The ESA Climate Change Initiative: Satellite Data Records for Essential Climate Variables

TL;DR: The Climate Change Initiative (CCI) as discussed by the authors provides a forum to bring the data and modeling communities together to provide a climate system perspective and a forum for bringing data and modelling communities together.

Abstract: Observations of Earth from space have been made for over 40 years and have contributed to advances in many aspects of climate science. However, attempts to exploit this wealth of data are often hampered by a lack of homogeneity and continuity and by insufficient understanding of the products and their uncertainties. There is, therefore, a need to reassess and reprocess satellite datasets to maximize their usefulness for climate science. The European Space Agency has responded to this need by establishing the Climate Change Initiative (CCI). The CCI will create new climate data records for (currently) 13 essential climate variables (ECVs) and make these open and easily accessible to all. Each ECV project works closely with users to produce time series from the available satellite observations relevant to users' needs. A climate modeling users' group provides a climate system perspective and a forum to bring the data and modeling communities together. This paper presents the CCI program. It outlines its benefit and presents approaches and challenges for each ECV project, covering clouds, aerosols, ozone, greenhouse gases, sea surface temperature, ocean color, sea level, sea ice, land cover, fire, glaciers, soil moisture, and ice sheets. It also discusses how the CCI approach may contribute to defining and shaping future developments in Earth observation for climate science.

Summary

Summary

• S ustained observations from satellites contribute vital knowledge to their understanding of Earth’s climate and how it is changing—one of the major challenges of the twenty-first century.
• Satellites observe on a global scale, which makes them useful for both the monitoring and modeling of climate and hence for improving the prediction and attribution of climate change.
• A major challenge in climate research is to move beyond single variable estimates of climate change to analyze and close the budgets of the energy, water, and carbon cycles characterizing their climate system (e.g., Trenberth et al. 2013).
• The Global Climate Observing System (GCOS) has set out requirements for satellite data to meet the needs of climate science, designating key variables that are currently feasible for observation and important to the United Nations Framework Convention on Climate Change as “essential climate variables” (ECVs) (GCOS 2011).
• The specifications given by GCOS for ECV data products are designed to provide information to characterize the state of the global climate system and enable long-term climate monitoring.
• This often requires data at longer temporal scales (such as weekly or monthly), but.

Figures

Fig. 2. Latitude–time series of atmospheric (top) CO2 and (bottom) CH4 obtained from SCIAMACHY/ Envisat (Schneising et al. 2011). In (top), the rising CO2 levels, the CO2 seasonal cycle, and the hemispheric differences in CO2 resulting from greater biomass and anthropogenic emissions in the Northern Hemisphere are clearly visible. In (bottom), the seasonal cycle of methane is primarily due to wetland emissions. The reason for the recent methane increase is not yet well understood.

Fig. 4. An example assessment of stability of an SST dataset, adapted from Merchant et al. (2012). The figures shows a time series of monthly mean comparisons between nighttime satellite SSTs from ATSrs with matched SSTs measured in situ from tropical moored buoys. The early data are seen to be less stable, with a step change detected around 1993 (dashed lines). For 1995–2010, the 95% confidence interval for the stability of the satellite SST data are estimated to be –0.0018 to 0.0019 K yr–1, which represents an uncertainty that should be accounted for in interpreting any trends in the satellite SST dataset.

Table 1. Added value of eSA CCI climate data records for the marine eCVs being developed. The last column presents an overview of challenges investigated by the CCI projects.

Fig. 3. results from the aerosol CCI project comparing global gridded, 1° monthly baseline AOD datasets at the start of the project of four submitted algorithms using AATSr (red box) with their algorithm-comparison exercise datasets after the second evaluation (blue box). To evaluate the retrieval improvement, (middle) MODIS aerosol data have been chosen as reference in this figure.

Full text

The ESA’s Climate Change Initiative is reprocessing and reassessing over 40 years of
multi-sensor satellite records to generate consistent, traceable, long-term datasets of
“essential climate variables” for the climate modeling and research communities.
THE ESA CLIMATE CHANGE
INITIATIVE
Satellite Data Records for Essential Climate Variables
by R. Hollmann, C. J. meRCHant, R. SaundeRS, C. downy, m. buCHwitz, a. Cazenave,
e. CHuvieCo, P. defouRny, G. de leeuw, R. foRSbeRG, t. HolzeR-PoPP, f. Paul, S. Sandven,
S. SatHyendRanatH, m. van Roozendael, and w. waGneR
S
ustained observations from satellites contribute
vital knowledge to our understanding of Earth’s
climate and how it is changing—one of the major
challenges of the twenty-first century. Satellites ob-
serve on a global scale, which makes them useful for
both the monitoring and modeling of climate and
hence for improving the prediction and attribution of
climate change. A major challenge in climate research
is to move beyond single variable estimates of climate
change to analyze and close the budgets of the energy,
water, and carbon cycles characterizing our climate
system (e.g., Trenberth et al. 2013).
The Global Climate Observing System (GCOS)
has set out requirements for satellite data to meet the
needs of climate science, designating key variables
that are currently feasible for observation and impor-
tant to the United Nations Framework Convention
on Climate Change (UNFCCC) as “essential climate
variables” (ECVs) (GCOS 2011). The specifications
given by GCOS for ECV data products are designed
to provide information to characterize the state of
the global climate system and enable long-term cli-
mate monitoring. This often requires data at longer
temporal scales (such as weekly or monthly), but
AFFILIATIONS: Hollmann—Deutscher Wetterdienst (DWD),
Offenbach, Germany; meRCHant*—University of Edinburgh,
Edinburgh, United Kingdom; SaundeRS—Met Office, Exeter, United
Kingdom; downy—European Space Agency, Harwell, United
Kingdom; buCHwitz—University of Bremen, Bremen, Germany;
Cazenave—Centre National d’Etudes Spatiales, Toulouse,
France; CHuvieCo—University of Alcalá, Alcalá de Henares,
Spain; defouRny— Earth and Life Institute, Université Catholique
de Louvain, Louvain-la-Neuve, Belgium; de leeuw—Finnish
Meteorological Institute, and Department of Physics, University
of Helsinki, Helsinki, Finland; foRSbeRG—National Space Institute,
Technical University of Denmark, Lyngby, Denmark; HolzeR-
PoPP—German Aerospace Center (DLR), Wessling, Germany;
Paul—University of Zurich, Zurich, Switzerland; Sandven—Nansen
Environmental and Remote Sensing Center, Bergen, Norway;
SatHyendRanatH—Plymouth Marine Laboratory, Plymouth,
United Kingdom; van Roozendael—Aeronomy, Brussels, Belgium;
waGneR—Vienna University of Technology, Vienna, Austria
* CURRENT AFFILIATION: University of Reading, Reading, United
Kingdom
CORRESPONDING AUTHOR: Rainer Hollmann, Deutscher
Wetterdienst (DWD), Frankfurterstr. 135, 63067, Offenbach,
Germany
E-mail: rainer.hollmann@dwd.de
The abstract for this article can be found in this issue, following the table
of contents.
DOI:10.1175/BAMS-D-11-00254.1
In final form 12 December 2012
©2013 American Meteorological Society
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ECV data products are also used to validate model
processes, for which data on hourly time scales are
sometimes needed. Therefore, the ECV specifications
may in some cases be a compromise. Where different
instruments contribute observations toward an ECV,
their products must be made consistent. Stringent
requirements are also set by GCOS for quantifying
uncertainties in ECVs.
In response, the European Space Agency (ESA)
has launched the Climate Change Initiative (CCI) to
provide satellite-based climate data records (CDRs)
that meet the challenging requirements of the climate
community (GCOS 2011). The aim is to realize the
full potential of the long-term Earth observation (EO)
archives that both ESA and third parties have estab-
lished. This includes aspects of producing a CDR:
data acquisition, calibration, algorithm development,
validation, maintenance, and provision of the data to
the climate research community.
The CCI is consistent with several international
efforts targeting the generation of satellite derived cli-
mate data records. The Climate Data Record Program
from the National Oceanic and Atmospheric Admin-
istration (NOAA; www.ncdc.noaa.gov/cdr/) aims to
“develop and implement a robust, sustainable, and
scientifically defensible approach to producing and
preserving climate records from satellite data.” The
European Organisation for the Exploitation of Meteo-
rological Satellites (EUMETSAT) also aims to provide
certain climate data records in a sustained mode both
within its own operational facility and its Climate
Monitoring Satellite Application Facility (Schulz et al.
2009). Taking into account that the accuracy of the
derived datasets critically depends on the availability
of high quality satellite data, the Global Space-based
Inter-Calibration System (Goldberg 2007) is being
implemented to better characterize the intersatel-
lite biases of the “level 1” data (i.e., calibrated and
geolocated measurements of radiances, etc., prior
to inference of geophysical variables). CCI uses the
most recent and corrected level-1 datasets. High-
level coordination of several global CDR activities is
ensured through the World Meteorological Organiza-
tion (WMO) Sustained, Coordinated Processing of
Environmental Satellite Data for Climate Monitoring
(SCOPE-CM), GCOS, and the Committee on Earth
Observation Satellites (CEOS) working group on cli-
mate. The CCI program already works directly with
GCOS and the World Climate Research Programme
(WCRP) through their role in advising and assessing
the program. In response, the CCI projects contribute
through discussions, through directed feedback, or
via the new ECV datasets to these programs.
Previous CDR development efforts, such as the
satellite tropospheric temperature record (e.g.,
Mears and Wentz 2005, 2009; Christy et al. 2000),
led to the conclusion that it is crucial to have a
transparent, traceable, and sustainable process in
terms of scientific algorithm development and also
for the generation of the dataset itself. A thorough
analysis of requirements and proper quantification
of uncertainties are also important.
One focus of the CCI is to provide products for
climate modelers, who increasingly use satellite data
to initialize, constrain, and validate models on a wide
range of space and time scales (seasonal to centen-
nial). Better predictions require better models, which
in turn require reliable observations to evaluate them.
The growing use of satellite data is partly due to the
increasing resolution, complexity, and range of the
physical processes now represented in climate models.
Longer records provide opportunities to examine the
models over a greater range of situations (e.g., El Niño
and other interannual variations), improving the
assessment of their reliability.
The second, equally important, focus is a clear
need of stable, long-term, and consistent data
records for budget closure studies. For this, the
interdependence and feedback of variables are stud-
ied and estimates of the uncertainties of the budget
are obtained. For example, in a recent study (e.g.,
Loeb et al. 2009) the observed imbalance of energy
at the top of the atmosphere is linked to the net heat
content of the ocean. To understand such changes,
consistent measurements of related variables of
ocean and atmosphere are necessary (e.g., sea surface
temperature, sea ice, sea level, ice sheets, clouds)
along with estimates of their uncertainties. Similar
information on the change in mass of glaciers and
ice sheets can help constrain the effects on global
and regional sea level.
Third, the provision of uniformly processed
datasets for reanalysis of the coupled atmosphere (Dee
et al. 2011), ocean, and land surface is a major future
application of the CCI-generated CDRs. Some will
be directly assimilated whereas others will provide
boundary conditions for the reanalysis.
This paper outlines the objectives of the CCI and
the improvements to be made to satellite datasets.
The next section describes the CCI activities in more
detail, whereas the “New climate data records” section
summarizes the new climate data records for the
ECVs being addressed by the CCI and the “Provision
and exploitation of datasets” section describes the
plans for making the data available to the user and
their potential exploitation.
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OVERVIEW OF THE CCI. Out of the 50 ECVs
identified by GOCS, 13 have been selected for the
CCI (in the current program) where an important
contribution can be made to international climate
change science with the available data. A total of 10
projects started in 2010, and 3 projects (sea ice, soil
moisture, and ice sheets) began in early 2012:
atmosphere: ozone, clouds, aerosols, and green-
house gases (CO
2
, CH
4
);
ocean: sea level, sea surface temperature,
ocean color, and sea ice; and
terrestrial: land cover, fire disturbance, soil
moisture, glaciers and ice caps, and
ice sheets.
The sensors and technologies used for observa-
tion and the length of the record vary between
ECVs. Figure 1 shows, for all ECVs, which satellite
instruments will contribute to the climate data
records produced for each ECV. As presented in
Fig. 1, the CCI projects use and benefit from the
full capacity of globally available satellite-based
Earth observation data. Depending on the ECV,
the most appropriate satellite instruments are used
either for production of datasets or for intercom-
parison in the validation efforts. The CCI requires
some common approaches and activities to be
undertaken in parallel by
the teams; these common
elements are as follows.
An early task for
every CCI project was an
assessment of the require-
ments of climate scientists
and other users for EO-
based climate data records
for each ECV. This included
surveys of scientists, and
assessments of established
requirements from GCOS,
the scientific literature, and
statements by other expert
groups. In many cases, the
user requirement reports
for each ECV contributed
to the recent GCOS update
to the satellite supplement
on needs for climate obser-
vations (GCOS 2010). The
resulting user requirements
documents (URDs) are
publicly available through
the CCI website. Based on interaction with the sci-
entific stakeholders and taking into account existing
datasets, each project then defined the most fea-
sible products to address the needs of its core users.
Product specification documents (PSDs) describe the
CDRs to be generated.
A second aspect of every project is an open
process of algorithm intercomparison and selec-
tion to define the best available techniques for the
production of CDRs. Teams are involved in devel-
oping their own algorithms and in performing an
objective assessment of alternative approaches. In
some cases, it has been possible to engage parties
outside of the CCI in contributing datasets for
comparison. The related product validation and
algorithm selection reports (PVASRs) present the
results for each ECV.
Third, each project must specify the long-term
needs for a system to deliver EO-based climate data
and information for each of the 13 ECVs including
descriptions of data access, software, and hardware.
Teams prototype and demonstrate the generation
of CDRs that are as complete as possible within
a research context, for each ECV, to illustrate the
outputs of the systems specified. The intention is that
many of these prototype systems will be fully com-
missioned in the future, including joint development
across cognate groups of ECVs if appropriate.
Fig. 1. Primary satellite sensors contributing to each ECV in the CCI program.
Other sensors not listed here will also contribute indirectly or through
validation activities. A more complete table with all sensors used is available
online (at www.esa-cci.org).
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Fourth, the prototype products will be openly and
independently verified, validated, and assessed for
their utility to climate science, by independent teams
not involved in the product development.
All four elements are being pursued consistently
with principles and practices designed to maxi-
mize the benefit to climate science. Input data and
output products will be available, all algorithms
will be described in open literature and reports,
and data standards have been adopted to make
data readily usable by climate modelers and other
groups. This was mostly not the case for previ-
ous CDRs, which made it difficult for modelers
to use the datasets, but has been a key focus for
the CCI. All projects will promote the new CDRs
more actively to the climate research community
to ensure they are used as soon as they become
available. To facilitate these interactions, a “climate
modelling users group” (CMUG) was set up at the
same time as the projects. Part of the work for the
CMUG will be to help with dataset promotion and
to study the consistency between different ECV
CDRs, but their main aim is to ensure that teams’
activities take full account of the needs of climate
modelers and reanalyses. These can vary across
different modeling activities so each CCI project
has worked to develop attributes of their ECV data
product of most benefit to their users (as outlined
Table 1. Added value of ESA CCI climate data records for the marine ECVs being developed. The last
column presents an overview of challenges investigated by the CCI projects.
A: Marine
ECVs Precursor and its attributes
CCI CDR expected product
improvements and temporal coverage
Scientific challenges the
products will address
SST
Along-Track Scanning Radiometer
(ATSR) Reprocessing for Climate:
Independent, stable SST for
1991–2009, 0.1 K point accuracy,
relative sparse sampling from
narrow-swath ATSRs
• Cross-calibrate Advanced Very High Resolution
Radiometer (AVHRR) brightness temperatures
to ATSRs, giving AVHRR density of sampling with
ATSR-based accuracy, stability, and independence
• A 20-yr, independent, accurate (0.1 K),
high-stability CDR
• Characterization of uncertainty components at all
spatiotemporal scales
• User-friendly data formats and documentation
• Temporal coverage: 1991–2010
• Consistent, stable SSTs with
higher coverage
• Realistic uncertainties to
inform model comparisons and
assimilation
• Independence from in
situ records for rigorous
reassessment of recent marine
climate change
Ocean
color
GlobColour: Merged products
including chlorophyll, spectral
values of water-leaving radiance,
and inherent optical properties at
specific wavelengths
• Multiple merged products including chlorophyll,
spectral values of water-leaving radiance, and
spectrally resolved inherent optical properties
• Improved spatial coverage (e.g., at daily and
weekly scales)
• Retrieval of variable spectral shapes of
phytoplankton optical properties
• Temporal coverage: 1997–2012
• The improved ocean color CCI
product will support trend
studies in marine ecosystem
properties
• Removing spurious trends
(arising from intersensor
differences) of current merged
ocean color data
Sea
level
Archiving, Validation, and
Interpretation of Satellite
Oceanographic data (AVISO)
radar altimeter sea level record
from 1992–2012
• The global mean sea level derived from ESA
missions [European Remote Sensing Satellite-1
(ERS-1)/ERS-2, Envisat] has been significantly
improved and the uncertainty has decreased from
0.5–0.6 mm yr
–1
to <0.3 mm yr
–1
• Combines all available missions and covers high
latitude oceans and coastal zones
• Regional mean sea level trends have also been
improved
• Temporal coverage: 1992–2010
• Significant improvements to
meet climate standards
• Reduction in uncertainty of
the global mean trend below
0.3 mm yr
–1
• Different sources of sea level
variability distinguished (ocean
thermal expansion, ice sheets,
land water, glaciers)
Sea ice
EUMETSAT Satellite Application
Facility on Ocean and Sea
Ice (OSI SAF) arctic ice
concentration product
• Generation of the first homogeneous and
validated ice thickness dataset for the Arctic,
based on radar altimeter data
• Homogenous validated ice concentration datasets
for the Arctic and Antarctic from 1979 to 2012
with error estimates based on validation data
• Improve global climate model
simulations of sea ice
• Understand the interaction
between sea ice, ocean, and
atmosphere
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in Tables 1–3). For example, to evaluate predictions
of climate change, long-term stability of observa-
tions and its associated uncertainty are critically
important. In contrast, stability is less crucial for
model process studies, where spatial and temporal
resolution become more important.
Table 2. As in Table 1, but for the GCOS atmospheric ECVs.
B: Atmosphere
ECVs
Precursor and its
attributes
CCI CDR expected product
improvements and temporal coverage
Scientific challenges the
products will address
Clouds
International Satellite Cloud
Climatology Project (ISCCP):
Polar satellites, merged with
geostationary satellites;
two-channel cloud detection
approach
• Cloud CCI combines both European/ESA and
U.S. instruments
• An optimal estimation retrieval scheme will be
consistently applied to different instruments
to produce a long time series (with improved
temporal and spatial sampling, including
uncertainty estimates of the cloud properties)
• The products will have improved spectral
consistency using a five-channel approach.
• Level-1 calibration with advanced sensors
• Initial temporal coverage: 2007–09 with the
intention to go for 1982–2013
• The role of clouds in climate
is crucial and remains a main
uncertainty in climate science
• Measurements of cloud
properties with associated
uncertainty estimates will help
to identify different sources of
uncertainty
Aerosol
GlobAerosol: ATSR-2,
Advanced Along-Track
Scanning Radiometer (AATSR),
Medium Resolution Imaging
Spectrometer (MERIS), and
Spinning Enhanced Visible and
Infrared Imager (SEVIRI) merged
products: Aerosol optical depth
(AOD) 0.55 and 0.87 µm
ESA Grid Processing on Demand
(G-POD): Full 13-yr ATSR-2/
AATSR data series
• The products have improved accuracy, added
uncertainty characterization, and quality flags
• Using new instruments and new or improved
algorithms
• Additional retrieval parameters
• Initial temporal coverage: 2008, to be
extended to 1995–2012
• Understand and thereby reduce
differences between aerosol
products retrieved from different
sensors
• Quantify uncertainties to
establish tighter constraints in
aerosol–climate modeling
Green-
house
gases
(GHGs)
Initial Scanning Imaging
Absorption Spectrometer for
Atmospheric Cartography
(SCIAMACHY) (e.g., 2003–05
CO
2
) and first Greenhouse
Gases Observing Satellite (GOSAT)
(launch 2009).
• The CCI project will generate global time
series of column-averaged mixing ratios of
CO
2
and CH
4
from SCIAMACHY and GOSAT
• Improved quality (reduced biases) and better
error characterization
• Extended temporal coverage: 2003–10
• Climate prediction requires a good
understanding of the sources and
sink of the two major GHGs carbon
dioxide (CO
2
) and methane (CH
4
)
• Provide global atmospheric
distributions of CO
2
and CH
4
to
enhance our knowledge on their
regional sources and sinks
Ozone
Total ozone: e.g., Global
Ozone Monitoring Experiment
(GOME) Data Processor 5
(GOME GDP 5)
Nadir profiles: e.g., GOME2-
OPERA
Limb profiles: e.g., Envisat
QWGs
• For total ozone a harmonized GOME,
SCIAMACHY, and GOME2 data product with
1% accuracy is planned
• For nadir profiles, a harmonized time series
of GOME, SCIAMACHY, GOME2, and
Ozone Monitoring Instrument (OMI) will be
generated with improved information content
in the troposphere
• For limb profiles a merged Envisat +
Advanced Composition Explorer (ACE), Odin
Spectrometer and Infrared Imaging System
(OSIRIS), and Sub-Millimetre Radiometer
(SMR) dataset is planned, with major
improvements in the error analysis
• Temporal coverage: 1995–2011 (nadir sensors)
and 2001–11 (limb sensors)
• The evolution of ozone is inti
-
mately coupled to climate change
• The products are essential to help
assess the fate of atmospheric
ozone and better understand its
link with anthropogenic activities
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Frequently asked questions

Q1. What have the authors contributed in "The esa climate change initiative" ?

The specifications given by GCOS for ECV data products are designed to provide information to characterize the state of the global climate system and enable long-term climate monitoring. 

Q2. What is the purpose of the CCI?

A desirable feature of the CCI is the characterization of uncertainties for each variable, which are crucial to modelers for applications such as assimilation in reanalyses, assessing model processes, and interpreting long-term trends in parameters. 

Q3. What is the purpose of the CMUG?

The availability of observation simulators (Bodas-Salcedo et al. 2011) for CDRs will be a vital component to enable optimal use of these data in models as was demonstrated with ISCCP data (Williams and Webb 2009). 

Q4. What is the benefit of using satellite data for climate research?

Another benefit is consistency for the input radiances from the satellite instruments and auxiliary datasets [e.g., European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Re-Analysis (ERA-Interim) model fields] used for the generation of all CDRs. 

Q5. What is the purpose of the project?

Where appropriate, observation simulators will be generated for CDRs produced by the CCI to facilitate more direct comparisons of the observations with model1550 october 2013|the next phase, a common access portal for data and documentation has been proposed and issues of scientific consistency (consistent auxiliary datasets, consistent dynamical masks for clouds and ice, and documented cross-ECV sensitivities of corrections applied) are under investigation. 

Q6. What is the purpose of the study?

The CMUG will study the consistency between some CCI datasets by examining the various responses to anomalies (e.g., El Niño, Pinatubo) in each dataset and also through assimilation in reanalyses and in defining surface fields. 

Q7. What is the purpose of the paper?

In setting up a strong focus on uncertainty characterization and consistency, CCI will provide new opportunities to make progress in their understanding of the Earth system. 

Q8. What are the main features of the CCI datasets?

Some of the CCI datasets will be new products not previously available to the research community, while others will be improvements (e.g., higher quality and addition of uncertainties) and extensions to existing data records, as described in Table 1. 

Q9. What are the other CDRs with different applications?

Other CDRs with different applications (e.g., reference datasets: e.g., land cover, glaciers) are made available on public servers for download. 

Q10. What are the main constraints for the use of satellites for climate research?

The use of satellite datasets for climate research has been limited to date because of several constraints (e.g., length of datasets and inconsistencies), although notable exceptions include the top-of-atmosphere Earth radiation budget, sea surface height, and the ISCCP datasets.