The Copernicus Climate Change Service (C3S) is one of the six thematic services of the EU-funded Earth Observation programme Copernicus, managed by the European Commission (EU). C3S is implemented by the European Centre for Medium-Range Weather Forecasts (ECMWF), and it has as primary objective providing access to authoritative climate information in support of climate adaptation and mitigation policies of the EU.
C3S supports the implementation needs of the Global Climate Observing System (GCOS) and in turn, the objectives of the United Nations Framework Convention on Climate Change (UNFCCC), by assuring timely access to a large number of quality assured Climate Data Records (CDRs) of Essential Climate Variables (ECVs) derived from space-based Earth observations. In total, GCOS specifies a total of 54 ECVs which are critical to characterize the climate system (relevant), measured globally with existing technologies (feasible) and at an affordable level of investment (cost-effective). C3S has already implemented services for 22 of these land, ocean and atmosphere ECVs. Target requirements for most of the CDRs of ECV products, in terms of uncertainty, stability, temporal and spatial resolution, are based on the framework defined in the GCOS 2016 Implementation Plan (GCOS-IP 2016). However, the ECV services implemented in C3S have proven that target requirements are not always attainable with the current technology, what in turn also provides a valuable feedback for the upcoming update of the 2022 GCOS-IP.
C3S has recently finalized the successful implementation of the first phase of ECV services with access to data and associated information products through the C3S Climate Data Store (CDS). The CDS offers open and free access to an evolving catalogue of climate data products about data of the past, present and future, as well as tools to enable their use. The CDS counts already more than 100,000 registered users. It currently offers access to ECV products associated with 22 ECVs. Each data product provides state-of-the-art reliable access to quality-assured and regularly updated CDRs and Interim CDRs with global or near-global coverage, as well as comprehensive supportive documentation. In addition, an independent evaluation of the data products and associated services assures their quality and roadmap towards target requirements. Also, the implementation of a series of online applications or data viewers provides simple examples of the use of the data accessible through the CDS for climate purposes.
In this presentation we will show the status of the C3S ECV services in the first year of Copernicus 2 (COP 2), we will provide an overview of their main individual components and the plans of these services for the whole COP 2 period, between 2021-2027.

Contribution to the operational monitoring of the climate and the detection of global climatic change is a major objective of EUMETSAT. The growing relevance of this objective can be sensed by the fact that our societies are now actively managing weather and climate risks as a core task. The first three risks identified in the 2020 World Economic Forum Risk Report are weather and climate-related. Extreme weather becoming more likely in a changing climate has been continuously identified as the biggest global risk in terms of likelihood and is in the top five in terms of impact. Failure to implement climate change mitigation actions and natural disasters are second and third-ranked risks respectively, both in terms of impact and likelihood.

Adaptation and mitigation strategies require a full combination of operational weather and climate information services built on solid scientific foundations, including observations, seasonal and decadal predictions, climate projections, climate analyses, and assessments of impacts. Mitigating climate change and adapting to the impacts of weather extremes and changing environmental conditions require detailed information on all relevant scales. Earth system observations from space are a key component of such strategies and provide valuable information for the benefit of society. EUMETSAT engages in providing satellite data, information, and services dedicated to a better understanding of climate variability and change including the characterisation of extreme events as well as impacts of these changes on society.

EUMETSAT and its network of Satellite Application Facilities have released close to 100 climate data records since starting sustained climate monitoring activities in 2008. Most Climate Data Records were generated by reprocessing of the original sensor data, covering long time-series (~40 years) and several generations of instruments followed by the creation of GCOS Essential Climate Variables from the sensor data. According to the ECV Inventory of the joint CEOS-CGMS Working Group on Climate, the data records produced by EUMETSAT providing inputs to 19 out of 37 ECVs rated observable from space. Data from EUMETSAT are used in many national climate services and the European Copernicus Climate Change Service that EUMETSAT specifically supports with inputs for global and regional reanalysis and the ECV data sets generated by its Satellite Application Facility Network. The usage of the data for climate change analysis is documented in the recent IPCC report on the physical basis as well.

This paper presents an overview of the EUMETSAT’s strategy for the future development of its support to climate services addressing the usage of the evolving space segment, the development of new products from rescued past data, and research to operations and operations to research exchanges involving new cloud infrastructures.

The major challenge for climate data is the integration of benefits of legacy and new generation systems. This involves EUMETSAT systems and third party system of the past, e.g., NOAA instruments on Metop satellites. With the launch of MTG and EPS-SG more data records at instrument level will be generated and maintained. For instruments with a heritage it means addition to the past and improvement of the past data. In addition, it means integration with similar instruments in different orbits, e.g., EPS-SG MetImage with NOAA VIRS and past AVHRR. For new sensors, such as MTG IRS it means new data records. The new instruments hold a strong capability for new products, e.g., MWI and ICI hold potential for precipitation climatology but need to be used together with NASA GPM, geostationary data and the heritage of microwave radiometers to build a climatology. This is a huge task but Europe still lacks a global precipitation climatology using this data. In addition, information on aerosols and clouds will be improved using the 3MI instrument on EPS-SG, but how to utilise this for climate monitoring needs to be studied.

Sentinel satellites operated by EUMETSAT on behalf of the EU have very high relevance for climate change monitoring as well. In particular, Sentinel-6 Michael Freilich altimeter sea-level estimates and the planned CO2M mission with CO2 and CH4 estimates are highly relevant and are included in EUMETSAT’s framework in support of climate services. In addition, the development of an understanding on how new capabilities such as the planned microwave sounding constellation and Doppler Wind LIDAR can be utilised for climate monitoring. Similarly, it is important to continue to take into account specific climate requirements in the planning and definition of future EUMETSAT mandatory programmes such as M4G and EPS-TG.

Coordination and cooperation with other space agencies is a must, e.g., the utilisation of past, current, and future geostationary observations for climate monitoring is a challenge as up to 50 geostationary satellite missions operated by many agencies are part of the record with a variety of instrumentation since the late 1970s. Data from the geostationary ‘ring’ are essential for the provision of many global GCOS ECV products that are used in climate service applications and climate change research. EUMETSAT is engaged in data rescue, uncertainty characterisation, recalibration, harmonisation and reprocessing of such data and aims at the provision of the geostationary ‘ring’ data to users in the near future on its joint EUMETSAT-ECMWF cloud infrastructure the so called European Weather Cloud and the EUMETSAT Data Store.

Further improvements of the EUMETSAT climate data portfolio aim at the inclusion of new products developed by European research. EUMETSAT will help bridge the gap between research and operations by importing scientific methods and exporting the infrastructure for processing, operational data management and the distribution mechanism using the European Weather Cloud. The cloud infrastructure also offers new opportunities to tailor data products to uses to deliver records of information complementing the data records. In addition, investments will be made to improve the EUMETSAT Data Store and to strengthen the user engagement by stimulation measures, e.g., via the EUMETSAT user training programme.

Vector-borne diseases (VBD) account for about 17% of all lost life, illness, and disability globally. These diseases are gradually expanding their spatial range emerging in regions where they were typically absent, and re-emerging in areas where they had subsided for decades. They apply pressures that stretch scarce resources to breaking point. Climate change is expected to make these diseases more widespread, frequent and unpredictable. Dengue is the fastest spreading mosquito-borne viral disease in the world. About half of the global population lives at risk of the disease. Dengue is disproportionately linked to poverty and inequality in many low and middle-income countries.A fundamental gap is that current dengue control strategies are essentially reactive, meaning that they take place after cases occurred. These reactive responses reduce the probability to control and mitigate outbreaks. Disease models driven by Earth observations and seasonal climate forecasts could provide useful information of future disease risk to allow early action. We developed a superensemble of probabilistic models to predict dengue incidence across Vietnam up to 6 months ahead at a sub-national scale. The modelling framework was codesigned with stakeholders from the World Health Organization, the United Nations Development Programme, the Vietnamese Ministry of Health, the Pasteur Institute Ho Chi Minh City, the Pasteur Institute Nha Trang, the Institute of Hygiene and Epidemiology Tay Nguyen, and the National Institute of Hygiene and Epidemiology. The model superensemble generated accurate predictions evaluated using proper scores. The skill of the model varied with geographic location, forecast horizon, and time of the year, performing at its best during the peak season and in areas with high levels of transmission. Evaluated using a theoretical cost-loss analysis, the superensemble had considerable value in most provinces relative to not using the forecasting system. The system is being prospectively evaluated and the framework is being extended to other areas of the world.

In situ observations of 2m air temperature (T2m) are widely used in climate science and services. Land surface temperature (LST) is strongly related to T2m, and can be observed using InfraRed (IR) or MicroWave (MW) satellite observations, with several satellite LST datasets now freely available to users. Long-term satellite LST data are desirable for climate science and services because they can provide temporal and spatial information on surface temperatures that cannot be achieved using in situ T2m. In particular, these data can offer global coverage, enabling better understanding of regional climate change and impacts in areas with sparse in-situ data. Additionally, satellite LST data provides an independent measure of surface temperature change and for some applications, additional information not available through T2m observations. For example, over dense vegetation, LST represents the canopy temperature and could therefore be used more directly than T2m to indicate vegetative water status. Over vegetation-free regimes, LST represents the temperature of the land surface, and could be used to monitor road surface temperatures more accurately than T2m. However, studies of temperature change require the satellite LST data to be free from non-climatic discontinuities, which is not always the case, with previous studies showing variable agreement between LST and T2m time series. The European Space Agency’s Climate Change Initiative for LST (LST_cci) aims to deliver a significant improvement to the capability of current satellite LST data records to meet the Global Climate Observing System requirements for climate applications and realise the full potential of long-term LST data for climate science and services. The objective of this study is to assess the stability and trends in six new LST_cci datasets and demonstrate the value in using these LST data to augment T2m observations.
LST anomalies are compared with homogenized station T2m mean, minimum and maximum anomalies over Europe, which verifies that the LSTs in all six of these datasets are well coupled with T2m (LST vs T2m anomaly correlations and slopes range between ~0.6 and ~0.9). Having confirmed the relationship between the LST and T2m anomalies, the homogenized T2m data are used to assess the temporal stability of the LST_cci data through a comparison of the LST and T2m anomaly time series. Only the LST_cci datasets for the MODerate resolution Imaging Spectroradiometer (MODIS) on-board Aqua and the Advanced Along-Track Scanning Radiometer (AATSR) appear stable; the LST_cci MODIS/Terra, ATSR-2, and multisensor InfraRed and MicroWave datasets show non-climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002-2018), statistically significant trends in LST of 0.64-0.66 K/decade are obtained, which compare well with the equivalent T2m trends of 0.52-0.59 K/decade; there is no statistically significant difference between these trends in LST and T2m. The LST and T2m trends for AATSR (2002-2012) are found to be statistically insignificant, likely due to the comparatively short analysis period and specific years available for analysis.
In addition, this study demonstrates that the way in which LST and T2m data are compared can affect results. The IR satellite LST data are only available for cloud-free conditions and when cloudy T2m anomalies are excluded from the comparison, a ‘clear-sky bias’ is introduced. This results in an annual cycle and non-zero mean difference in the monthly mean LST-minus-T2m anomaly time series, which are both removed when the monthly T2m anomaly time series is regenerated from ‘all-sky’ T2m observations. Nevertheless, when the trends in T2m are recalculated from the ‘all-sky’ T2m data, they are still statistically significant and almost identical (0.01-0.06 K/decade difference) for the MODIS/Aqua time period. Therefore, it is concluded that the results presented in this study do not offer any evidence that a clear-sky bias affects trends calculated using cloud-free IR observations. The importance of having long and homogeneous time series for climate applications, including LST, is also highlighted in this study through a comparison of trends for T2m for the MODIS/Aqua and MODIS/Terra time periods. Here, the addition of two additional years of data at the beginning of the MODIS/Terra period (2000-2001) is found to reduce the trends in T2m by ~0.2 K/decade.
This study suggests that satellite LST data have great potential to be used in climate science and services. In particular, the analysis presented here demonstrates that satellite LSTs can be used to augment land T2m data where time series of surface temperatures are required, provided the required homogeneity is assured.

The Ocean Colour Climate Change Initiative (OC-CCI) project has spent a decade working to create an ever-improving climate data record (CDR) of the ocean colour Essential Climate Variable (ECV). The cyclical process of improvement, release and feedback has led to 6 versions of the dataset being created to-date with incorporation of data from additional sensors, incremental improvements at all stages of the processing chain, from top-of-atmosphere derived pixel flagging to inter-sensor bias correction and blended in-water algorithms. The OC-CCI dataset is the highest volume dataset of the ESA ECV catalogue, containing over 90 variables, spanning 23 years and now being provided for testing purposes at 1km resolution. The OC-CCI dataset has been used in over 180 publications including a number of prominent climate related studies such as the upcoming IPCC working group 2 report and publications in eminent journals such as Nature (Tang et al. 2021, Dutkiewicz et al. 2019). The data has been used to study the impacts of climate from the local scale, such as climate impacts on Sea Urchin Settlement (Okamoto et al. 2019), to the global scale, such as computation of oceanic primary production (Kulk et al. 2020). The data have also been used to investigate a variety of cross-disciplinary research areas such as the connections between ocean physics and biology (Balaguru et al. 2018), the synergy between ocean models and remote-sensing observations (Baird et al. 2020), and the links between ocean and human health (Campbell et al. 2020). This uptake of the data by the scientific community has been aided by the standardised formatting and multiple channels for access to the data (from bulk downloads to web-based interactive browsers). The processing chains that were developed in a research phase under OC-CCI are now used for operational data processing for services such as CMEMS and C3S. Here, we reflect on the lessons learned over the last decade of ECV development and consider the current maturity and future of the OC-CCI dataset in terms of 1) transforming research mode data into operational products, 2) data accessibility and interface with toolboxes, 3) the percolation of the data into decision-making spheres, and 4) the use of the data in cross-disciplinary science.


References:
Baird, M; Chai, F; Ciavatta, S; Dutkiewicz, S; Edwards, C; Evers-King, H; Friedrichs, M; Frolov, S; Gehlen, Ma; Henson, S; Hickman, A; Jahn, O; Jones, E; Kaufman, D; Mélin, F; Mouw, C; Muhling, B; Rousseaux, C; Shulman, I; Wiggert, J. Synergy between Ocean Colour and Biogeochemical/Ecosystem Models. IOCCG Report Number 19 (2020).

Balaguru K, Doney SC, Bianucci L, Rasch PJ, Leung LR, Yoon J-H, et al. (2018) Linking deep convection and phytoplankton blooms in the northern Labrador Sea in a changing climate. PLoS ONE 13(1): e0191509. https://doi.org/10.1371/journal.pone.0191509

Campbell AM, Racault MF, Goult S, Laurenson A. Cholera Risk: A Machine Learning Approach Applied to Essential Climate Variables. Int J Environ Res Public Health. 2020;17(24):9378. Published 2020 Dec 15. doi:10.3390/ijerph17249378

Dutkiewicz, S., Hickman, A.E., Jahn, O., Henson, S., Beaulieu, C. and Monier, E., 2019. Ocean colour signature of climate change. Nature communications, 10(1), p.578.

Tang, W., Llort, J., Weis, J. et al. Widespread phytoplankton blooms triggered by 2019–2020 Australian wildfires. Nature 597, 370–375 (2021). https://doi.org/10.1038/s41586-
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Kulk, Gemma & Platt, Trevor & Dingle, James & Jackson, Thomas & Jönsson, Bror & Bouman, Heather & Babin, Marcel & Brewin, Bob & Doblin, Martina & Estrada, Marta & Figueiras, F.G. & Furuya, Ken & González-Benítez, Natalia & Gudfinnsson, Hafsteinn & Gudmundsson, Kristinn & Huang, Bangqin & Isada, Tomonori & Kovač, Žarko & Lutz, Vivian & Sathyendranath, Shubha. (2020). Primary Production, an Index of Climate Change in the Ocean: Satellite-Based Estimates over Two Decades. Remote Sensing. 12. 826. https://doi.org/10.3390/rs12050826

Daniel K. Okamoto, Stephen Schroeter, Daniel C. Reed (2019) Effects of Ocean Climate on Spatiotemporal Variation in Sea Urchin Settlement and Recruitment, bioRxiv 387282; doi: https://doi.org/10.1101/387282

The seriousness of the climate emergency and the need for substantial and rapid action is increasingly widely recognised. However, many key actors are stuck, not knowing how to respond. The UCL Climate Action Unit works with communities of practice, communities of and place, institutions, policy makers, and the general public to assist them in identifying their agency to act. Space EO data can provide key information to guide such actions. Our theory of change addresses the behavioural, social, and political obstacles which get in the way. It integrates scientific insights from neuroscience, psychology and the social sciences into the delivery of real-world interventions. We engage with professionals and experts in public, private and civil society organisations to help them develop more effective responses to climate change. We have a growing portfolio of examples of improving the way climate risk information drives decision making in policy and business, equipping experts and communicators to tell better and more varied stories to better motivate action, and helping key communities and organisations to deliver climate-positive action. The key is that awareness and information alone do drive action ‘Actions Drive Beliefs”. The presentation will summarise the underlying principles, provide examples of recent successes and show the relevance to the exploitation of space EO data and services.