The Earth Radiation Budget (ERB) at the top of the atmosphere quantifies how the earth gains energy from the sun and looses energy to space. It is of fundamental importance for climate and climate change.

The current state of the art of the satellite measurements of the Earth Radiation Budget is reviewed. Combining all available measurements, the most likely value of the Total Solar Irradiance at solar minimum is 1362 W/m², the most likely earth albedo is 29.8 % and the most likely annual mean Outgoing Longwave Radiation is 238 W/m².

The current ERB measurements have sufficient stability to track the temporal variability of the Earth Energy Imbalance (EEI) driving climate change, but they can not measure its absolute value with sufficient accuracy. We introduce the Low Earth Orbit Novel Advanced Radiation Diurnal Observation (LEONARDO) concept which aims at making the first ever significant measurement of the EEI from space.

Decadal changes of the Outgoing Longwave Radiation (OLR) as measured by the Clouds and Earth's Radiant Energy System (CERES) from 2000 to 2018, The Earth Radiation Budget Experiment from 1985 to 1998, and the High-resolution Infrared Radiation Sounder from 1985 to 2018 are analysed. The OLR has been rising since 1985, and correlates well with the rising global temperature. An observational estimate of the derivative of the OLR with respect to temperature of 2.93 +/- 0.3 W/m²K is obtained. The regional patterns of the observed OLR change from 1985-2000 to 2001-2017 show a warming pattern in the Northern Hemisphere in particular in the Arctic, as well as tropical cloudiness changes related to a strengthening of La Nina.

The monitoring of the ERB relies mainly on the CERES program, with instruments on the morning orbit satellite Terra since 2000, and on the afternoon orbit satellites Aqua since 2003, Suomi NPP since 2012, and Noaa-20 since 2018. No Ceres follow-on mission is foreseen on a morning orbit satellite after Terra. Therefore it is usefull to complement the Ceres measurements, with those from the Earth Radiation Mission (ERM) instruments on the Feng Yung 3 (FY3) Chinese polar meteorological satellites. We present a validation of the ERM measurements on the FY3C morning orbit satellite – active since 2013 - by comparison with the Ceres measurements.

The planetary energy balance is the difference between how much solar radiation reaches Earth and the sum of outgoing reflected solar and emitted thermal radiation to space. If Earth absorbs more energy from the sun as visible light than it emits to space as thermal infrared radiation, it will eventually warm up, altering precipitation patterns, sea level, and extreme weather. At short timescales (< 30 years), natural fluctuations in ocean currents and atmospheric wind patterns can introduce substantial variations in both radiative fluxes at the top-of-atmosphere (TOA) and surface temperature. A recent example of this is the global warming “hiatus” (or “pause”) in the 2000s, in which the rate of increase in global mean surface temperature was lower than that during the prior two decades. Here we examine changes in Earth’s energy budget during and after the global warming “pause” (or “hiatus”) using observations from the Clouds and the Earth’s Radiant Energy System. We find a marked 0.83 Wm-2 reduction in global mean reflected shortwave (SW) TOA flux during the three years following the hiatus that results in an increase in net energy into the climate system. A partial radiative perturbation analysis reveals that decreases in low cloud cover are the primary driver of the decrease in SW TOA flux. The regional distribution of the SW TOA flux changes associated with the decreases in low cloud cover closely matches that of sea-surface temperature warming, which shows a pattern typical of the positive phase of the Pacific Decadal Oscillation. A simple energy budget framework is used to show that TOA radiation (particularly in the SW) likely played a dominant role in driving the marked increase in temperature tendency during the post-hiatus period. The presentation will also compare the CERES observations during and after the hiatus with state-of-the-art General Circulation Models simulations in which sea-surface temperatures and sea-ice boundary conditions are constrained by observations.

Water vapor in the upper troposphere strongly regulates the strength of water-vapor feedback, which is the primary process for amplifying the response of the climate system to external radiative forcings. Monitoring changes in upper-tropospheric water vapor and scrutinizing the causes of such changes are therefore of great importance for establishing the credibility of model projections of past and future climates. Here, we use 40 years of HIRS and MSU satellite observations to document near-global trends in upper tropospheric water vapor. Results are also compared to global climate model simulations performed with and without anthropogenic forcings. Our analysis demonstrates that the upper-tropospheric moistening observed over this period cannot be explained by natural causes and results principally from an anthropogenic warming of the climate. By attributing the observed increase directly to human activities, this study verifies the presence of the largest known feedback mechanism for amplifying anthropogenic climate change.

A rise in global surface temperature is often seen as an indication of climate change on Earth, and has been the subject of considerable public and scientific attention. Sea surface temperature (SST) measurements are scarce and satellite measurements offer the advantage of continuous coverage of SST over the remote oceans. The question remains of whether or not these measurements can have the accuracy and stability necessary for the study of climate trends and natural variability.
In this work, we present our computed global sea surface temperature (SST) dataset, i.e. the skin temperature over the sea, from the calibrated radiance measurements obtained twice a day using the Infrared Atmospheric Sounding Interferometer (IASI) instruments. IASI sounds the Earth-atmosphere system in the thermal infrared and covers the spectral range from 645 to 2760 cm-1. Three instruments have been launched in 2006, 2012 and 2018 aboard the Metop satellites.
The SSTs are computed from recently reprocessed L1C radiances data, using Planck’s function, at 100 radiative channels where the atmosphere is relatively transparent for cloud-free scenes. The channel selection method (based on jacobians analysis) allows us to rank the channels by highest to lowest sensitivity to SST. We analyze first the dependence of temperature on channel selection and we determine the ultimate number of channels that is necessary for proper SST use.
The SST dataset covers the IASI sounding period between 2007 and 2018, under cloud-free conditions. Climate means, inter-annual variability and trends in global SST are computed. We divide them into seasons, and look separately into day and night statistics. These results are then compared with those obtained using SSTs from the state-of-the art ECMWF’s ERA5 analysis. The comparison with ERA5 allows for an assessment of the satellite-derived SST. A general excellent agreement is found between the datasets, with relatively low biases and high correlation coefficients. Regions of better and worse agreement are highlighted and studied further.

The Earth’s Outgoing Longwave Radiation (OLR) is a key component in the study of climate feedbacks and processes. As part of the Earth’s radiation budget, it reflects how the Earth-atmosphere system compensates the incoming solar radiation at the top of the atmosphere. It can be retrieved from the radiance intensities measured by satellite sounders and integrated over all the zenith angles of observation. Since satellite instruments generally acquire the radiance at a limited number of viewing angle directions and because the radiance field is not isotropic, the conversion is however not straightforward. This problem is usually overcome by the use of empirical angular distribution models (ADMs) developed for different scene types that directly link the directional radiance measurement to the corresponding OLR.

OLR estimates from dedicated broadband instruments are available since the mid-1970s; however, such instruments only provide an integrated OLR estimate over a broad spectral range. They are therefore not well suited for tracking separately the impact of the different parameters affecting the OLR (including greenhouse gases), making it difficult to track down deficiencies in climate models. Currently, several hyperspectral instruments in space acquire radiances in the thermal infrared region, and in principle, these should allow to better constrain the OLR. However, as these instruments were not specifically designed to measure the OLR, there are several challenges to overcome. Here we propose a new retrieval algorithm for the estimation of the spectrally resolved OLR from measurements made by the IASI sounder on board the Metop satellites. It is based on a set of spectrally resolved ADMs developed from synthetic spectra for a large selection of scene types associated with different states of the atmosphere and the surface. Atmospheric and surface parameters are derived from the Copernicus Atmosphere Monitoring Service (CAMS) reanalysis dataset and selected using a dissimilarity-based subset selection algorithm. These spectral ADMs are then used to convert the measured IASI radiances into spectral OLR.

We then analyze how the integrated IASI OLR compare with the CERES and integrated AIRS OLR. We present time series of OLR over the 10 years of IASI for selected spectral channels. Trends are analyzed in the spectral ranges of major climate gases (e.g. CO2, CH4), which provides robust constraints on the climatic impact of their continued rise. We also show the existence of strong trends for weaker absorbers (such as CFCs).

Land surface temperature (LST) is one of the most relevant parameters when addressing the physical processes that take place at the surface of the Earth. Satellite data are particularly appropriate for measuring LST over the globe with high temporal and spatial resolution. The estimation of LST from space-borne sensors has been systematically performed at a global scale for over three decades and LST climate data records (CDRs) are now available, opening new perspectives for climate monitoring.
The retrieval of LST from satellite observations generally relies on measurements in the thermal infrared (IR) window, the spectral band that contains the peak of Earth’s spectral radiance and presents relatively weak atmospheric attenuation under clear-sky conditions. Although there is a large number of IR sensors on-board geostationary satellites and polar orbiters providing LST retrievals with different temporal and spatial resolutions, the use of IR observations limits LST estimates to clear-sky conditions. The use of IR LST datasets may therefore result in analyses that are “biased” towards clear-sky conditions. Such “clear-sky bias” of IR-based LST has never been quantified and therefore the true impact on LST-based studies when restricting to clear-sky data is still uncertain. On the other hand, an “all-weather” global LST database may be obtained from passive microwave (MW) measurements, which are much less affected by clouds.
A 3-year record of all-weather MW LST is used here to provide an estimate of the clear-sky bias of IR LST at the global scale. The MW LST dataset used for this purpose relies on MW observations provided by the Advanced Microwave Scanning Radiometer–Earth Observing System (AMSR-E) onboard NASA’s Aqua satellite, and is available through the ESA DUE GlobTemperature project. The associated cloud coverage to each observation is provided by the Moderate Resolution Imaging Spectroradiometer (MODIS) on-board the same satellite. Using the subset of clear-sky MW LST as a surrogate for IR LST, global maps of “LST clear-sky bias” can therefore be generated that will allow making a first assessment of the spatial and temporal characteristics of the deviations. These are also analyzed locally, taking into account the predominant cloud regime.