Global‐scale changes in water vapor and responses to surface temperature variability since 1979 are evaluated across a range of satellite and ground‐based observations, a reanalysis (ERA5) and coupled and atmosphere‐only CMIP6 climate model simulations. Global‐mean column integrated water vapor increased by 1%/decade during 1988–2014 in observations and atmosphere‐only simulations. However, coupled simulations overestimate water vapor trends and this is partly explained by past studies showing that internal climate variability suppressed observed warming in this period. Decreases in low‐altitude tropical water vapor in ERA5 and ground‐based observations before around 1993 are considered suspect based on inconsistency with simulations and increased column integrated water vapor in microwave satellite data since 1979. Atmospheric Infra‐red Sounder satellite data does not capture the increased tropospheric water vapor since 2002 shown by other satellite, reanalysis, and model products. However, global water vapor responses to interannual surface temperature variability are consistent across data sets with increases of ∼4%–5% near the surface and 10%–15% at 300 hPa for each 1°C increase in global surface temperature. Global water vapor responses are explained by thermodynamic amplification of upper tropospheric temperature changes and the Clausius Clapeyron temperature dependence of saturation vapor pressure that are dominated by the tropical ocean responses. Upper tropospheric moistening is larger in climate model simulations with greater upper tropospheric warming.

The Global Energy and Water cycle Exchanges (GEWEX) Data and Assessments Panel (GDAP) initiated the GEWEX Water Vapor Assessment (G-VAP), which has the main objectives to quantify the current state of the art in water vapour products being constructed for climate applications and to support the selection process of suitable water vapour products by GDAP for its production of globally consistent water and energy cycle products. During the construction of the G-VAP data archive, freely available and mature satellite and reanalysis data records with a minimum temporal coverage of 10 years were considered. The archive contains total column water vapour (TCWV) as well as specific humidity and temperature at four pressure levels (1000, 700, 500, 300 hPa) from 22 different data records. All data records were remapped to a regular longitude–latitude grid of 2^∘ × 2^∘. The archive consists of four different folders: 22 TCWV data records covering the period 2003–2008, 11 TCWV data records covering the period 1988–2008, as well as 7 specific humidity and 7 temperature data records covering the period 1988–2009. The G-VAP data archive is referenced under the following digital object identifier (doi): https://doi.org/10.5676/EUM_SAF_CM/GVAP/V001. Within G-VAP, the characterization of water vapour products is, among other ways, achieved through intercomparisons of the considered data records, as a whole and grouped into three classes of predominant retrieval condition: clear-sky, cloudy-sky and all-sky. Associated results are shown using the 22 TCWV data records. The standard deviations among the 22 TCWV data records have been analysed and exhibit distinct maxima over central Africa and the tropical warm pool (in absolute terms) as well as over the poles and mountain regions (in relative terms). The variability in TCWV within each class can be large and prohibits conclusions about systematic differences in TCWV between the classes.

The accuracy of land surface temperature (LST) observations is critical to many applications. Any observation of LST is subject to incomplete knowledge, so an accurate assessment of the uncertainty budget is critical. We present a comprehensive and consistent approach to determining an uncertainty budget for LST products. We apply this approach to the Moderate Resolution Imaging Spectroradiometer (MODIS) instrument on-board the Aqua satellite. In order to generate the uncertainty model, a new implementation of the generalised split-window algorithm is applied, in which retrieval coefficients are categorised by viewing angle and water vapour. Validation of the LST against in situ data shows a mean absolute bias of 0.37 K for daytime and 0.73 K for nighttime. The average standard deviation per site is 1.53 K for daytime and 1.21 K for nighttime. Uncertainties from the implemented model are estimates in their own right and are also validated. We do this by comparing the standard deviation of the differences between the satellite and in situ LSTs, and the total uncertainties of the validation matchups. We show that the uncertainty model provides a good fit. Our approach offers a framework for quantifying uncertainties for LST that is equally applicable across different sensors and different retrieval approaches.

To date, a large variety of water vapour data records from satellite and reanalysis are available. It is key to understand the quality and uncertainty of these data records in order to fully exploit these records and to avoid data being employed incorrectly or misinterpreted. Therefore, it is important to inform users on accuracy and limitations of these data records based on consistent inter-comparisons carried out in the framework of international assessments. Addressing this challenge is the major objective of the Global Water and Energy Exchanges (GEWEX) water vapor assessment (G-VAP) which was initiated by the GEWEX Data and Assessments Panel (GDAP). Here, an overview of G-VAP objectives and an introduction to the results from G-VAP’s first phase are given. After this overview, a summary of available data records on water vapour and closely related variables and a short introduction to the utilized methods are presented. The results from inter-comparisons, homogeneity testing and inter-comparison of trend estimates, achieved within G-VAP’s first phase are summarized. The conclusions on future research directions for the wider community and for G-VAP’s next phase are outlined and recommendations have been formulated. For instance, a key recommendation is the need for recalibration and improved inter-calibration of radiance data records and subsequent reprocessing in order to increase stability and to provide uncertainty estimates. This need became evident from a general disagreement in trend estimates (e.g., trends in TCWV ranging from −1.51 ± 0.17 kg/m²/decade to 1.22 ± 0.16 kg/m²/decade) and the presence of break points on global and regional scale. It will be a future activity of G-VAP to reassess the stability of updated or new data records and to assess consistency, i.e., the closeness of data records given their uncertainty estimates.

Day-to-day variations in surface air temperature affect society in many ways, but daily surface air temperature measurements are not available everywhere. Therefore, a global daily picture cannot be achieved with measurements made in situ alone and needs to incorporate estimates from satellite retrievals. This article presents the science developed in the EU Horizon 2020–funded EUSTACE project (2015–19, www.eustaceproject.org) to produce global and European multidecadal ensembles of daily analyses of surface air temperature complementary to those from dynamical reanalyses, integrating different ground-based and satellite-borne data types. Relationships between surface air temperature measurements and satellite-based estimates of surface skin temperature over all surfaces of Earth (land, ocean, ice, and lakes) are quantified. Information contained in the satellite retrievals then helps to estimate air temperature and create global fields in the past, using statistical models of how surface air temperature varies in a connected way from place to place; this needs efficient statistical analysis methods to cope with the considerable data volumes. Daily fields are presented as ensembles to enable propagation of uncertainties through applications. Estimated temperatures and their uncertainties are evaluated against independent measurements and other surface temperature datasets. Achievements in the EUSTACE project have also included fundamental preparatory work useful to others, for example, gathering user requirements, identifying inhomogeneities in daily surface air temperature measurement series from weather stations, carefully quantifying uncertainties in satellite skin and air temperature estimates, exploring the interaction between air temperature and lakes, developing statistical models relevant to non-Gaussian variables, and methods for efficient computation.

The development of algorithms for the retrieval of water cycle components from satellite data – such as total column water vapor content (TCWV), precipitation (P), latent heat flux, and evaporation (E) – has seen much progress in the past 3 decades. In the present study, we compare six recent satellite-based retrieval algorithms and ERA5 (the European Centre for Medium-Range Weather Forecasts' fifth reanalysis) freshwater flux (E−P) data regarding global and regional, seasonal and interannual variation to assess the degree of correspondence among them. The compared data sets are recent, freely available, and documented climate data records (CDRs), developed with a focus on stability and homogeneity of the time series, as opposed to instantaneous accuracy. One main finding of our study is the agreement of global ocean means of all E−P data sets within the uncertainty ranges of satellite-based data. Regionally, however, significant differences are found among the satellite data and with ERA5. Regression analyses of regional monthly means of E, P, and E−P against the statistical median of the satellite data ensemble (SEM) show that, despite substantial differences in global E patterns, deviations among E−P data are dominated by differences in P throughout the globe. E−P differences among data sets are spatially inhomogeneous. We observe that for ERA5 long-term global E−P is very close to 0 mm d⁻¹ and that there is good agreement between land and ocean mean E−P, vertically integrated moisture flux divergence (VIMD), and global TCWV tendency. The fact that E and P are balanced globally provides an opportunity to investigate the consistency between E and P data sets. Over ocean, P (nearly) balances with E if the net transport of water vapor from ocean to land (approximated by over-ocean VIMD, i.e., ∇⋅(vq)_ocean) is taken into account. On a monthly timescale, linear regression of $E_{\text{ocean}}-\mathrm{\nabla }\cdot (vq{)}_{\text{ocean}}$ with P_ocean yields R²=0.86 for ERA5, but smaller R² values are found for satellite data sets. Global yearly climatological totals of water cycle components (E, P, E−P, and net transport from ocean to land and vice versa) calculated from the data sets used in this study are in agreement with previous studies, with ERA5 E and P occupying the upper part of the range. Over ocean, both the spread among satellite-based E and the difference between two satellite-based P data sets are greater than E−P, and these remain the largest sources of uncertainty within the observed global water budget. We conclude that, for a better understanding of the global water budget, the quality of E and P data sets needs to be improved, and the uncertainties more rigorously quantified.