Upper Tropospheric Humidity (UTH)
- Vertical atmospheric velocity at 500hPa ($\omega_{500}$)

(data-section-2)=


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Download the datasets and perform space-time aggregation
---
The datasets are now downloaded.

```text
100%|██████████| 12/12 [00:00<00:00, 15.31it/s]
100%|██████████| 12/12 [00:00<00:00, 34.24it/s]
```


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Download the datasets and perform space-time aggregation > Temporal and spatial aggregation
---
All data records are provided at different spatial and temporal resolutions :
- Upper Tropospheric Humidity : 1$^\circ$ / 1-day
- Vertical atmospheric velocity at 500hPa ($\omega_{500}$) : 0.25$^\circ$ / 1-hour

Since this is a cross-variable analysis, the study requires to process all the datasets at the same horizontal and temporal resolutions.

Note that for the UTH data record, two manipulations of the latitude $\times$ longitude grid are done : first a reversal of the latitudes since the data is stored South-to-North (North-to-South for ERA5 $\omega_{500}$), and second a change of the longitude values since they range from 0° to 360° (-180° to 180° for ERA5 $\omega_{500}$) .

The common spatial aggregation is thus 1$^\circ$ and the data records are processed at the monthly scale.

coarsen datasets to 1° grid / 1-month


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Download the datasets and perform space-time aggregation > Change of unit for vertical velocity
---
Most scientific studies focusing on the large-scale atmospheric circulation use __hPa/day__ as the unit for the upward atmospheric wind (see [[3]](https://doi.org/10.1007/s00382-003-0369-6)), since it offers a more meaningful unit at the considered time scale.

from Pa/s to hPa/day
and the unit in the dataset is changed accordingly


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Download the datasets and perform space-time aggregation > Organisation of the data
---
Creation of a dataframe that keeps also the information of each month to study the month-to-month variability
sort and stack the data
convert into a serie
extraction of the month
Creation of the dataframe
Supression of missing values

(Analysis-section-1)=


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Overview of the geophysical variables of interest > Upper Tropospheric Humidity
---
The Upper Tropospheric Humidity (UTH, in %) is a single-layer geophysical variable provided by EUMETSAT on a 1°$\times$1° regular grid.

The UTH data record of the CDS relies on measurements from the 183.31±1 GHz channels of the Advanced Microwave Sounding Unit-B (AMSU-B) and the Microwave Humidity Sounder (MHS) on board the NOAA- and MetOp- satellite series. The present use case analyses the AMSU-B data of the NOAA-16 satellite.

For the 183GHz microwave channel the UTH is retrieved under all situations, except where measurements are influenced by surface (high orography or extremely dry atmospheric conditions), or when there is a deep convective storm with large ice particules (see [[2]](https://doi.org/10.1029/2006GL029118), [[6]](https://doi.org/10.1029/2007JD009314)).

The map below shows the UTH for July 2001.

*Figure 1: Monthly average of UTH (in % of relative humidity) for July 2001.*

As expected (see [[5]](https://doi.org/10.1029/93JD01283), [[8]](https://doi.org/10.5194/acp-14-11129-2014)) high UTH values are associated to monsoonal regions of strong storms that bring upward a high amount of moisture : in the Eastern Pacific, over West Africa and in the Indian Ocean and Indonesia (known as the Warm Pool).
On the other side of the UTH range, a drier atmosphere is visible in the continental desert regions, and more generally in both hemispheres, North and South of the Monsoonal regions.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Overview of the geophysical variables of interest > Atmospheric vertical velocity at 500 hPa
---
The map below shows the $\omega_{500}$ for the same month of July 2001, overlayed with contours of UTH of the same month.

```text
Text(0.5, 1.0, 'July 2001 - monthly average')
```

*Figure 2: Monthly average of the vertical velocity at 500hPa (in hPa/day) for July 2001, from the ERA5 reanalyses. The overlaid contours are UTH from Figure 1.*

Negative values of $\omega_{500}$ indicate ascending motions, while positive values indicate descending motions.

The contours of UTH, overlayed on the map, help to interpret the structures.

From the general atmospheric circulation, ascending motions ($\omega_{500}$ < 0) are regions where storms form by convection. These regions form the moist Inter-Tropical Convergence Zone (ITCZ), a region of strong convergence of the surface winds of the Hadley cells. The convective storms moisten the troposphere by bringing upward the moisture from the boundary layer.

Descending motions ($\omega_{500}$ > 0) are dry regions that are free of storm clouds and are mainly located over the subtropics (near 20°S - 20°S). In these subsidence regions, the air sinks and warms adiabatically without changing its water vapour content, thus decreasing the rate of relative humidity.

Therefore, $\omega_{500}$ serves to define __large-scale circulation regimes__, as stated in [[3]](https://doi.org/10.1007/s00382-003-0369-6), extremely useful to decompose the dynamical structure of the tropical atmosphere and to analyze its properties (for the purpose of studying trends in precipitation [[11]](https://doi.org/10.1256/qj.04.176) or for the purpose of studying cloud radiative forcing [[3]](https://doi.org/10.1007/s00382-003-0369-6), among other applications). Many studies use this framework to assess global climate models ([[2]](https://doi.org/10.1029/2006GL029118), [[3]](https://doi.org/10.1007/s00382-003-0369-6), [[11]](https://doi.org/10.1256/qj.04.176)).

The following density histogram illustrates the probability density function (PDF) of $\omega_{500}$ for the full year 2001.

```text
<>:18: SyntaxWarning: invalid escape sequence '\o'
<>:18: SyntaxWarning: invalid escape sequence '\o'
/data/wp5/.tmp/ipykernel_2407812/2054132186.py:18: SyntaxWarning: invalid escape sequence '\o'
  plt.xlabel('circulation regimes - $\omega_{500}$ [hPa/day]')
```

*Figure 3: Probability density functions (PDF) of the monthly $\omega_{500}$ for the year 2001 in the tropics (30°N - 30°S). Each light-blue PDF is for one given month. The red marks and vertical bars show the average PDF for year 2001 and the associated standard deviation, considered as the uncertainty, within each 10-hPa bin.*

Overall the distribution of $\omega_{500}$ is negatively skewed, with a peak around 20 hPa/day. This peak, compliant with [[3]](https://doi.org/10.1007/s00382-003-0369-6), shows that the tropics are dominated by a weak subsiding motion found in the subtropics and in the western parts of the ocean basins.
The negative tail reflects the magnitude of vertical motions within clouds.
The small vertical bars highlight the small temporal variability in the occurrence of each bins of $\omega_{500}$ throughout the year.

(Plot-and-results)=


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Plot and description of the results > Variation of UTH with the vertical velocity at 500hPa
---
For a given latitude$\times$longitude$\times$month the UTH is associated a value of $\omega_{500}$, defining a circulation regime, and each bin of $\omega_{500}$ groups all UTH for this particular regime.

The entire year 2001 is considered, and month-to-month variability is presented.

mean within each bin

```text
<>:25: SyntaxWarning: invalid escape sequence '\o'
<>:25: SyntaxWarning: invalid escape sequence '\o'
/data/wp5/.tmp/ipykernel_2407812/1983021514.py:25: SyntaxWarning: invalid escape sequence '\o'
  xlabel="circulation regimes - $\omega_{500}$ [hPa/day]",
```

*Figure 4: Evolution of UTH upon different circulation regimes of the tropics, defined by 10-hPa bins of $\omega_{500}$, for each month of year 2001. For each bin of $\omega_{500}$, a box-and-whiskers diagram is used to show the month-to-month variability: the gray boxes are the interquartiles and the median are in grey. The green symbols represent the mean of the UTH of each bin of the given months. The red circles are the mean of the means (ie the annual mean) within each bin.*

Overall the results are in agreement with Figure 1 of [[2]](https://doi.org/10.1029/2006GL029118), which associates the retrieved monthly UTH from NOAA-16 to the vertical velocity at 500hPa, but for only 2 months of data which are November and December 2001. In agreement with this study, the UTH shows a sharp transition from convergence regions to subsidence regions. The UTH value remains stable at around 20% over regions where $\omega_{500}$ exceeds 30 hPa/day.

Figure 4 shows that there is little month-to-month variability within a given circulation regime, the largest variability being observed in the extreme ascending regimes, for $\omega_{500}$ > 55 hPa/day. This is essentially explained by the small occurrence of such regimes, as visible on Figure 1.

Caution must be taken though when comparing the occurence of circulation regimes between the present use case and the literature : the use case is based on ERA5 vertical velocity while the literature ([[2]](https://doi.org/10.1029/2006GL029118) and [[3]](https://doi.org/10.1007/s00382-003-0369-6)) rely on former versions of ECMWF reanalysis ERA40 ([[11]](https://doi.org/10.1256/qj.04.176)) and NCEP/NCAR reanalyses ([[12]](https://doi.org/10.1175/1520-0477%281996%29077<0437:TNYRP>2.0.CO;2)). Therefore, differences in the present results and the literature may arise from differences in their horizontal resolutions, in their vertical description of the atmosphere, in the version of the assimilation system and in the atmospheric model

Here, the UTH reaches a plateau of maximum value of approximately 45-50% when upward motions reach -60 hPa/day. The reference reports slightly lower values, which still fall within the UTH distribution observed in this study. The discrepancies between these results and those presented in [[2]](https://doi.org/10.1029/2006GL029118) can arise from the following reasons:

- The retrieval method for UTH is similar, based on a simple linear equation ([[2]](https://doi.org/10.1029/2006GL029118), [[4]](https://doi.org/10.1175/1520-0450%281988%29027%3C0889:EOTUTR%3E2.0.CO;2)) (see also the __"Methodology"__ section):

$$
  \text{ln}(UTH) = a + b \cdot TB
  $$


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > Analysis and results > Plot and description of the results > Variation of UTH with the vertical velocity at 500hPa
---
%3E2.0.CO;2)) (see also the __"Methodology"__ section):

$$
  \text{ln}(UTH) = a + b \cdot TB
  $$

however, the regression coefficients $a$ and $b$ used to obtain the UTH from the TB measured by the instrument are not identical between the UTH dataset of the CDS [[see the ATBD]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=288339045) and the UTH of the reference paper [[2]](https://doi.org/10.1029/2006GL029118), which may explain some of the differences in the binned UTH per $\omega_{500}$. Firstly, those parameters are computed using offline radiative transfer codes that have some discrepancies in the water vapor absorption channels (see for instance [[[14]](https://doi.org/10.1175/1520-0477%282000%29081%3C0797:AIORCF%3E2.3.CO;2), [[15]](https://www.wcrp-climate.org/resources/wcrp-publications/1095-pub-2017)). Secondly, the dataset used to compute the regression coefficients, and considered as a reference dataset, is built from different sources : a collection of 5000 representative profiles for the CDS dataset [[see the ATBD, pages 9 and 10]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=288339045), and an extraction of profiles from ERA40 reanalyses (see explanations in [[2]](https://doi.org/10.1029/2006GL029118)).

- The results in [[2]](https://doi.org/10.1029/2006GL029118) are based on only two months of data (November and December 2001) using  former versions of reanalyses (ERA40) for $\omega_{500}$, which can lead to discrepancies in the amplitudes and occurences when compared to our more comprehensive dataset, even if limited to only one year.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > ℹ If you want to know more > Key resources
---
Some key resources and further reading were linked throughout this assessment.

The CDS catalogue entries for the data used is:
- Upper tropospheric humidity gridded data from 1999 to present derived from satellite observations:
https://cds.climate.copernicus.eu/datasets/satellite-upper-troposphere-humidity

- ERA5 hourly data on pressure levels from 1940 to present:
https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels

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/)

And more resources on tropical atmosphere, large-scale circulation and upper tropospheric humidity :

- https://www.nature.com/scitable/knowledge/library/tropical-weather-84224797/

- Additonal scientific articles on Upper Tropospheric Humidity :

- IPCC Assessment Report 4, 2007 : 3.4.2.2 Upper Tropospheric Water Vapor, in WG1: The Physical Science Basis, https://archive.ipcc.ch/publications_and_data/ar4/wg1/en/ch3s3-4-2-2.html
  - Shi, L., Schreck III, C. J., John, V. O., Chung, E.-S., Lang, T., Buehler, S. A., and Soden, B. J., 2022: Assessing the consistency of satellite-derived upper tropospheric humidity measurements, Atmos. Meas. Tech., 15, 6949–6963, https://doi.org/10.5194/amt-15-6949-2022
  - Chung E., B. Soden, B.J. Sohn, and L. Shi, 2014: Upper-tropospheric moistening in response to anthropogenic warming, Proc. Natl. Acad. Sci. U.S.A. 111 (32) 11636-11641, https://doi.org/10.1073/pnas.1409659111


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > ℹ If you want to know more > References
---
[[1]](https://doi.org/10.1029/97GL03563) Pierrehumbert R. T (1998), Lateral mixing as a source of subtropical water vapor, Geophys. Res. Lett., 25, 151-154, doi:10.1029/97GL03563

[[2]](https://doi.org/10.1029/2006GL029118) Brogniez, H., and R. T. Pierrehumbert (2007), Intercomparison of tropical tropospheric humidity in GCMs with AMSU-B water vapor data, Geophys. Res. Lett., 34, L17812, doi:10.1029/2006GL029118.

[[3]](https://doi.org/10.1007/s00382-003-0369-6) Bony, S., Dufresne, JL., Le Treut, H. et al. (2004), On dynamic and thermodynamic components of cloud changes. Climate Dynamics 22, 71–86. doi:10.1007/s00382-003-0369-6

[[4]](https://doi.org/10.1175/1520-0450%281988%29027%3C0889:EOTUTR%3E2.0.CO;2) Schmetz J. and O. Turpeinen (1988), Estimation of the Upper Tropospheric Relative Humidity Field from METEOSAT Water Vapor Image Data, J. Appl. Meteor. Clim., 27, 8, 889-899. doi:10.1175/1520-0450(1988)027%3C0889:EOTUTR%3E2.0.CO;2.

[[5]](https://doi.org/10.1029/93JD01283) Soden B. and F. Bretherton (1993), Upper tropospheric relative humidity from the GOES 6.7 μm channel: Method and climatology for July 1987, J. Geophys. Res, 98, D9, 16,669-16,688. doi:/10.1029/93JD01283.

[[6]](https://doi.org/10.1029/2007JD009314) Buehler, S. A., M. Kuvatov, V. O. John, M. Milz, B. J. Soden, D. L. Jackson, and J. Notholt (2008), An upper tropospheric humidity data set from operational satellite microwave data, J. Geophys. Res., 113, D14110, doi:10.1029/2007JD009314.

[[7]](https://doi.org/10.1175/2009JCLI2963.1) Brogniez H., R. Roca and L. Picon (2009), A study of the free tropospheric humidity interannual variability using Meteosat data and an advection–condensation transport model, J. Clim., 22, 24, 6773–6787, doi:10.1175/2009JCLI2963.1.

[[8]](https://doi.org/10.5194/acp-14-11129-2014) Schröder, M., R. Roca, L. Picon, A. Kniffka, and H. Brogniez (2014) Climatology of free tropospheric humidity: extension into the SEVIRI era, evaluation and exemplary analysis, Atmos. Chem. Phys., 14, 11129-11148, doi:10.5194/acp-14-11129-2014.

[[9]](https://doi.org/10.5194/acp-7-2489-2007) Chung E. S., B. J. Sohn, J. Schmetz and M. Koening (2007) Diurnal variation of upper tropospheric humidity and its relations to convective activities over tropical Africa, Atmos. Chem. Phys., 7, 2489–2502, doi:10.5194/acp-7-2489-2007.


EQC Quality Assessment: "Studying the influence of atmospheric circulation on upper tropospheric humidity"
Dataset: satellite-upper-troposphere-humidity [CDS]
Aspect: validation_q01  |  Category: Satellite_ECVs
Section: Studying the influence of atmospheric circulation on upper tropospheric humidity > ℹ If you want to know more > References
---
humidity and its relations to convective activities over tropical Africa, Atmos. Chem. Phys., 7, 2489–2502, doi:10.5194/acp-7-2489-2007.

[[10]](https://doi.org/10.1175/JCLI-D-19-0046.1) Tivig M., V. Grützun, V. O. John and S. Buehler (2020), Trends in upper-tropospheric humidity: expansion of the subtropical dry zones? J. Clim. 33, 6, 2149–2161, doi:10.1175/JCLI-D-19-0046.1.

[[11]](https://doi.org/10.1256/qj.04.176) Uppala S. and co authors (2005), The ERA-40 re-analysis, Q. J. R. Meteorol. Soc., 31, 2961– 3012, doi: 10.1256/qj.04.176.

[[12]](https://doi.org/10.1175/1520-0477%281996%29077<0437:TNYRP>2.0.CO;2) Kalnay E. and co authors (1996) The NCEP/NCAR 40-year reanalysis project. Bull. Am. Meteorol. Soc., 77, 437–471, doi:10.1175/1520-0477(1996)077<0437:TNYRP>2.0.CO;2.

[[13]](https://doi.org/10.1088/1748-9326/5/2/025205) Allan R., B. Soden, V. John, W. Ingram and P. Good (2010) Current changes in tropical precipitation, Environ. Res. Lett., 5, 025205, doi:10.1088/1748-9326/5/2/025205.

[[14]](https://doi.org/10.1175/1520-0477%282000%29081%3C0797:AIORCF%3E2.3.CO;2) Soden, B., and coauthors (2000), An Intercomparison of Radiation Codes for Retrieving Upper-Tropospheric Humidity in the 6.3-μm Band: A Report from the First GVaP Workshop. Bull. Amer. Meteor. Soc., 81, 797–808. doi:10.1175/1520-0477%282000%29081<0797:AIORCF>2.3.CO;2

[[15]](https://www.wcrp-climate.org/resources/wcrp-publications/1095-pub-2017) Schroeder, M., M. Lockhoff, L. Shi, T. August, R. Bennartz, E. Borbas, H. Brogniez, X. Calbet, S. Crewell, S. Eikenberg, F. Fell, J. Forsythe, A. Gambacorta, K. Graw, S.-P. Ho, H. Hoeschen, J. Kinzel, E. R. Kursinski, A. Reale, J. Roman, N. Scott, S. Steinke, B. Sun, T. Trent, A. Walther, U. Willen, Q. Yang, 2017: GEWEX water vapor assessment (G-VAP). WCRP Report 16/2017, World Climate Research Programme, Geneva, Switzerland, 216 pp.


EQC Quality Assessment: "Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)"
Dataset: satellite-aerosol-properties [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)
---

Production date: 30-09-2024

Produced by: Consiglio Nazionale delle Ricerche ([CNR](https://www.cnr.it/en)- Fabio Madonna and Faezeh Karimian.)


EQC Quality Assessment: "Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)"
Dataset: satellite-aerosol-properties [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023) > Quality assessment questions
---
**• Can satellite data consistently and homogeneously capture the seasonal variability of AOD in different parts of the world?**

**• Is the temporal coverage of aerosol satellite data sufficient to characterize the aerosol global distribution in the period 2017-2023?**

In the context of climate change and natural atmospheric variability, understanding the spatial and temporal distribution of aerosols is crucial for analyzing phenomena such as air quality, global warming, and public health. Seasonal variations in aerosols are influenced by numerous factors, including biological cycles, seasonal fires, desert dust, and industrial activities [1, 2, 3]. However, uncertainties in satellite data, stemming from limitations in temporal coverage and spatial resolution, could affect our ability to draw reliable conclusions [1].

This assessment uses the SLSTR aerosol ensemble product at Level-2 super-pixel resolution (10 × 10 km²), an uncertainty-weighted average of the SDVV, ORAC and Swansea retrievals, to evaluate its ability to consistently and homogeneously capture the seasonal variability of Aerosol Optical Depth (AOD) across different global regions. The central question we aim to answer is whether satellite data, despite their limitations and uncertainties, can accurately capture the atmospheric dynamics that influence the distribution of aerosols over the years [2, 4].

The dataset used in this study is the CDS (Climate Data Store) AOD product derived from SLSTR onboard Sentinel-3, providing monthly gridded AOD values at 550 nm at approximately 1° spatial resolution in NetCDF format, covering the period 2017–2023.

In summary, this study investigates whether SLSTR data can be reliably used to monitor the seasonal variability of AOD by comparing different geographic regions and assessing whether the spatial coverage is sufficient to characterize AOD distribution globally.

The results show that the satellite-derived AOD estimates are consistent with other findings reported in the literature [1, 4, 5] and are supported by a thorough scientific analysis. This consistency suggests that SLSTR data can be a robust tool for monitoring global aerosol distribution.


EQC Quality Assessment: "Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)"
Dataset: satellite-aerosol-properties [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023) > Quality assessment statement
---
These are the key outcomes of this assessment

• The SLSTR aerosol data exhibits consistency with other satellite-derived datasets, particularly in regions like Southern Africa and Southeast Asia [3, 6].

• Coarse-mode AOD trends over North Africa and the Mediterranean Basin align with findings from previous studies, such as Cuevas-Agulló et al. (2024), highlighting an increase in dust intrusions during 2020–2022 [2].

• The absence of data for several months in East Asia and Siberia restricts a full understanding of regional aerosol dynamics.

• A weaker correlation between Fine Mode AOD and Total AOD in regions like North Africa and India suggests the presence of varying aerosol types and the influence of mineral dust transport.

## 📋 Methodology

This assessment aims to assess the consistency and completeness of SLSTR-derived satellite data in capturing the seasonal variability of Aerosol Optical Depth (AOD) over different global regions from 2017 to 2023.

The methodology adopted for the analysis is split into the following steps:

[](section-1)
* Import all required libraries
* Spatial and temporal definitions

[](section-2)
* Download AOD data
* Computation of time series
* Define required functions

[](section-3)
* Prepare the dataframe
* Global map plots: mean AOD and FM-AOD
* Mapping of subregions
* Regional AOD and FM-AOD time series plots
* Observation count plot for AOD550 averages
* Scatter plot of fine-mode AOD vs. total AOD

[](section-4)

## 📈 Analysis and results
(section-1)=
### Choose the data to use and set up the code

#### Import all required libraries
In this section, we import all the relevant packages needed for running the notebook.

#### Spatial and temporal definitions

The analysis performed in this assessment focuses on data from July 2017 to June 2023, using monthly averages from the SLSTR sensor on Sentinel-3A. The regions are grouped into meaningful areas like North America, Europe, Africa, and Asia, each defined by specific latitude and longitude ranges. These subregions are color-coded for easy comparison in plots.

Assign time dimension and compute means
Optional color palette for consistency
Define subregions

(section-2)=
### Data Retrieval and Preparation

In this step, the selected satellite data is downloaded using predefined time and variable settings. The data is then formatted to include a clear time coordinate, and average maps of key variables like AOD550 and FM_AOD550 are calculated over the full time period to prepare for analysis and visualization.

#### Download AOD data

```text
100%|██████████| 72/72 [00:07<00:00,  9.06it/s]
```


EQC Quality Assessment: "Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)"
Dataset: satellite-aerosol-properties [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023) > Quality assessment statement
---
%|██████████| 72/72 [00:07<00:00,  9.06it/s]
```

```text
<xarray.Dataset> Size: 75MB
Dimensions:                      (source: 72, latitude: 180, longitude: 360)
Coordinates:
  * source                       (source) object 576B '148c29e9a49fd1261d149a...
  * latitude                     (latitude) float32 720B -89.5 -88.5 ... 89.5
  * longitude                    (longitude) float32 1kB -179.5 -178.5 ... 179.5
Data variables:
    AOD550                       (source, latitude, longitude) float32 19MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    FM_AOD550                    (source, latitude, longitude) float32 19MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    AOD550_UNCERTAINTY_ENSEMBLE  (source, latitude, longitude) float32 19MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
    NMEAS                        (source, latitude, longitude) float32 19MB dask.array<chunksize=(1, 180, 360), meta=np.ndarray>
Attributes: (12/18)
    Conventions:               CF-1.6
    creator_email:             thomas.popp@dlr.de
    creator_name:              German Aerospace Center, DFD
    geospatial_lat_max:        90.0
    geospatial_lat_min:        -90.0
    geospatial_lon_max:        180.0
    ...                        ...
    sensor:                    SLSTR
    standard_name_vocabulary:  NetCDF Climate and Forecast (CF) Metadata Conv...
    summary:                   Level 3 aerosol properties retreived using sat...
    title:                     Ensemble aerosol product level 3
    tracking_id:               d79c7d5c-a788-11ee-9e43-0050569370b0
    version:                   v2.3
```

#### Computation of time series

To analyze changes over time, a monthly time coordinate was created and assigned to the dataset. The data’s internal “source” dimension was replaced with this new time axis, allowing for clear temporal analysis. Then, the mean values of AOD550 and FM_AOD550 were calculated across the full time period, producing overall average maps for these key aerosol indicators.

Assign time dimension and compute means
Add time as a coordinate based on the 'source' dimension

#### Define required functions

The following functions were defined to support the analysis of aerosol data:

The function "plot_global_map" creates a global map of AOD values, adding coastlines, borders, and gridlines for geographic context.

"plot_multi_series_aod" generates seasonal time series for each subregion, showing trends in AOD or fine-mode AOD, with uncertainty as a shading.

"plot_observation_counts_with_stats_on_plot" shows how many data points were used in each region per month, helping assess data coverage.

"scatter_fine_vs_total" creates scatter plots comparing total AOD with fine-mode AOD, including regression lines and R² values to reveal correlations across regions.

Collect all monthly means to find global y-limits
Concatenate all monthly mean series and find min/max ignoring NaNs
Optionally, add a small margin for visual comfort
Calculate area in km² and normalize
Optionally set upper limit to 100 if values exceed it
Plot 1:1 line and set axis limits
Remove any empty axes

(section-3)=
### Plot and describe the results

#### Global map plots: mean AOD and FM-AOD

The first map shows the mean Aerosol Optical Depth (AOD550) over the globe. Higher AOD values, shown in darker orange, are observed over regions with frequent dust storms (e.g. the Sahara), biomass burning (e.g. central Africa), and industrial pollution (e.g. parts of India and China).

The second map displays the mean Fine-mode AOD550, which focuses on smaller particles like smoke, urban pollution, and secondary aerosols, dominating aerosol loading over central Africa, Southeast Asia, and parts of South America. These areas associated with biomass burning and anthropogenic emissions.

Together, these plots help distinguish between regions dominated by coarse-mode aerosols (e.g. dust) and those with higher concentrations of fine-mode particles.

*Figure 1.* Global distribution of mean Aerosol Optical Depth (AOD550) and Fine-mode AOD550 from the SLSTR sensor onboard Sentinel-3A, averaged over the period July 2017 to June 2023.


EQC Quality Assessment: "Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)"
Dataset: satellite-aerosol-properties [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023) > Quality assessment statement
---
*Figure 1.* Global distribution of mean Aerosol Optical Depth (AOD550) and Fine-mode AOD550 from the SLSTR sensor onboard Sentinel-3A, averaged over the period July 2017 to June 2023.

#### Mapping of subregions
This section defines the geographic boundaries of the subregions used for regional aerosol analysis. Each domain is represented as a simple rectangular box defined by latitude and longitude limits, enabling consistent spatial aggregation of Aerosol Optical Depth (AOD) data across different parts of the globe. The map visualizes these subregions to aid interpretation of subsequent regional time series and correlation analyses.

*Figure 2.* The spatial domains of the subregions used in the aerosol analysis. Each red rectangle represents the geographic boundaries defined by latitude and longitude for a specific subregion.

#### Regional AOD and FM-AOD time series plots
The time series of 550 nm AOD monthly means from SLSTR satellite data refer to subregions selected because of the similarity in the observed aerosol types: Northern Industrial Economies, Southern Biomass Burning, Dust-Dominated Regions, and Asia. Each subplot includes shaded uncertainty areas (±1σ) quantifying AOD variability.
In the AOD550 plot, Northern industrial regions display often have AOD peaks in summertime, while, since 2020 the Mediterranean Basin shows high AOD levels throughout the year, strongly influenced by the mineral dust intrusion consistently with a documented enhancement of dust emissions in Northern Africa [4].
The FM_AOD550 is also reported showing that biomass burning regions, like Southern Africa, have peaks during dry periods typically dominated by forest fires [2, 3].

The observed data gaps in regions such as Siberia primarily arise from the inherent limitations of satellite aerosol retrievals in polar and subpolar areas. According to the Product User Guide (PUG), the SLSTR aerosol product has reduced temporal coverage in these high-latitude regions due to the absence of sunlight during extended winter periods, which prevents reliable satellite observations (Table 6, footnote 1). This sunlight dependency means that aerosol data cannot be retrieved when there is no solar illumination, resulting in missing data during polar night [7].

*Figure 3.* Monthly mean time series of total AOD at 550 nm from SLSTR for different sub-regions. Each panel shows regional time series with uncertainty as a shaded area (±1σ). Northern industrial regions show peaks in summer. The Mediterranean Basin maintains high AOD values year-round, especially in the period 2020-2022.

*Figure 4.* Monthly mean time series of fine-mode AOD550 from SLSTR for selected global sub-regions. Fine mode aerosols include smoke, urban pollution, and secondary particles. Biomass burning regions like South America and Southern Africa show peaks during dry periods.

#### Observation count plot for AOD550 averages

For assessing the spatial completeness of the data used in the time series analysis, this plot displays the normalized observation density, i.e., the number of valid AOD550 observations per 1,000,000 km², used each month to compute regional averages. Each line represents a distinct subregion, allowing visual comparison of data coverage over time, normalized by region size.

Most regions, such as South America, exhibit consistently high normalized observation densities, typically ranging from 60 to 80 counts per 1,000,000 km². In contrast, areas like Siberia show larger variability and lower density, implying a higher uncertainty for the derived monthly statistics and trend assessments.
The lower number of aerosol observations in Siberia is mainly due to its high-latitude location and harsh environmental conditions. As highlighted in the Product User Guide (PUG), satellite retrievals rely on sunlight, which is limited or absent during the extended polar night in winter months, reducing the temporal coverage in this region [7].

```text
/data/wp5/.tmp/ipykernel_1257448/4247846240.py:144: MatplotlibDeprecationWarning: The get_cmap function was deprecated in Matplotlib 3.7 and will be removed in 3.11. Use ``matplotlib.colormaps[name]`` or ``matplotlib.colormaps.get_cmap()`` or ``pyplot.get_cmap()`` instead.
  colors = cm.get_cmap('tab20', len(all_regions))
```

```text
Y-axis limits before setting: (np.float64(-4.058112166220274), np.float64(85.22035549062575))
Y-axis limits after setting: (np.float64(-4.058112166220274), np.float64(85.22035549062575))
```

*Figure 5.* Normalized observation density for each sub-region, showing the number of valid observations per 1,000,000 km² over time. The maximum normalized observation density reached approximately 85 counts / 1,000,000 km².

#### Scatter plot of Fine Mode AOD vs total AOD


EQC Quality Assessment: "Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)"
Dataset: satellite-aerosol-properties [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023) > Quality assessment statement
---
number of valid observations per 1,000,000 km² over time. The maximum normalized observation density reached approximately 85 counts / 1,000,000 km².

#### Scatter plot of Fine Mode AOD vs total AOD

This figure shows a series of scatter plots comparing Fine Mode Aerosol Optical Depth (FM_AOD550) with Total AOD (AOD550) for each subregion, based on monthly averages. Each subplot includes a red regression line with its corresponding equation and R² value, indicating the strength of the linear relationship.
All regions display a high correlation, suggesting that fine-mode particles—such as smoke, continental aerosols, and anthropogenic pollution—contribute significantly to total aerosol loading.
Regions with smaller R² values, such as North Africa and India, indicate a greater contribution from coarse and giant particles to the total AOD.
These plots are useful for understanding regional differences in aerosol size and type, the latter known the local aerosol climatology.

*Figure 6.*  Scatter plots of monthly mean Fine Mode Aerosol Optical Depth (FM_AOD550) versus Total Aerosol Optical Depth (AOD550) for selected global subregions. Each panel includes a linear regression line (in red) with the equation and coefficient of determination (R²) indicated. The black dashed line represents the 1:1 line, highlighting perfect agreement between the two variables.

(section-4)=
### Take-Home Messages

The current aerosol climatology shows substantial deviations from earlier studies [1]. Over North Africa and the Mediterranean Basin, a significant increase in mineral dust intrusions was observed during 2020–2022, as reported by Cuevas-Agulló et al. (2024). This increase is associated with enhanced activity of high-pressure systems over the Euro-Atlantic sector, which favour the obstruction of the westerlies and the occurrence of cut-off lows at subtropical latitudes.
During the same period, the coarse-mode AOD fraction increased over the Mediterranean, further confirming the influence of mineral dust transport due to the atmospheric circulation in the Mediterranean Basin.
The weaker correlation between fine-mode and total AOD reflects the greater contribution of coarse-mode particles, particularly dust, rather than inconsistencies in retrieval.
In the Northern Hemisphere, seasonal variability remains generally consistent across the selected regions, dominated by continental aerosols and anthropogenic pollution.
Strong seasonal peaks are also observed in:
- Southern Africa, where higher AOD values in the Southern hemisphere are consistent with the occurrence of forest fires during the dry austral season (Aug-Sep-Oct) [2, 3].
- Southeast Asia, where AOD is influenced by local fires and aerosol transport from northern Australia [5].
In Siberia, several months of data are missing from the ensemble retrieval, making it difficult to assess seasonal variability.

## ℹ If you want to know more

### Key Resources

• Aerosol properties gridded data from 1995 to present derived from satellite observations:

https://cds.climate.copernicus.eu/datasets/satellite-aerosol-properties?tab=overview

Code libraries used:

• C3S EQC custom function, c3s_eqc_automatic_quality_control, prepared by B-Open

### References

[1] Remer, L. A., et al. (2008). Global aerosol climatology from the MODIS satellite sensors. Journal of Geophysical Research: Atmospheres, 113(D14S07). https://doi.org/10.1029/2007JD009661

[2] Nyasulu, M., Haque, M.M., Musonda, B. et al. The long-term spatial and temporal distribution of aerosol optical depth and its associated atmospheric circulation over Southeast Africa. Environ Sci Pollut Res 29, 30073–30089 (2022). https://doi.org/10.1007/s11356-021-18446-7

[3] Flamant, C., Chaboureau, J.-P., Gaetani, M., Schepanski, K., and Formenti, P.: The radiative impact of biomass burning aerosols on dust emissions over Namibia and the long-range transport of smoke observed during the Aerosols, Radiation and Clouds in southern Africa (AEROCLO-sA) campaign, Atmos. Chem. Phys., 24, 4265–4288, https://doi.org/10.5194/acp-24-4265-2024, 2024.

[4] Cuevas-Agulló, E. et al. (2024). Sharp increase in Saharan dust intrusions over the western Euro-Mediterranean... https://doi.org/10.5194/acp-24-4083-2024

[5] Jin, S. et al. (2023). A comprehensive reappraisal of long-term aerosol characteristics... https://doi.org/10.5194/acp-23-8187-2023


EQC Quality Assessment: "Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023)"
Dataset: satellite-aerosol-properties [CDS]
Aspect: consistency_q01  |  Category: Satellite_ECVs
Section: Assessment of Seasonal Variability and Completeness of Aerosol Optical Depth from SLSTR Satellite Data (2017-2023) > Quality assessment statement
---
-2024

[5] Jin, S. et al. (2023). A comprehensive reappraisal of long-term aerosol characteristics... https://doi.org/10.5194/acp-23-8187-2023

[6] Garrigues, S., Remy, S., Chimot, J., Ades, M., Inness, A., Flemming, J., Kipling, Z., Laszlo, I., Benedetti, A., Ribas, R. and Jafariserajehlou, S., 2022. Monitoring multiple satellite aerosol optical depth (AOD) products within the Copernicus Atmosphere Monitoring Service (CAMS) data assimilation system. Atmospheric Chemistry and Physics Discussions, 2022, pp.1-80.

[7] Copernicus Climate Change Service (2021). Product User Guide and Specifications for Satellite Aerosol Properties (C3S2_312a_Lot2_DLR_2021SC1). Copernicus Climate Change Service. https://dast.copernicus-climate.eu/documents/satellite-aerosol-properties/C3S2_312a_Lot2_FDDP-AER/C3S2_312a_Lot2_D-WP2-FDDP-AER_202311_PUGS_AER_v2.0_final2.pdf


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Quality assessment question
---
* **Is the temporal consistency of the Level-3 Obs4MIPs merged dataset suitable for calculating changes in the annual growth rates of XCO₂ and the amplitude of the seasonal cycles?**

The most important anthropogenic greenhouse gas is carbon dioxide (CO$_2$), with CO$_2$ emissions contributing 66% of the total radiative forcing by long-lived greenhouse gases [[1]](https://library.wmo.int/idurl/4/69057). It is therefore important to monitor the long-term changes in atmospheric CO$_2$ concentrations, to understand the sources and sinks of the atmospheric carbon content and to provide reliable projections of future CO$_2$ concentrations under various scenarios.

Earth System Models (ESMs) are computer simulations that couple the physical climate (i.e. the atmosphere, the oceans and sea ice) with other components of the Earth, such as the land surface, vegetation, biogeochemistry (i.e. the carbon and nitrogen cycles), the cryosphere and, in some cases, human systems, in order to simulate how the entire Earth behaves now and in the future [[2]](https://doi.org/10.5194/gmd-9-1937-2016). They are widely used, among other aims, to provide climate projections under different greenhouse gas and land use scenarios, to determine the contribution of human activities to observed changes or specific extreme events, to investigate biogeochemical cycles, and to inform mitigation pathways and climate risk analysis for policymakers.

In this assessment, we show how the column-averaged mixing ratios of CO$_2$ (XCO$_2$) provided by the Obs4MIPs Level 3 data product of [[3]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) can be used to evaluate the annual growth rates and seasonal cycles of CO$_2$ provided by ESMs. Previous versions of this dataset have been already used to calculate global annual growth rates of CO$_2$ ([[4]](https://doi.org/10.5194/acp-18-17355-2018), [[5]](https://doi.org/10.5194/amt-13-789-2020)). These studies reported good agreement between growth rates derived from satellite data and those derived from surface observations by the National Oceanic and Atmospheric Administration (NOAA).

The Obs4MIPs Level 3 data product results from integrating XCO₂ data from different combinations of satellite sensors and algorithms over time and space. Based on the results of references [[6]](https://doi.org/10.5194/bg-17-6115-2020) and independent analyses, we investigate the impact of changes in the sampling characteristics of the Obs4MIPs dataset on the ability to use it to evaluate the capacity of ESMs to reproduce CO₂ seasonal cycle amplitudes and related trends/inter-annual variability, across its entire temporal coverage.


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Quality assessment statement
---
These are the key outcomes of this assessment

* The Obs4MIPs Lev3 merged dataset is suitable for assessing the capacity of Earth System Models to reproduce CO₂ growth rates and seasonal cycle variability, however the dataset features related to the spatial and temporal data coverage must be carefully considered (see points below).
* For annual aggregation, high-latitude regions (i.e.. latitudes higher than 60°N and 60°S) should not be considered due to non-uniform data coverage across the different seasons.
* The introduction of new satellite data (e.g., GOSAT in 2009 and OCO-2 in 2015) affects the data coverage at global scale with changing coverage of high latitude regions and oceans.
* The months characterised by changes in the contributing satellite data can show inconsistent CO₂ growth rates; however, this feature is averaged out when computing the annual growth.
* Changes in data coverage over the period covered by the Obs4MIPs Level 3 dataset affect the calculation of the seasonal cycle amplitude.
```


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Methodology
---
To assess if the temporal consistency of the Level-3 Obs4MIPs merged dataset from the Climate Data Store “Carbon dioxide data from 2002 to present derived from satellite observations” [[3]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) is suitable for calculating changes in the annual growth rates of XCO$_2$ and the amplitude of the seasonal cycles, we inspected a specific case study available from scientific literature  [[6]](https://doi.org/10.5194/bg-17-6115-2020) and we performed independent analyses. In particular, we analysed the changes of data coverage as a function of months and years, we calculated the monthly resolved CO$_2$ annual growth rates and the annual seasonal cycles. To do that we used a methodology consistent with [[6]](https://doi.org/10.5194/bg-17-6115-2020). However, we perform our analyses over the period 2003 - 2023 by using the most recent version (4.6) of the Level-3 Obs4MIPs merged dataset [[7]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160605). This dataset is obtained by gridding the merged level-2 product generated with the ensemble median algorithm (XCO2_EMMA). EMMA combines several different XCO2 level-2 satellite data products: SCIAMACHY/Envisat (2003–2012), TANSO-FTS/GOSAT (2009–2022), OCO-2 (2014-2022), TANSO-FTS-2/GOSAT2 (2019-2023).

[[4]](https://doi.org/10.5194/acp-18-17355-2018) used a previous version (O4Mv3) of the Level-3 Obs4MIPs merged data product available by the Climate Data Store to calulate the global atmospheric CO$_2$ annual mean growth rates during 2003–2016 and to analyse the relative contributions of anthropogenic emissions and El Niño Southern Oscillation (ENSO) to the observed growth rates. This study reported a  good agreement (mean difference ± standard deviation: 0.0 ± 0.3 ppm/year; R: 0.82)

More specifically, this notebook aims to:
* Generate global maps of monthly XCO$_2$ data coverage, to show that the spatial coverage of data is not uniform across the different regions of the world for the different seasons.
* Generate global maps of  XCO$_2$ data coverage as a function of the different temporal periods covered by different satellite/sensors, i.e. SCIAMACHY/Envisat (2003–2012), TANSO-FTS/GOSAT (2009–2023), OCO-2 (2014-2023), TANSO-FTS-2/GOSAT2 (2019-2023).
* To calculate the global annual growth rates of XCO$_2$ and the seasonal cycle amplitudes for the entire measurement period 2003-2022.

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

**[](template:section-1)**
 * Import all relevant packages.
 * Select the variable of interest
 * Cache needed functions.

**[](template:section-2)**
 * Define the data request to CDS.

**[](template:section-3)**


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Methodology
---
Select the variable of interest
 * Cache needed functions.

**[](template:section-2)**
 * Define the data request to CDS.

**[](template:section-3)**

This section presents several results for the XCO$_2$ products, i.e.:
 * The monthtly mean data coverage of XCO$_2$ over the period 2002-2022.
 * The mean data coverage of XCO$_2$ over specific time periods characterised by the use of observations from different satellites.
 * The calculation of the XCO$_2$ growth rates and annual seasonal cycles by using an approach similar to that adopted by [[6]](https://doi.org/10.5194/bg-17-6115-2020). Specifically, we computed monthly resolved annual growth rates by subtracting the XCO$_2$ value 6 months in the future from the one 6 months in the past. Then these monthly resolved growth rates are averaged to a yearly GR for a calendar year. We calculated the annual seasonal cycled of global XCO$_2$ by detrending the monthly time series with the cumulative sum of monthly growth rates, using the annual mean growth rates as substitution for missing values where necessary. Finally, we normalised the time series of detrended monthly values by subtracting the average seasonal cycle over the whole period 2003 - 2023. Spatial averages are calculated by taking the arithmetic averages over all grid cells weighted by their area.
 * Similarly to [[6]](https://doi.org/10.5194/bg-17-6115-2020), we calculated of the annual seasonal cycle amplitudes (SCAs) as the peak-to-trough amplitude in a calendar year of the detrended time series. The SCA was calculated for a shorter time period (2004 – 2022) with respect to the full available dataset (2003 - 2023), to avoid artefacts related to the fact that monthly growth rates were only available for six months in 2003 and 2023.


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 1. Choose the data to use and set-up the code > Import all relevant packages
---
In this section, we import all the relevant packages needed for running the notebook and we define the style for plot appearance ("seaborn-v0_8-notebook") as well as the style of hatches used in the maps.


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 1. Choose the data to use and set-up the code > Define the variable of interest
---
In this section, we define the parameters to be ingested by the code (that can be customized by the user), i.e.:
* the variable of interest.

Choose variable (xch4 or xco2)


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 1. Choose the data to use and set-up the code > Cache needed functions
---
In this section, we cached a list of functions used in the analyses.

* The `convert_units` function rescales XCO$_2$ mole fraction to parts per million (ppm).

* The `compute_coverage` function calculates the spatial fractional coverage of the available data and creates associated maps.

* The `def area_weighted_series` function calculates the spatial averages over all grid cells in a region. It uses spatial weighting to account for the latitudinal dependence of the grid size in the lon/lat grids.

* The `compute_GR_and_seasonal` funtion calculates the monthly resolved annual growth rates of XCO$_2$ and the annual seasonal cycles.

numerator: sum over lat,lon of x * weight (ignore NaNs in x)
avoid division by zero
convert to xarray DataArray with time coords
compute GR: GR(m) = x(t+6) - x(t-6)
annual mean GR (used to substitute missing monthly GR)
fill monthly GR missing with annual mean where possible
monthly increment (ppm/month) from yearly GR
cumulative trend (ppm)
seasonal cycle: xco2 - cumulative trend ; center to zero mean

(template:section-2)=


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 2. Retrieve data > 2.1 Obs4MIPs
---
In this section, we define the data request to CDS (data product Obs4MIPs, Level 3, version 4.5, XCO$_2$) and download the full dataset.

(template:section-3)=


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 3. Data analysis > Global maps of monthly XCO$_2$ data coverage
---
In this section, we calculate and plot the mean monthly fractional coverage of data over the period 2003 - 2023. In agreement with the results by [[6]](https://doi.org/10.5194/bg-17-6115-2020), the number of available observations depends significantly on the location with low data coverage values over locations typically affected by high cloud coverage (like tropics) and low Sun elevation (like high latitude in winter months). Coverage over ocean is sparse as ocean retrievals are included from GOSAT, OCO-2 and GOSAT2 Sun-glint mode observations. [[6]](https://doi.org/10.5194/bg-17-6115-2020) demonstrated that taking into account this sampling coverage feature is essential for a proper comparison with outputs from ESMs.

```text
100%|██████████| 1/1 [00:00<00:00,  3.36it/s]
```

*The figure shows the monthly fractional XCO$_2$ data coverage from 2003 to 2023 derived from the XCO2_OBS4MIPS dataset (version 4.6).*


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 3. Data analysis > Global maps of XCO$_2$ data coverage for different periods
---
In this section, to assess the temporal consistency of spatial data coverage over the whole period covered by the Level-3 Obs4MIPs merged dataset, we calculate and plot the mean fractional coverage of CO$_2$ data for time periods characterised by the contribution of different satellite observations: i.e., 2003 - 2008 (only SCIAMACHY observations), 2009 - 2011 (combination of SCIAMACHY and GOSAT), 2012 - 2014 (GOSAT), 2015 - 2018 (combination of GOSAT and OCO-2), 2019 - 2023 (combination of GOSAT, OCO-2 and GOSAT2). A comprehensive analysis of the number of soundings contained within the EMMA database on a monthly basis, along with the comparative contribution of each individual Level 2 algorithm to the EMMA database, can be found in [[8]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160577).

Besides not including data over oceans, with respect to subsequent periods in which GOSAT measurements were used (2009 - 2011 and 2012 - 2014), for the period 2003 - 2008 more data are available for regions higher than 50°N as well as over tropical regions in South America and Africa. The introduction of OCO-2 and GOSAT2 observations (2015 – 2018 and 2019 – 2023, respectively) resulted in an increase of the data coverage over Europe and the oceans. A substantial proportion of these regions exhibited data coverage levels exceeding 0.5, with the data extending to higher latitudes.

To define temporal coverage
To categorise data coverage as a function of time periods
add custom labels
Add hatched pattern where coverage > 0.5
convert to 2D array (lat, lon)
mask values <=0.5
Adjust layout and move colorbar below all maps

*The figure shows the monthly fractional XCO$_2$ data coverage for different time periods characterised by the use of different satellites/sensors to obtain obervations. The hatched areas denote regions with data coverage higher than 0.5*


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 3. Data analysis > Calculation of growth rate and seasonal cycles
---
In this section, we calculate and report the time series of monthly global CO₂ values, the resolved annual growth rate, and the seasonal cycles.
Over the period 2003–2014, our average growth rate of 2.02 ppm/year (with a standard deviation of 0.49 ppm/year) is in very good agreement with the values provided in [[4]](https://doi.org/10.5194/acp-18-17355-2018). The calculated seasonal cycles also completely agree with those calculated in [[6]](https://doi.org/10.5194/bg-17-6115-2020) (1.9 ± 0.4 ppm/year), which report a clear change in shape and amplitude when GOSAT observations began contributing to the calculation of global CO₂ in 2009. The low growth rate observed in 2009 is due to the introduction of GOSAT data, which altered the shape of the seasonal cycle. When considering only land-based data (orange lines), the low growth rate in 2009 disappears, and the seasonal cycles appear more consistent throughout the measurement period with an higher amplitude due to the higher carbon fluxes occurring over land regions than over oceans. This clearly shows that any changes to the sampling features that occur during the period covered by the dataset must be taken into account, particularly when evaluating uniform, gap-free datasets such as those provided by ESMs.
The high growth rate values observed in 2010, 2012, 2016, 2019 and 2023 are consistent with those reported by the in situ global network coordinated by [[1]](https://library.wmo.int/idurl/4/69057), and are related to El Niño conditions which favour net carbon emissions into the atmosphere on a global scale (see [[9]](https://doi.org/10.5194/acp-25-13053-2025) for a focus on the large growth rate on 2023).

-------------------------------------------------------
Load dataset and basic preprocess
-------------------------------------------------------
ds = download.download_and_transform(*request)
Replace missing value 1.0e20 with NaN
Convert xco2 to ppm
-------------------------------------------------------
Prepare area weights (cos(lat)) as 2D DataArray (lat, lon)
-------------------------------------------------------
-------------------------------------------------------
land mask: keep only cells with land_fraction > 0.5
-------------------------------------------------------
weights masked
-------------------------------------------------------
Compute series for both cases: all gridcells and masked (land_fraction>0.5)
-------------------------------------------------------
area-weighted global series
compute monthly GR and seasonal cycle for both
-------------------------------------------------------
Quick diagnostics (optional prints)
-------------------------------------------------------
print("Global (all) series length:", len(global_all_series.dropna()))
print("Global (masked) series length:", len(global_masked_series.dropna()))
print("Fraction positive GR (all):", (monthly_GR_all.dropna() > 0).mean())
print("Fraction positive GR (masked):", (monthly_GR_masked.dropna() > 0).mean())
-------------------------------------------------------
Plotting: three vertical plots with both lines
-------------------------------------------------------
(1) Global monthly mean XCO2
(2) Monthly Growth Rate (ppm/yr)
add stats box for both series (means and stds)
(3) Seasonal Cycle (detrended)
center y-axis of seasonal cycle around zero (symmetric limits) using both series combined
-------------------------
Format x-axis to show integer years
-------------------------

*The figure shows the global time series of monthly XCO$_2$ mean values (upper panel), monthly resolved annual growth rate (middle panel) and detrended seasonal cycles (bottom panel) from 2003 to 2023. Blue lines indicate results for the whole dataset, orange lines indicate results for land-masked data.*


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > Analysis and results > 3. Data analysis > Calculation of the Seasonal Cycle Amplitude (SCA)
---
In this section, we show the time series of the annual global SCA calculated for the XCO$_2$ variables for the whole dataset (blue) as well as for the land data (orange). Overall, the global SCA shows a decreasing trend over the period 2004 - 2022, however it should be pointed out that discontinuities exist at the time when new satellite observations were added to retrieve XCO$_2$: especially GOSAT in 2009 and OCO-2 in 2015. When the land data only are considered, the negative tendency diappeared from 2008 onward in agreement with the ESM simulations reported by [[6]](https://doi.org/10.5194/bg-17-6115-2020).

---------------------------------------------------------------------
Compute the SCA (peak-to-trough amplitude in each year, min 7 months of data)
---------------------------------------------------------------------
------------------------------------------------------------------------------
------------------------------------------------------------------------------
(1) SCA plot

*The figure shows the annual time series of SCA from 2004 to 2022 for the whole dataset (blue) and for land-masked data (orange).*


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > ℹ If you want to know more > Key resources
---
The CDS catalogue entries for the data used were:
* Carbon dioxide data from 2002 to present derived from satellite observations: https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview

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/)

Users interested in accessing official C3S products about long term trends of greenhouse gases from satellite observations are encouraged to access specifically designed Copernicus resources like the [European State of Climate](https://climate.copernicus.eu/esotc/2024/trends-climate-indicators) or the [Global Climate Highlights](https://climate.copernicus.eu/global-climate-highlights-2024).

Users interested in the investigation of CO$_2$ fluxes can consider to use specifically designed Copernicus resources like [CAMS global inversion-optimised greenhouse gas fluxes and concentrations](https://ads.atmosphere.copernicus.eu/datasets/cams-global-greenhouse-gas-inversion?tab=overview).


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > ℹ If you want to know more > References
---
[[1]](https://library.wmo.int/idurl/4/69057) World Meteorological Organization. (2024). WMO Greenhouse Gas Bulletin, 20, ISSN 2078-0796.

[[2]](https://doi.org/10.5194/gmd-9-1937-2016) Eyring, V., Bony, S., Meehl, G. A., Senior, C. A., Stevens, B., Stouffer, R. J., and Taylor, K. E. (2016). Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization, Geoscience Model Development, 9, 1937–1958.

[[3]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) Copernicus Climate Change Service, Climate Data Store. (2018). Carbon dioxide data from 2002 to present derived from satellite observations. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (Accessed on 30-Oct-2025).

[[4]](https://doi.org/10.5194/acp-18-17355-2018) Buchwitz, M., Reuter, M., Schneising, O., Noël, S., Gier, B., Bovensmann, H., Burrows, J. P., Boesch, H., Anand, J., Parker, R. J., Somkuti, P., Detmers, R. G., Hasekamp, O. P., Aben, I., Butz, A., Kuze, A., Suto, H., Yoshida, Y., Crisp, D., and O'Dell, C. (2018). Computation and analysis of atmospheric carbon dioxide annual mean growth rates from satellite observations during 2003–2016, Atmospheric Chemistry and Physics, 18, 17355–17370.

[[5]](https://doi.org/10.5194/amt-13-789-2020) Reuter, M., Buchwitz, M., Schneising, O., Noël, S., Bovensmann, H., Burrows, J. P., Boesch, H., Di Noia, A., Anand, J., Parker, R. J., Somkuti, P., Wu, L., Hasekamp, O. P., Aben, I., Kuze, A., Suto, H., Shiomi, K., Yoshida, Y., Morino, I., Crisp, D., O'Dell, C. W., Notholt, J., Petri, C., Warneke, T., Velazco, V. A., Deutscher, N. M., Griffith, D. W. T., Kivi, R., Pollard, D. F., Hase, F., Sussmann, R., Té, Y. V., Strong, K., Roche, S., Sha, M. K., De Mazière, M., Feist, D. G., Iraci, L. T., Roehl, C. M., Retscher, C., and Schepers, D. (2020). Ensemble-based satellite-derived carbon dioxide and methane column-averaged dry-air mole fraction data sets (2003–2018) for carbon and climate applications, Atmospheric Measurement Techniques, 13, 789–819.

[[6]](https://doi.org/10.5194/bg-17-6115-2020) Gier, B. K., Buchwitz, M., Reuter, M., Cox, P. M., Friedlingstein, P., and Eyring, V. (2020). Spatially resolved evaluation of Earth system models with satellite column-averaged CO2, Biogeosciences, 17, 6115–6144.

[[7]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160605) Reuter, M., Fuentes Andrade, B., Buchwitz, M. (2025). C3S Greenhouse Gas (GHG:CO2 & CH4) v4.6: Product User Guide and Specification (PUGS), C3S2_313a_DLR_WP1-DDP-GHG-v1_PUGS_XGHG_v4.6.


EQC Quality Assessment: "Consistency of carbon dioxide satellite observations for the evaluation of Earth system models."
Dataset: satellite-carbon-dioxide [CDS]
Aspect: consistency_q04  |  Category: Satellite_ECVs
Section: Consistency of carbon dioxide satellite observations for the evaluation of Earth system models. > ℹ If you want to know more > References
---
& CH4) v4.6: Product User Guide and Specification (PUGS), C3S2_313a_DLR_WP1-DDP-GHG-v1_PUGS_XGHG_v4.6.

[[8]](https://confluence.ecmwf.int/pages/viewpage.action?pageId=567160577) Reuter, M., Fuentes Andrade, B., Buchwitz, M. (2025). C3S Greenhouse Gas (GHG: CO2 & CH4) v4.6: Algorithm Theoretical Basis Document (ATBD), C3S2_313a_DLR_WP1-DDP-GHG-v1_ATBD_XGHG_v4.6.

[[9]](https://doi.org/10.5194/acp-25-13053-2025) Feng, L., Palmer, P. I., Smallman, L., Xiao, J., Cristofanelli, P., Hermansen, O., Lee, J., Labuschagne, C., Montaguti, S., Noe, S. M., Platt, S. M., Ren, X., Steinbacher, M., and Xueref-Remy, I. (2025). The role of the tropical carbon balance in determining the large atmospheric CO2 growth rate in 2023, Atmospheric Chemistry and Physics, 25, 13053–13076.


EQC Quality Assessment: "Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes"
Dataset: satellite-carbon-dioxide [CDS]
Aspect: resolution_q03  |  Category: Satellite_ECVs
Section: Spatial resolution and completeness of carbon dioxide satellite observations for quantifying carbon fluxes
---
Production date: 09-04-2026

Produced by: Paolo Cristofanelli (CNR)

Please note that this assessment also refers to the deprecated Level 2 XCO$_2$ data products which won't be extended after 2022.

## 🌍 Use case: Using satellite observations for investigating the global carbon fluxes

## ❓ Quality assessment question
* **Are the CO$_2$ satellite observations suitable for deriving global and regional carbon fluxes in terms of resolution and spatial completeness?**

Carbon dioxide (CO$_2$) is the most important anthropogenic greenhouse gas, accounting for almost 64% of the total radiative forcing by long-lived greenhouse gases [[1]](https://library.wmo.int/viewer/68532/?offset=#page=4&viewer=picture&o=bookmarks&n=0&q=). The atmospheric concentration of CO$_2$ in 2023 was 419.3$\,\pm\,$0.1 ppm [[2]](https://essd.copernicus.org/articles/17/965/2025/).

Accurate spatial and temporal quantification of carbon dioxide (CO$_2$) is central to understanding the carbon cycle and supporting policy decisions on climate change mitigation. Satellite retrievals (Level 2 data products) of column-averaged dry air mole fractions of CO$_2$ (XCO$_2$) have been widely used to infer regional and global variations in carbon fluxes through atmospheric inversion modelling (e.g., [[3]](https://doi.org/10.5194/acp-14-13739-2014), [[4]](https://doi.org/10.1175/BAMS-D-15-00310.1), [[5]](https://acp.copernicus.org/articles/19/14233/2019/), [[6]](https://doi.org/10.5194/acp-19-12067-2019), [[7]](https://essd.copernicus.org/articles/16/2857/2024/), [[8]](https://climate.copernicus.eu/esotc/2023/greenhouse-gas-fluxes)).

Atmospheric inverse modelling [[9]](https://doi.org/10.5194/bg-10-6699-2013) links CO$_2$ fluxes to observed atmospheric CO$_2$ mole fractions (like those provided by satellite measurements) using atmospheric transport (and chemistry) models, and is often referred to as "top-down" approach. Typically "top-down" inverse systems estimate residual natural or fluxes not related to fossil fuel consumption and cement production (FFC) from land and ocean regions (e.g., [[10]](https://acp.copernicus.org/articles/22/9215/2022/) and [[7]](https://essd.copernicus.org/articles/16/2857/2024/)).

Atmospheric inversion systems estimate carbon fluxes by adjusting modelled CO$_2$ fluxes to match atmospheric observations. The so-called "a‑priori" flux fields provide initial modelled CO$_2$ fluxes by combining, e.g., estimates of anthropogenic emissions from statistical inventories, terrestrial biosphere and oceanic fluxes from models or climatologies and emissions from vegetation fires. An example of a global-scale inversion system and adopted "a-priori" fields is represented by the CAMS "PYVAR" inversion system [[11]](https://confluence.ecmwf.int/display/CKB/Description+of+the+CO2+inversion+production+chain).

Due to their large spatial coverage, satellite observations are used for constraining atmospheric inversions by complementing independent datasets provided by high-precision in-situ measurements like those provided by [[12]](https://doi.org/10.15138/9N0H-ZH07).

In this assessment, through a review of existing literature and use cases combined with specific data analyses, we investigate if the spatial resolution and coverage of the dataset "Carbon dioxide data from 2002 to present derived from satellite observations" provided by [[13]](https://cds.climate.copernicus.eu/datasets/satellite-carbon-dioxide?tab=overview) are suitable to constrain global and regional Earth's carbon fluxes in "top-down" inversion systems.

![image.png](attachment:196b9642-4db0-4282-bb42-20bffbd84814.png)