Satellite Climate Data Research Articles

Does the subsidiary's climate risk matter? Evidence from the stock price crash

Combining satellite climate data with the subsidiaries’ geographical locations of listed firms in China, we document a significant positive relation between subsidiary-level climate risk exposure and future stock price crash risk of conglomerate. The results are stronger for firms with higher CEO performance pressure, higher subsidiary climate risk management costs and weaker external governance. Our findings shed light on the economic consequences of climate risks at the subsidiary level.

Studying the Aerosol Effect on Deep Convective Clouds over the Global Oceans by Applying Machine Learning Techniques on Long-Term Satellite Observation

Long-term (1982–2019) satellite climate data records (CDRs) of aerosols and clouds, reanalysis data of meteorological fields, and machine learning techniques are used to study the aerosol effect on deep convective clouds (DCCs) over the global oceans from a climatological perspective. Our analyses are focused on three latitude belts where DCCs appear more frequently in the climatology: the northern middle latitude (NML), tropical latitude (TRL), and southern middle latitude (SML). It was found that the aerosol effect on marine DCCs may be detected only in NML from long-term averaged satellite aerosol and cloud observations. Specifically, cloud particle size is more susceptible to the aerosol effect compared to other cloud micro-physical variables (e.g., cloud optical depth). The signature of the aerosol effect on DCCs can be easily obscured by meteorological covariances for cloud macro-physical variables, such as cloud cover and cloud top temperature (CTT). From a machine learning analysis, we found that the primary aerosol effect (i.e., the aerosol effect without meteorological feedbacks and covariances) can partially explain the aerosol convective invigoration in CTT and that meteorological feedbacks and covariances need to be included to accurately capture the aerosol convective invigoration. From our singular value decomposition (SVD) analysis, we found the aerosol effects in the three leading principal components (PCs) may explain about one third of the variance of satellite-observed cloud variables and significant positive or negative trends are only observed in the lead PC1 of cloud and aerosol variables. The lead PC1 component is an effective mode for detecting the aerosol effect on DCCs. Our results are valuable for the evaluation and improvement of aerosol-cloud interactions in the long-term climate simulations of global climate models.

Climatology of the global summer monsoon rainy seasons: Revisited from a high-resolution satellite climate data record

Reliable information on the monsoon rainfall at finer spatiotemporal scales has always been crucial for applications in hydrometeorology, water resource management, disaster prevention, and assessment of numerical model outputs. Here, the spatial-temporal characteristics of the summer monsoon rainy seasons worldwide are revisited using a global, high-resolution, bias-corrected satellite rainfall climate data record. The Precipitation Estimation from Remotely Sensed Information using Artificial Neural Networks–Climate Data Record (PERSIANN-CDR) provides 0.25° rainfall estimates for the latitude band 60°S–60°N from 1983 to the near present. The global monsoon regimes are defined based on the annual rainfall range and the summer rainfall ratio. The onset, peak, and withdrawal of the rainy season are then determined using a concise yet pertinent rainfall parameter with universal objective criteria.Overall, six major summer monsoon regimes of the globe, the Asian Monsoon, Indonesian–Australian monsoon, North African monsoon, South African monsoon, North American and South American monsoon, are all reasonably represented by PERSIANN-CDR. The progression patterns of climatological onset, peak, and withdrawal phases of these monsoons are consistent with those reported by previous global and regional monsoon studies. Furthermore, compared with other coarser resolution satellite rainfall estimates such as the Climate Prediction Center Merged Analysis of Precipitation (CMAP), the present result is clearly in better agreement with that obtained from ground-based observations since it allows the determination of more detailed information associated with local-scale factors such as the sharp land-sea contrast, local surface heating, and topography effects. However, some “spots”, particularly over complex topography lacking ground truth validations, are revealed to have rainy season characteristics that differ substantially from those over the surrounding areas and have not been fully described by published literature. These distinct features need to be confirmed by further regional monsoon studies.

Analyzing Sensitive Aerosol Regimes and Active Geolocations of Aerosol Effects on Deep Convective Clouds over the Global Oceans by Using Long-Term Operational Satellite Observations

Long-term satellite climate data records of aerosol and cloud along with meteorological reanalysis data have been used to study the aerosol effects on deep convective clouds (DCCs) over the global oceans from a climatology perspective. Our focus is on identifying sensitive aerosol regimes and active geolocations of the aerosol effects on DCCs by using statistical analyses on long-term averaged aerosol and cloud variables. We found the aerosol effect tends to manifest relatively easily on the long-term mean values of observed cloud microphysical variables (e.g., cloud particle size and ice water amount) compared to observed cloud macrophysical variables (e.g., cloud cover and cloud top height). An increase of aerosol loading tends to increase DCC particle size and ice water amount in the tropical convergence zones but decrease them in the subtropical subsidence regions. The aerosol effect on the cloud microphysical variables is also likely to manifest over the northwestern Pacific Ocean and central and eastern subtropical Pacific Ocean. The aerosol effect manifested on the microphysical cloud variables may also propagate to cloud cover but weakly to cloud top height since the latter is more susceptible to the influence of cloud dynamical and thermodynamic processes. Our results, based on the long-term averaged operational satellite observation, are valuable for the evaluation and improvement of aerosol-cloud interactions in global climate models.

Climatology of the Global Summer Monsoon Rainy Seasons: Revisited from a High-Resolution Satellite Climate Data Record

Climatology of the Global Summer Monsoon Rainy Seasons: Revisited from a High-Resolution Satellite Climate Data Record

Consistency of Satellite Climate Data Records for Earth System Monitoring

Abstract Climate data records (CDRs) of essential climate variables (ECVs) as defined by the Global Climate Observing System (GCOS) derived from satellite instruments help to characterize the main components of the Earth system, to identify the state and evolution of its processes, and to constrain the budgets of key cycles of water, carbon, and energy. The Climate Change Initiative (CCI) of the European Space Agency (ESA) coordinates the derivation of CDRs for 21 GCOS ECVs. The combined use of multiple ECVs for Earth system science applications requires consistency between and across their respective CDRs. As a comprehensive definition for multi-ECV consistency is missing so far, this study proposes defining consistency on three levels: 1) consistency in format and metadata to facilitate their synergetic use (technical level); 2) consistency in assumptions and auxiliary datasets to minimize incompatibilities among datasets (retrieval level); and 3) consistency between combined or multiple CDRs within their estimated uncertainties or physical constraints (scientific level). Analyzing consistency between CDRs of multiple quantities is a challenging task and requires coordination between different observational communities, which is facilitated by the CCI program. The interdependencies of the satellite-based CDRs derived within the CCI program are analyzed to identify where consistency considerations are most important. The study also summarizes measures taken in CCI to ensure consistency on the technical level, and develops a concept for assessing consistency on the retrieval and scientific levels in the light of underlying physical knowledge. Finally, this study presents the current status of consistency between the CCI CDRs and future efforts needed to further improve it.

On the Land-Sea Contrast in the Surface Solar Radiation (SSR) in the Baltic Region

The climatological surface solar radiation (SSR; also called global radiation), which is largely dependent on cloud conditions, is an important indicator of the solar energy production potential. In the Baltic area, previous studies have indicated lower cloud amounts over seas than over land, in particular during the summer. However, the existing literature on the SSR climate or how it translates into solar energy potential has not paid much attention to how the SSR behaves quantitatively in relation to the coastline. In this paper, we have studied the climatological land–sea contrast of the SSR over the Baltic area. For this, we used two satellite climate data records, CLARA-A2 and SARAH-2, together with a coastline data base and ground-based pyranometer measurements of the SSR. We analyzed the behaviour of the climatological mean SSR over the period 2003–2013 as a function of the distance to the coastline. The results show that off-shore locations on average receive higher SSR than inland areas and that the land–sea contrast in the SSR is strongest during the summer. Furthermore, the land–sea contrast in the summer time SSR exhibits similar behavior in various parts of the Baltic. For CLARA-A2, which shows better agreement with the ground-based measurements than SARAH-2, the annual SSR is 8% higher 20 km off the coastline than 20 km inland. For summer, i.e., June–August, this difference is 10%. The observed land–sea contrast in the SSR is further shown to correspond closely to the behavior of clouds. Here, convective clouds play an important role as they tend to form over inland areas rather than over the seas during the summer part of the year.

Distinct microalgae species for food\u2014part 1: a methodological (top-down) approach for the life cycle assessment of microalgae cultivation in tubular photobioreactors

Specific microalgae species are an adequate source of EPA and DHA and are able to provide a complete protein, which makes them highly interesting for human nutrition. However, microalgae cultivation has also been described to be energy intensive and environmentally unfavorable in pilot-scale reactors. Moreover, production in cold temperature zones has not been sufficiently investigated. In particular, the effects of tube materials and cultivation season length have rarely been previously investigated in the context of a comparative LCA of microalgae cultivation. A computational “top-down” model was conducted to calculate input flows for Nannochloropsis sp. and Phaeodactylum tricornutum cultivation in a hypothetical tubular photobioreactor. Cultivation processes were calculated according to detailed satellite climatic data for the chosen location in Central Germany. This model was applied to a set of different scenarios, including variations in photobioreactor material, tube diameter, microalgae species, and cultivation season length. Based on these data, a life cycle assessment (LCA) was performed following ISO standard 14040/44. The impact assessment comprised the global warming potential, acidification, eutrophication, cumulative energy demand, and water scarcity. The results showed that a long cultivation season in spring and fall was always preferable in terms of environmental impacts, although productivity decreased significantly due to the climatic preconditions. Acrylic glass as a tube material had higher environmental impacts than all other scenarios. The cultivation of an alternative microalgae species showed only marginal differences in the environmental impacts compared with the baseline scenario. Critical processes in all scenarios included the usage of hydrogen peroxide for the cleaning of the tubes, nitrogen fertilizer, and electricity for mixing, centrifugation, and drying. Microalgae cultivation in a tubular photobioreactor in a “cold-weather” climate for food is sustainable and could possibly be a complement to nutrients from other food groups. The added value of this study lies in the detailed description of a complex and flexible microalgae cultivation model. The new model introduced in this study can be applied to numerous other scenarios to evaluate photoautotrophic microalgae cultivation in tubular photobioreactors. Thus, it is possible to vary the facility location, seasons, scale, tube dimensions and material, microalgae species, nutrient inputs, and flow velocity. Moreover, single processes can easily be complemented or exchanged to further adjust the model individually, if, for instance, another downstream pathway is required.

Anticipating urbanization-led land cover change and its impact on local climate using time series model: a study on Dhaka city

Urbanization-led changes in natural landscape often result in environmental degradation and subsequently contribute to local climate variability. Therefore, apart from global climate change, Dhaka city’s ongoing rapid urban growth may result in altering future local climate patterns significantly. This study explores transition relationships between urbanization (population), land cover, and climate (temperature) of Dhaka city beginning in 1975 through to forecast scenarios up to 2035. Satellite image, geographic, demographic, and climatic data were analyzed. Change in core urban land cover (area) was regarded as a function of population growth and was modeled using linear regression technique. The study developed and validated a time series (ARIMA) model for predicting mean maximum temperature change where (forecasted) land cover scenarios were regressors. Throughout the studied period, the city exhibited an increasing urbanization trend that indicated persistent growth of core urban land cover in future. As a result, the city’s mean maximum temperature was found likely to increase by around 1.5-degree Celsius during 2016–2035 on average from that of observed 1996–2015 period. It is expected that findings of this study may help in recognizing urbanization-led climate change easily, which is crucial to effective climate change management actions and urban planning.

Climatology Perspective of Sensitive Regimes and Active Regions of Aerosol Indirect Effect for Cirrus Clouds over the Global Oceans

Long-term satellite climate data records (CDRs) of clouds and aerosols are used to investigate the aerosol indirect effect (AIE) of cirrus clouds over the global oceans from a climatology perspective. Our study focuses on identifying the sensitive regimes and active regions where AIE signatures easily manifest themselves in the sense of the long-term average of cloud and aerosol variables. The aerosol index (AIX) regimes of AIX < 0.18 and 0.18 < AIX < 0.46 are respectively identified as the sensitive regimes for negative and positive aerosol albedos and lifetime effects of cirrus clouds. Relative humidity first decreases (along with upward motions) and then reverses to increase (along with downward motions) in the first regime of negative aerosol albedo and lifetime effects. Relatively wet and strong upward motions are the favorable meteorological conditions for the second regime of positive aerosol albedo and lifetime effects. Two swath regions extending from 15°N to 30°N over the east coastal oceans of China and the USA are the active regions of positive aerosol albedo effects. Positive aerosol lifetime effects are only active or easy to manifest in the regions where a positive aerosol albedo effect is active. The results based on the long-term averaged satellite observations are valuable for the evaluation and improvement of aerosol-cloud interactions for cirrus clouds in global climate models.

A validated model of a photovoltaic water pumping system for off-grid rural communities

The low electrification rate in rural sub-Saharan Africa prevents access to energy services which are essential to improve living conditions. One of these energy services is electrified water pumping, which is particularly relevant for these areas where water access continues being a significant challenge. Pumping systems powered by photovoltaic energy have emerged as an interesting solution in off-grid areas. This article presents a model of photovoltaic water pumping system (PVWPS) for providing domestic water to off-grid rural communities. The model simulates the pumped flow rate and the water level in the storage tank from the climatic data (irradiance, ambient temperature) and the profile of water collection by the users of the system. The modelling of the different stages of the energy conversion chain and a method for identifying the unknown parameters of PVWPS are presented in this article. The model is applied to a pilot PVWPS situated in a rural village of Burkina Faso. The comparison between the measurements performed on the system and the model outputs allows to validate the model experimentally. Results indicate that the model permits to accurately simulate the water height in the tank both when climatic data from local sensors and from satellite are inputted in the model. The model could therefore be applied to other off-grid areas to perform techno-economic optimization and size new PVWPS as well as to evaluate the performances of existing PVWPS. The originalities of this work include the consideration of the water collection profile as a model input and the monitoring of a PVWPS in a rural village of Sub-Saharan Africa, an area where no continuous measurements on these systems has been performed, to the best knowledge of the authors. Further, the comparison of the impact of inputting satellite climatic data instead of measured ones on the PVWPS model accuracy is also a novel contribution.

Modelling spatio-temporal data of dengue fever using generalized additive mixed models

Epidemiological studies have revealed a complex association between weather and dengue transmission. Our aim is the development of a Spatio-temporal modelling of dengue fever via a Generalized Additive mixed model (GAMM). The structure is based on unknown smoother functions for climatic and a set of non-climatic covariates. All the climatic covariates were found statistically significant with optimal lagged effect and the smoothed curves fairly captured the real dynamic on dengue fever. It was also found that critical levels of dengue cases were reached with temperature between 26 °C and 30 °C. The findings also revealed for the first time that the El Niño phenomenon fluctuating between 26.5 °C and 28.0 °C had the worse impact on dengue transmission. This study brings together a large dataset from different sources including Ministry of Health from Venezuela. It was also benefited from a remote satellite climatic data provided by the National Aeronautics and Space Administration (NASA).

Temporal Means and Variability of Arctic Sea Ice Melt and Freeze Season Climate Indicators Using a Satellite Climate Data Record

Information on the timing of Arctic snow and ice melt onset, sea ice opening, retreat, advance, and closing, can be beneficial to a variety of stakeholders. Sea ice modelers can use information on the evolution of the ice cover through the rest of the summer to improve their seasonal sea ice forecasts. The length of the open water season (as derived from retreat/advance dates) is important for human activities and for wildlife. Long-term averages and variability of these dates as climate indicators are beneficial to business strategic planning and climate monitoring. In this study, basic characteristics of temporal means and variability of Arctic sea ice climate indicators derived from a satellite-based climate data record from March 1979 to February 2017 melt and freeze seasons are described. Our results show that, over the Arctic region, anomalies of snow and ice melt onset, ice opening and retreat dates are getting earlier in the year at a rate of more than 5 days per decade, while that of ice advance and closing dates are getting later at a rate of more than 5 days per decade. These significant trends resulted in significant upward trends for anomalies of inner and outer ice-free periods at a rate of nearly 12 days per decade. Small but significant downward trends of seasonal ice loss and gain period anomalies were also observed at a rate of −1.48 and −0.53 days per decade, respectively. Our analyses also demonstrated that the means of these indicators and their trends are sensitive to valid data masks and regional averaging methods.

Signal Processing Enhancements to Improve Instantaneous Accuracy of a Scanning Bolometer: Application to MERBE

Cloud radiative forcing climate signals cannot be detected and proved sufficiently by the existing space-based Earth Radiation Budget (ERB) measurements due to the insufficient instrument calibration accuracy, relative to the sizes of mere decadal-scale trends. This paper, therefore, introduces a new project called the Moon and ERB Experiment (MERBE). Its methodology is for all earth observations using broadband thermal detectors, such as bolometers, to adhere toward more traceable calibration standards based on scans of our moon. This traceability limits instrument-dependent biases and spurious drifts for the past decades of existing earth data measurements as well as future measurements. The goal of the MERBE project is to substantially increase existing satellite climate data accuracy; therefore, a reexamination of all aspects of space-based ERB device calibration was also warranted. This paper concentrates on the improvement of ERB data quality from the level of detector voltages and onward. A component model of bolometer thermal and electronic time response and offset effects is used to design an improved inversion filter, which deconvolves path direction dependence of a scanning thermal detector. For every ERB instrument on the TRMM, Terra, Aqua, and SNPP satellites, instantaneous error reductions are achieved in all recovered MERBE radiance measurements. This also will allow for more accurate results from ERB device lunar scans based on improved telescope field-of-view mapping, as presented in other MERBE work.

The International Satellite Cloud Climatology Project H-Series climate data record product

Abstract. This paper describes the new global long-term International Satellite Cloud Climatology Project (ISCCP) H-series climate data record (CDR). The H-series data contain a suite of level 2 and 3 products for monitoring the distribution and variation of cloud and surface properties to better understand the effects of clouds on climate, the radiation budget, and the global hydrologic cycle. This product is currently available for public use and is derived from both geostationary and polar-orbiting satellite imaging radiometers with common visible and infrared (IR) channels. The H-series data currently span July 1983 to December 2009 with plans for continued production to extend the record to the present with regular updates. The H-series data are the longest combined geostationary and polar orbiter satellite-based CDR of cloud properties. Access to the data is provided in network common data form (netCDF) and archived by NOAA's National Centers for Environmental Information (NCEI) under the satellite Climate Data Record Program (https://doi.org/10.7289/V5QZ281S). The basic characteristics, history, and evolution of the dataset are presented herein with particular emphasis on and discussion of product changes between the H-series and the widely used predecessor D-series product which also spans from July 1983 through December 2009. Key refinements included in the ISCCP H-series CDR are based on improved quality control measures, modified ancillary inputs, higher spatial resolution input and output products, calibration refinements, and updated documentation and metadata to bring the H-series product into compliance with existing standards for climate data records.

Land suitability modeling for camel husbandry in Dubti and Mile districts, North East Ethiopia

This study was conducted to evaluate the suitability of land for camel husbandry with the help of GIS and Remote Sensing techniques in Dubti and Mile district of Afar National Regional State. Spatial modeling technique particularly weighted overlay analysis was employed to evaluate suitability of land. Land remote sensing satellite image, climatic data (temperature and rainfall) and topographical data were factors of the study. Besides, these factors, road and drinking water availability and accessibility maps were also considered for the final suitability map. Geographic information system software (ARCGIS) and earth resources data analysis system Image software were used to manipulate and analyze the data. Firstly, land use land cover, rainfall, temperature, elevation and slope factor suitability maps were produced. Furthermore, road and drinking water accessibility maps were also developed. Based on the factor suitability maps, overall land suitability evaluation was undertaken for the enhancement of camel production. As a result, the final map indicated that most of the western part of Mile district is found to be suitable for camel production and areas of the eastern and north eastern part of Dubti district are not suitable for the purpose mainly due to inaccessibility of water and forage as basic demands for the camels to reproduce.

Using Long-Term Satellite Observations to Identify Sensitive Regimes and Active Regions of Aerosol Indirect Effects for Liquid Clouds Over Global Oceans.

Long‐term (1981–2011) satellite climate data records of clouds and aerosols are used to investigate the aerosol‐cloud interaction of marine water cloud from a climatology perspective. Our focus is on identifying the regimes and regions where the aerosol indirect effects (AIEs) are evident in long‐term averages over the global oceans through analyzing the correlation features between aerosol loading and the key cloud variables including cloud droplet effective radius (CDER), cloud optical depth (COD), cloud water path (CWP), cloud top height (CTH), and cloud top temperature (CTT). An aerosol optical thickness (AOT) range of 0.13 < AOT < 0.3 is identified as the sensitive regime of the conventional first AIE where CDER is more susceptible to AOT than the other cloud variables. The first AIE that manifests as the change of long‐term averaged CDER appears only in limited oceanic regions. The signature of aerosol invigoration of water clouds as revealed by the increase of cloud cover fraction (CCF) and CTH with increasing AOT at the middle/high latitudes of both hemispheres is identified for a pristine atmosphere (AOT < 0.08). Aerosol invigoration signature is also revealed by the concurrent increase of CDER, COD, and CWP with increasing AOT for a polluted marine atmosphere (AOT > 0.3) in the tropical convergence zones. The regions where the second AIE is likely to manifest in the CCF change are limited to several oceanic areas with high CCF of the warm water clouds near the western coasts of continents. The second AIE signature as represented by the reduction of the precipitation efficiency with increasing AOT is more likely to be observed in the AOT regime of 0.08 < AOT < 0.4. The corresponding AIE active regions manifested themselves as the decline of the precipitation efficiency are mainly limited to the oceanic areas downwind of continental aerosols. The sensitive regime of the conventional AIE identified in this observational study is likely associated with the transitional regime from the aerosol‐limited regime to the updraft‐limited regime identified for aerosol‐cloud interaction in cloud model simulations.

Analysis of observed and perceived climate change and variability in Arsi Negele District, Ethiopia

Climate change and variability has been detected in Ethiopia. Smallholder and subsistence farmers, pastoralists and forest-dependent households are the most hit by climate-related hazards. They have to have perception of climate change in order to respond it through making coping and/or adaptation strategies. Local perceptions and coping strategies provide a crucial foundation for community-based climate change adaptation measures. This study was specifically designed to (1) assess households’ perception and knowledge in climate change and/or variability, and (2) establish the observed changes in climate parameters with community perceptions and climate anomalies. Purposive stratified random sampling method has been used to gather information from 355 sample households for individual interviews supplemented by group discussion and key informants interviews. The analysis of observed and satellite climate data for the study district showed that mean maximum and minimum temperature for the period 1983–2014 has increased by 0.047 and 0.028 °C/year, respectively. However, the total rainfall has declined by 10.16 mm per annum. Seasonally, the rainfall has declined by 2.198, 4.541, 1.814 and 1.608 mm per annum for Ethiopian summer, spring, autumn and winter seasons, respectively. Similarly, the mean maximum temperature of the study area had showed an increment of 0.035, 0.049, 0.044 and 0.065 °C per year for spring, winter, autumn and summer seasons, respectively. The observed climate variation has been confirmed by people’s perception. Considering what had been the existed situations before 30 years ago as normal, an increase in temperature, an increase in drought frequency, a decrease in total rainfall, erratic nature of its distribution and the tardiness of its onset had been perceived by 88, 70, 97, 80 and 94% of the respondents, respectively, at current time—2015. Deforestation as a casual factor of climate change and variability had been perceived by 99.7% of the respondents. This had been also confirmed by scientific studies as it emits carbon dioxide and is the main driver of climate change and variability. Indigenous knowledge, including climate predictions, has been used by people to implement their day-to-day agricultural activities. Therefore, science should be integrated with the perception and indigenous knowledge of people to come up with concrete solution for climate change and variability impacts on human livelihoods.

ECONOMIC IMPACT OF CLIMATE CHANGE ON MAIZE PRODUCTION IN KENYA

Purpose: The purpose of the study was to investigate the economic impact of climate change on maize production in Kenya.Methodology: The study used climate, soil and household data for 1357 households. The climate data was from ARTES (African Rainfall and Evaluation System) and Satellite climate data while the Soil data was from Kenya Soil Survey conducted by Kenya Agricultural Research Institute. The household data was obtained from Tegemeo Research Institute.Results: The regressions results suggest that climate has a significant impact on maize production. The study found that temperature has a bigger impact on maize production compared to precipitation as evidenced by the elasticity of temperature and precipitation.Unique contribution to theory, practice and policy: The study recommends that policy efforts should be directed at addressing the impact of climate change on maize production. Through research and development, the government should encourage the development of maize varieties that can adapt to the future expected hot conditions.

Satellite Climate Data Records: Development, Applications, and Societal Benefits

This review paper discusses how to develop, produce, sustain, and serve satellite climate data records (CDRs) in the context of transitioning research to operation (R2O). Requirements and critical procedures of producing various CDRs, including Fundamental CDRs (FCDRs), Thematic CDRs (TCDRs), Interim CDRs (ICDRs), and climate information records (CIRs) are discussed in detail, including radiance/reflectance and the essential climate variables (ECVs) of land, ocean, and atmosphere. Major international CDR initiatives, programs, and projects are summarized. Societal benefits of CDRs in various user sectors, including Agriculture, Forestry, Fisheries, Energy, Heath, Water, Transportation, and Tourism are also briefly discussed. The challenges and opportunities for CDR development, production and service are also addressed. It is essential to maintain credible CDR products by allowing free access to products and keeping the production process transparent by making source code and documentation available with the dataset.