Challenges and Future Outlook

Abstract. The World Meteorological Organization (WMO) established Regional Climate Centres (RCCs) around the world to create science-based climate information on a regional scale within the Global Framework for Climate Services (GFCS). The paper introduces the satellite component of the WMO Regional Climate Centre on Climate Monitoring (RCC-CM) for Europe and the Middle East. The RCC-CM product portfolio is based on essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS), spanning the atmospheric (radiation, clouds, water vapour) and terrestrial domains (snow cover, soil moisture). In the first part, the input data sets are briefly described, which are provided by the EUMETSAT (European Organisation for the Exploitation of Meteorological Satellites) Satellite Application Facilities (SAF), in particular CM SAF, and by the ESA (European Space Agency) Climate Change Initiative (CCI). In the second part, the derived RCC-CM products are presented, which are divided into two groups: (i) operational monitoring products (e.g. monthly means and anomalies) based on near-real-time environmental data records (EDRs) and (ii) climate information records (e.g. climatologies, time series, trend maps) based on long-term thematic climate data records (TCDRs) with adequate stability, accuracy and homogeneity. The products are provided as maps, statistical plots and gridded data, which are made available through the RCC-CM website (www.dwd.de/rcc-cm).

The Earth system is enduring multiple, interacting stressors causing immense and irreversible change to its biosphere. The unprecedented magnitude of human influence on the planet is a cause for much concern, but also an opportunity to mitigate undesirable impact by instituting socio-ecological management strategies and monitoring their effectiveness. To do so, local and global policy developers and decision makers must have access to clear and globally consistent information streams that serve as planetary diagnostics. This need is being addressed by several international, multi-agency networks which have developed sets of "Essential Variables" (EVs) to improve consistency in the observation of key phenomena within their domains of operation. For example, the implementation of the Essential Climate Variables (ECVs) led by the Global Climate Observing System (GCOS) has set an example being followed in the marine and biodiversity domains by, respectively, the Global Ocean Observing System's (GOOS) Essential Ocean Variables (EOVs) and the Group on Earth Observation Biodiversity Observation Network (GEO BON)'s Essential Biodiversity Variables (EBVs). Each of these EV collections has a subset dedicated to biological and ecological phenomena. This is greatly encouraging for biodiversity science at large, but also runs the risk of parallel and conflicting developments occuring in the EV communities addressing thecross-cutting theme of biodiversity. As a consequence, there is an urgent need for these systems, and the communities behind them, to pursue tight conceptual and technical interoperability in aid of a coherent treatment of biodiversity in planetary-scale monitoring. In this contribution, we will describe our current research and implementation of a semantic interoperability layer for the EVs, with a focus on biology and ecology. Applying the best practices and core infrastructure of the Open Biological and Biomedical Ontology (OBO) Foundry and Library, we have leveraged both mature and emerging reference ontologies targeting the domains of ecosystems and environment, population and communities, and high-level ecological phenomena. This interoperable collection of ontologies constitutes a foundation for highly expressive EV knowledge representation and management through robust, FAIR-compliant technologies. Further, we are coordinating these efforts with semantic technology adopted by UN agencies, representing global directives and indicators of the UN Sustainable Development Agenda for 2030. With open editorial models behind each of these ontologies, we aim to offer a scalable and inclusive system to bridge data and information products across the EVs through machine-accessible knowledge representation.

Global satellite observations of column-averaged carbon dioxide and methane: The GHG-CCI XCO2 and XCH4 CRDP3 data set

Global Navigation Satellite System (GNSS) is an established atmospheric observing system for atmospheric water vapour with high spatiotemporal resolution. Water vapour is under-sampled in the current meteorological and climate-observing systems, and thus obtaining and exploiting more high-quality humidity observations is essential.  The operational GNSS data processing at GFZ provides all tropospheric products, zenith total delays (ZTD), precipitable water vapour (PWV), slant total delays (STD) and tropospheric gradients in the framework of different meteorological projects like e.g. E-GVAP ("The EUMETNET EIG GNSS Water Vapour Programme", http://egvap.dmi). E-GVAP is in charge of the collection and quality control of operational GNSS tropospheric products for numerical weather prediction. GFZ is one of the E-GVAP Analysis Centres and processes about 600 GNSS stations in near real-time. The tropospheric products provided by GFZ are used by European weather services for operational forecasts.  GFZ contributes to climate research within the Global Climate Observing System (GCOS) Reference Upper Air Network (GRUAN). Established in 2006, GRUAN, is an international reference observing network of sites measuring essential climate variables above the Earth's surface. Currently, this network comprises 33 reference sites worldwide, designed to detect long-term trends of key climate variables such as temperature and humidity. GFZ hosts a central processing facility for the GNSS data and is responsible for the installation of GNSS hardware, data transfer, processing and archiving, as well as derivation of GNSS-PWV products according to the GRUAN requirements. A complementary small scale regional climate station network is the Austrian WegenerNet, which provides since 2007 measurements of hydrometeorological variables with very high spatial and temporal resolution. GNSS-PWV measurements have been included as a priority one measurement of the essential climate variable water vapour to both GRUAN and WegenerNet climate station networks.GNSS-derived tropospheric products and results of selected validation studies will be presented.

2 - Estimation of Soil Water Conditions Using Passive Microwave Remote Sensing

Extreme weather and climate events have over the years impacted human society and have currently become a more complex challenge with the changing climate. Many more climate change consequences have been recognized due to various environmental and social stresses in the last few decades. One way to assess the impacts of observed climate change is by viewing the Earth from space. Monitoring climate change from space is one of the most important and challenging responsibilities. Satellite remote sensing measurements can make the task of monitoring the entire Earth relatively easy with low costs; thus, environment and weather satellite measurements provide the ability to observe and predict the Earth’s environment and weather while improving the accuracy. Satellite observations make a significant contribution to the Essential Climate Variables (ECVs) and their associated data sets and products of the Global Climate Observing System (GCOS) which was established to ensure that all users have access to the climate observations, data records, and information which they require to address pressing climate-related concerns. Generating climate data records with multi-mission and multi-instrument measurements is difficult and complex because of the spectral spatial, temporal, and angular coverage issues. Cross-sensor calibration and validation play a pivotal role relative to understanding the climate and its trends—an important issue. The applications of satellite data for climate change are summarized based on the 20 chapters in this book, and recent advances of climate change monitoring from space are discussed. Monitoring climate change from space presents major challenges that will be briefly summarized. The era of collecting satellite images from space is rapidly being replaced by the era of “Satellite Climatology” as the time series of satellite measurements has reached over 30 years of extended applications and use.

Surface albedo has been documented as one of the Essential Climate Variables (ECV) of the Global Climate Observing System (GCOS) that governs the Earth's Radiation Budget. The availability of surface albedo data is necessary for a comprehensive environmental modelling study. Thus, both temporal and spatial scale issues need to be rectified. This study reports about the availability of surface albedo data through in-situ and remote sensing satellite observations. In this paper, we reviewed the existing models for surface albedo derivation and various initiatives taken by related environmental agencies in order to understand the issues of climate with respect to surface albedo. This investigation evaluated the major activities on albedo-related research specifically for the retrieval methods used to derive the albedo values. Two main existing albedo measurement methods are derived through in-situ measurement and remotely sensed observations. In-situ measurement supported with number of instruments and techniques such aspyrheliometers, pyranometers and Baseline Surface Radiation Network (BSRN) and remotely sensed observations using angularly integrated Bi-directional Reflectance Distribution Function (BRDF) by both geostationary and polar orbit satellites. The investigation results reveals that the temporal and spatial scaling is the major issues when the albedo values are needed for microclimatic study, i.e. high-resolution time-series analyses and at heterogeneity and impervious surface. Thus, an improved technique of albedo retrieval at better spatial and temporal scale is required to fulfil the need for such kind of studies. Amongst many others, there are two downscaling methods that have been identified to be used in resolving the spatial scaling biased issues: Smoothing Filter-based Intensity Modulation (SFIM) and Pixel Block Intensity Modulation (PBIM). The temporal issues can be resolved using the multiple regression techniques of land surface temperature, selected air quality parameters, aerosol and daily skylight.

The three main objectives of the Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) are to provide long-term high-quality climate records of vertical profiles of selected essential climate variables (ECVs), to constrain and calibrate data from more spatially comprehensive global networks, and to provide measurements for process studies that permit an in-depth understanding of the properties of the atmospheric column. In the five years since the first GRUAN implementation and coordination meeting and the printing of an article (Seidel et al.) in this publication, GRUAN has matured to become a functioning network that provides reference-quality observations to a community of users. This article describes the achievements within GRUAN over the past five years toward making reference-quality observations of upper-air ECVs. Milestones in the evolution of GRUAN are emphasized, including development of rigorous criteria for site certification and assessment, the formal certification of the first GRUAN sites, salient aspects of the GRUAN manual and guide to operations, public availability of GRUAN’s first data product, outcomes of a network expansion workshop, and key results of scientific studies designed to provide a sound scientific foundation for GRUAN operations. Two defining attributes of GRUAN are 1) that every measurement is accompanied by a traceable estimate of the measurement uncertainty and 2) that data quality and continuity are maximized because network changes are minimized and managed. This article summarizes how these imperatives are being achieved for existing and planned data products and provides an outlook for the future, including expected new data streams, network expansion, and critical needs for the ongoing success of GRUAN.

PROBA-V mission was conceived, and launched by ESA in 2013, to ensure continuity to the SPOT - VGT longterm data series of global daily land observations, started in 1998, bridging the gap to Sentinel-3. Nowadays, thanks to the successful and smooth operations of the PROBA- V satellite, a >20 years consistent data record of land surface reflectances, is available to the scientific community. This long-term dataset has clear relevance for trend analysis, anomaly detection and climate related applications. Despite this obvious interest, the exploitation of this dataset is still limited and mostly focused to near-real time applications, using the SPOT - VGT archive as a background average to detect anomalies, e.g., in NDVI time series. In order to fill this gap, and to exploit the full potential of this dataset, ESA has funded a new project called CISAR@MEP, whose primary goal is to set the stage for the generation of a global 20 years long-term data record of surface reflectance and aerosol properties from analysis of SPOT-VGT and PROBA-V observations. For this purpose, a recently developed algorithm, called CISAR and developed by Rayference, will be used, allowing the joint retrieval of aerosol and surface reflectance properties, together with the associated retrieval uncertainties. This new long-term data record will eventually fulfill current Global Climate Observing System (GCOS) requirements for the generation of Essential Climate Variables (ECVs), such as surface albedo and aerosol optical thickness, being 5% and 10%, respectively. Such data set can support the needs of both the atmospheric and land surface research communities and it can contribute to Copernicus operational service and ESA Climate Change Initiatives (CCI) activities. The CISAR@MEP project started during Q1 2019 with expected 2 years duration. The aim of this paper is to present this ESA funded project, to illustrate the objectives and the methodology and to provide some preliminary results. Furthermore, the technological implementation approach will be detailed, which makes full use of the PROBA-V Mission Exploitation Platform (MEP), a scalable processing environment, providing direct access to both SPOT-VGT and PROBA-V full mission archive together with the required resources and tools.

Ocean colour is recognised as an Essential Climate Variable (ECV) by the Global Climate Observing System (GCOS); and spectrally-resolved water-leaving radiances (or remote-sensing reflectances) in the visible domain, and chlorophyll-a concentration are identified as required ECV products. Time series of the products at the global scale and at high spatial resolution, derived from ocean-colour data, are key to studying the dynamics of phytoplankton at seasonal and inter-annual scales; their role in marine biogeochemistry; the global carbon cycle; the modulation of how phytoplankton distribute solar-induced heat in the upper layers of the ocean; and the response of the marine ecosystem to climate variability and change. However, generating a long time series of these products from ocean-colour data is not a trivial task: algorithms that are best suited for climate studies have to be selected from a number that are available for atmospheric correction of the satellite signal and for retrieval of chlorophyll-a concentration; since satellites have a finite life span, data from multiple sensors have to be merged to create a single time series, and any uncorrected inter-sensor biases could introduce artefacts in the series, e.g., different sensors monitor radiances at different wavebands such that producing a consistent time series of reflectances is not straightforward. Another requirement is that the products have to be validated against in situ observations. Furthermore, the uncertainties in the products have to be quantified, ideally on a pixel-by-pixel basis, to facilitate applications and interpretations that are consistent with the quality of the data. This paper outlines an approach that was adopted for generating an ocean-colour time series for climate studies, using data from the MERIS (MEdium spectral Resolution Imaging Spectrometer) sensor of the European Space Agency; the SeaWiFS (Sea-viewing Wide-Field-of-view Sensor) and MODIS-Aqua (Moderate-resolution Imaging Spectroradiometer-Aqua) sensors from the National Aeronautics and Space Administration (USA); and VIIRS (Visible and Infrared Imaging Radiometer Suite) from the National Oceanic and Atmospheric Administration (USA). The time series now covers the period from late 1997 to end of 2018. To ensure that the products meet, as well as possible, the requirements of the user community, marine-ecosystem modellers, and remote-sensing scientists were consulted at the outset on their immediate and longer-term requirements as well as on their expectations of ocean-colour data for use in climate research. Taking the user requirements into account, a series of objective criteria were established, against which available algorithms for processing ocean-colour data were evaluated and ranked. The algorithms that performed best with respect to the climate user requirements were selected to process data from the satellite sensors. Remote-sensing reflectance data from MODIS-Aqua, MERIS, and VIIRS were band-shifted to match the wavebands of SeaWiFS. Overlapping data were used to correct for mean biases between sensors at every pixel. The remote-sensing reflectance data derived from the sensors were merged, and the selected in-water algorithm was applied to the merged data to generate maps of chlorophyll concentration, inherent optical properties at SeaWiFS wavelengths, and the diffuse attenuation coefficient at 490 nm. The merged products were validated against in situ observations. The uncertainties established on the basis of comparisons with in situ data were combined with an optical classification of the remote-sensing reflectance data using a fuzzy-logic approach, and were used to generate uncertainties (root mean square difference and bias) for each product at each pixel.

Land surface albedo determines the splitting of downwelling solar radiation into components which are either reflected back to the atmosphere or absorbed by the surface. Land surface albedo is an important variable for the climate community, and therefore was defined by the Global Climate Observing System (GCOS) as an Essential Climate Variable (ECV). Within the scope of the Satellite Application Facility for Land Surface Analysis (LSA SAF) of EUMETSAT (European Organization for the Exploitation of Meteorological Satellites), a near-real time (NRT) daily albedo product was developed in the last decade from observations provided by the Spinning Enhanced Visible and Infrared Imager (SEVIRI) instrument on board the geostationary satellites of the Meteosat Second Generation (MSG) series. In this study we present a new collection of albedo satellite products based on the same satellite data. The MSG Ten-day Albedo (MTAL) product incorporates MSG observations over 31 days with a frequency of NRT production of 10 days. The MTAL collection is more dedicated to climate analysis studies compared to the daily albedo that was initially designed for the weather prediction community. For this reason, a homogeneous reprocessing of MTAL was done in 2018 to generate a climate data record (CDR). The resulting product is called MTAL-R and has been made available to the community in addition to the NRT version of the MTAL product which has been available for several years. The retrieval algorithm behind the MTAL products comprises three distinct modules: One for atmospheric correction, one for daily inversion of a semi-empirical model of the bidirectional reflectance distribution function, and one for monthly composition, that also determines surface albedo values. In this study the MTAL-R CDR is compared to ground surface measurements and concomitant albedo products collected by sensors on-board polar-orbiting satellites (SPOT-VGT and MODIS). We show that MTAL-R meets the quality requirements if MODIS or SPOT-VGT are considered as reference. This work leads to 14 years of production of geostationary land surface albedo products with a guaranteed continuity in the LSA SAF for the future years with the forthcoming third generation of European geostationary satellites.

The goal was to model irrigated (IBY) and rainfed (RBY) bean yields in central (Culiacán) and southern (Rosario) Sinaloa state as a function of the essential climate variables soil moisture, temperature, reference evapotranspiration, and precipitation. For Sinaloa, for the period 1982–2013 (October–March), the following were calculated: (a) temperatures, (b) average degree days for the bean, (c) cumulative reference evapotranspiration, and (d) cumulative effective precipitation. For essential climate variables, (e) daily soil moisture obtained from the European Space Agency and (f) IBY and RBY from the Agrifood and Fisheries Information Service were used. Multiple linear regressions were significant for predicting IBY–RBY (dependent variables) as a function of essential climate variables (independent variables). The four models obtained were significantly predictive: IBY–Culiacán (Pearson correlation (PC) = 0.590 > Pearson critical correlation (CPC) = |0.349|), RBY–Culiacán (PC = 0.734 > CPC = |0.349|), IBY–Rosario (PC = 0.621 > CPC = |0.355|), and RBY–Rosario (PC = 0.532 > CPC = |0.349|). Due to the lack of irrigation depth data, many studies only focus on modeling RBY; this study is the first in Sinaloa to predict IBY and RBY based on essential climate variables, contributing to the production of sustainable food.

The Essential Climate Variable (ECV) soil moisture (SM) datasets, originated from the European Space Agency, have revealed great potential for application in hydrology and agriculture. Hence, it is essential to continuously enhance the data quality and spatial completeness to satisfy the increasing scientific research requirements. In this study, we explore the potential possibility of Soil Moisture Active Passive (SMAP) datasets in filling the gaps of ECV SM. The comprehensive assessment results show that: (1) The data missing percent of gap-filled ECV decreases 20% on average, which can be one step closer to generate a seamlessly covered global land surface SM product with favorable quality. (2) Compared to the original ECV, the gap-filled ECV products express similar good response to the in-situ measurements, suggesting that the SMAP SM products could be taken to efficiently fill the gaps and consistently maintain favorable accuracy at the same time. (3) Compared to the in-situ measurements, the original ECV SM products demonstrate extremely high probability density peak percentages. Fortunately, this eminent high value could be effectively rectified through gap-filling progress using SMAP. Overall, this study conducts objective and detailed evaluation on the performance of applying SMAP to fill the gaps of ECV, and is expected to act as a valuable reference in ECV SM gap-filling method.

Turbulent and radiative exchanges of heat between the ocean and atmosphere (hereafter heat fluxes), ocean surface wind stress, and state variables used to estimate them, are Essential Ocean Variables (EOVs) and Essential Climate Variables (ECVs) influencing weather and climate. This paper describes an observational strategy for producing 3-hourly, 25-km (and an aspirational goal of hourly at 10-km) heat flux and wind stress fields over the global, ice-free ocean with breakthrough 1-day random uncertainty of 15 W m–2 and a bias of less than 5 W m–2. At present this accuracy target is met only for OceanSITES reference station moorings and research vessels (RVs) that follow best practices. To meet these targets globally, in the next decade, satellite-based observations must be optimized for boundary layer measurements of air temperature, humidity, sea surface temperature, and ocean wind stress. In order to tune and validate these satellite measurements, a complementary global in situ flux array, built around an expanded OceanSITES network of time series reference station moorings, is also needed. The array would include 500–1000 measurement platforms, including autonomous surface vehicles, moored and drifting buoys, RVs, the existing OceanSITES network of 22 flux sites, and new OceanSITES expanded in 19 key regions. This array would be globally distributed, with 1–3 measurement platforms in each nominal 10° by 10° box. These improved moisture and temperature profiles and surface data, if assimilated into Numerical Weather Prediction (NWP) models, would lead to better representation of cloud formation processes, improving state variables and surface radiative and turbulent fluxes from these models. The in situ flux array provides globally distributed measurements and metrics for satellite algorithm development, product validation, and for improving satellite-based, NWP and blended flux products. In addition, some of these flux platforms will also measure direct turbulent fluxes, which can be used to improve algorithms for computation of air-sea exchange of heat and momentum in flux products and models. With these improved air-sea fluxes, the ocean’s influence on the atmosphere will be better quantified and lead to improved long-term weather forecasts, seasonal-interannual-decadal climate predictions, and regional climate projections.

GCOS (Global Climate Observing System) is a long-term program for monitoring the climate, detecting the changes, and assessing their impacts. Remote sensing techniques are being increasingly used for climate-related measurements. Imagery of the SEVIRI instrument on board of the European geostationary satellites Meteosat-8 and Meteosat-9 are often used for the estimation of essential climate variables. In a joint project between the Swiss GCOS Office and ETH Zurich, geometric accuracy and temporal stability of 1-km resolution HRV channel imagery of SEVIRI have been evaluated over Switzerland. A set of tools and algorithms has been developed for the investigations. Statistical analysis and blunder detection have been integrated in the process for robust evaluation. The relative accuracy is evaluated by tracking large numbers of feature points in consecutive HRV images taken at 15-minute intervals. For the absolute accuracy evaluation, lakes in Switzerland and surroundings are used as reference. 20 lakes digitized from Landsat orthophotos are transformed into HRV images and matched via 2D translation terms at sub-pixel level. The algorithms are tested using HRV images taken on 24 days in 2008 (2 days per month). The results show that 2D shifts that are up to 8 pixels are present both in relative and absolute terms.