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Since 2011, the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to (i) ensure full exploitation and (ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of total column water vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude–latitude grid of 2° × 2° and on monthly time steps between January 1979 and December 2019. We first analysed all records for a "common" short period of 5 years (2005–2009), focusing on variability (spatial and seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg m -2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this "long period", key results show (i) trends between - 1.18 ± 0.68 to 3.82 ± 3.94 kg m -2 per decade and - 0.39 ± 0.27 to 1.24 ± 0.85 kg m -2 per decade were found over ice-free global oceans and land surfaces, respectively, and (ii) regression coefficients of TCWV against surface temperatures of 6.17 ± 0.24 to 27.02 ± 0.51 % K -1 over oceans (using sea surface temperature) and 3.00 ± 0.17 to 7.77 ± 0.16 % K -1 over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the long period, highlighting regional areas of significant positive and negative correlation and the level of agreement among records. [ABSTRACT FROM AUTHOR]

Satellite observations of column‐averaged water isotopes are relatively new retrieval products that are in need of further in situ evaluation. Such evaluation studies are generally difficult to perform due to the wide mismatch in temporal and spatial scales between the satellite observations based on instantaneous pixel averages during an overpass and airborne in situ measurements ranging up to several hours over a km‐scale. In addition, topography, weather conditions and in particular cloudiness impose severe constraints on an exact collocation between satellite and airborne in situ measurement platforms. Here we present a new method that allows a comparison between in situ measurements and satellite observations of δD on a broader statistical basis. We use regional isotope‐enabled model simulations as intermediate information to identify the area for best comparisons. Applying our methodology to TROPOMI total column δD retrievals for the L‐WAIVE campaign in Annecy, France, during June 2019 increases the number of satellite pixels for comparison despite widespread cloudiness on average by a factor of 20. In addition, the comparison of simulated and observed δD revealed a dependency of the satellite evaluation on the structure of the middle and upper troposphere. We conclude that our method provides a more robust statistic basis for in situ evaluation of δD satellite retrievals. The method will thus be useful in planning and executing forthcoming validation and evaluation campaigns, and can potentially be used for the evaluation of other satellite products. Plain Language Summary: A characteristic of atmospheric water vapor is the concentration of stable heavy hydrogen and oxygen isotopes. Isotopic concentrations are observed by diverse techniques such as remote sensing from satellites and land‐based instruments, and direct measurements from aircrafts, ships and stations on land. These measurements lead to a variety of data sets which span different distances (from a few to hundreds of km) and time periods (from seconds to days). While remote measurements provide data of a large spatial and temporal coverage at a coarse resolution, direct measurements are often obtained during research campaigns over a limited time period with high spatial and temporal resolution. Especially the later data sets are of high value as they describe the atmospheric state in high detail. In this study we develop a method to extrapolate direct, campaign based high‐quality measurements of water vapor to the largest possible representative area using output from a numerical weather simulation. This allows us to compare remotely sensed and direct isotope measurements. This comparison illustrates how to interpret total column measurement and to identify where the model has difficulties to correctly simulate the vertical isotope distribution. This knowledge is of use for future application of remotely sensed data sets and model development. Key Points: New, expanded collocation criterion provides larger data sets for comparison of δD observations during a field campaignWe evaluate TROPOMI δD column retrievals with in situ airborne measurements using a COSMOiso‐based collocation criterionThe combination of subcolumn and total column averaged δD is used to characterize vertical δD gradients and reveal model biases [ABSTRACT FROM AUTHOR]

Since 2011 the Global Energy and Water cycle Exchanges (GEWEX) Water Vapor Assessment (G-VAP) has provided performance analyses for state-of-the-art reanalysis and satellite water vapour products to the GEWEX Data and Analysis Panel (GDAP) and the user community in general. A significant component of the work undertaken by G-VAP is to characterise the quality and uncertainty of these water vapour records to; i) ensure full exploitation and ii) avoid incorrect use or interpretation of results. This study presents results from the second phase of G-VAP, where we have extended and expanded our analysis of Total Column Water Vapour (TCWV) from phase 1, in conjunction with updating the G-VAP archive. For version 2 of the archive, we consider 28 freely available and mature satellite and reanalysis data products, remapped to a regular longitude-latitude grid of 2°× 2°, and on monthly time steps between January 1979 and December 2019. We first analysed all records for a 'common' short period of five years (2005–2009), focusing on variability (spatial & seasonal) and deviation from the ensemble mean. We observed that clear-sky daytime-only satellite products were generally drier than the ensemble mean, and seasonal variability/disparity in several regions up to 12 kg/m2 related to original spatial resolution and temporal sampling. For 11 of the 28 data records, further analysis was undertaken between 1988–2014. Within this 'long period', key results show i) trends between -1.18±0.68 to 3.82±3.94 kg/m2/decade and -0.39±0.27 to 1.24±0.85 kg/m2/decade were found over ice-free global oceans and land surfaces respectively, and ii) regression coefficients of TWCV against surface temperatures of 6.17±0.24 to 27.02±0.51 %/K over oceans (using sea surface temperature) and 3.00±0.17 to 7.77±0.16 %/K over land (using surface air temperature). It is important to note that trends estimated within G-VAP are used to identify issues in the data records rather than analyse climate change. Additionally, breakpoints have been identified and characterised for both land and ocean surfaces within this period. Finally, we present a spatial analysis of correlations to six climate indices within the "long period", highlighting regional areas of significant positive and negative correlation and the level of agreement among records. [ABSTRACT FROM AUTHOR]

Since 2007, the Meteorological Operational satellite (MetOp) series of platforms operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) has provided valuable observations of the Earth's surface and atmosphere for meteorological and climate applications. With 15 years of data already collected, the next generation of MetOp satellites will see this measurement record extend to and beyond 2045. Although a primary role is in operational meteorology, tropospheric temperature and water vapour profiles will be key data products produced using infrared and microwave sounding instruments on board. Considering the MetOp data record that will span 40 years, these profiles will form an essential climate data record (CDR) for studying long-term atmospheric changes. Therefore, the performance of these products must be characterized to support the robustness of any current or future analysis. In this study, we validate 9.5 years of profile data produced using the Infrared and Microwave Sounding (IMS) scheme with the European Space Agency (ESA) Water Vapour Climate Change Initiative (WV_cci) project against radiosondes from two different archives. The Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) and Analyzed RadioSoundings Archive (ARSA) data records were chosen for the validation exercise to provide the contrast between global observations (ARSA) with sparser characterized climate measurements (GRUAN). Results from this study show that IMS temperature and water vapour profile biases are within 0.5 K and 10 % of the reference for "global" scales. We further demonstrate the difference between diurnal sampling and cloud amount match-ups on observed biases and discuss the implications that sampling also plays on attributing these effects. Finally, we present the first look at the profile bias stability from the IMS product, where we observe global stabilities ranging from - 0.32 ± 0.18 to 0.1 ± 0.27 K per decade and - 1.76 ± 0.19 to 0.79 ± 0.83 % ppmv (parts per million by volume) per decade for temperature and water vapour profiles, respectively. We further break down the profile stability into diurnal and latitudinal values and relate all observed results to required climate performance. Overall, we find the results from this study demonstrate the real potential for tropospheric water vapour and temperature profile CDRs from the MetOp series of platforms. [ABSTRACT FROM AUTHOR]

Since 2007, the Meteorological Operational satellite (Metop) series of platforms operated by the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) have provided valuable observations of the Earth's surface and atmosphere for meteorological and climate applications. With 15 years of data already collected, the next generation of Metop satellites will see this measurement record extend to and beyond 2045. With a primary role in operational meteorology, tropospheric temperature and water vapour profiles will be key data products produced using infrared and microwave-sounding instruments onboard. Considering the Metop data record that will span 40 years, these profiles will form an essential climate data record (CDR) for studying long-term atmospheric changes. Therefore, the performance of these products must be characterised to support the robustness of any current or future analysis. In this study, we validate 9.5 years of profile data produced using the Infrared and Microwave Sounding (IMS) scheme with the European Space Agency (ESA) Water Vapour Climate Change Initiative (WV_cci) against radiosondes from two different archives. The Global Climate Observing System (GCOS) Reference Upper-Air Network (GRUAN) and Analysed RadioSoundings Archive (ARSA) data records were chosen for the validation exercise to provide the contrast between global observations (ARSA) with sparser characterised climate measurements (GRUAN). Results from this study show that IMS temperature and water vapour profile biases are within 1 K and 10 % of the reference for 'global' scales. We further demonstrate the difference day/night and cloud amount match-ups have on observed biases and discuss the implications sampling also plays on attributing these effects. Finally, we present a first look at the profile bias stability of the IMS product, relating trends in the bias to required climate performance. Overall, we find the results from this study demonstrate the real potential for tropospheric water vapour and temperature profile CDRs from the Metop series of platforms. [ABSTRACT FROM AUTHOR]

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. Plain Language Summary: Evaporated water becomes a gas (water vapor) in the air where it traps heat by absorbing thermal infrared radiative energy as well as sunlight. Water vapor is also the "fuel" for rain and snowfall. As the climate warms, water vapor increases in the lowest few kilometers of the atmosphere, therefore, causes greater trapping of heat but also heavier precipitation events. This study looks at how water vapor has changed since 1979 by examining satellite measurements, observations at ground level and complex computer simulations that are also used to make predictions of future climate change. We find that the total water vapor in the atmosphere is increasing by about 1% every 10 years. Changes calculated as a percentage of the initial amount are larger higher up in the atmosphere, which is consistent with simple physics. There are some differences between the observations and simulations: some simulations overestimate the observed changes and this is partly because natural fluctuations in the ocean temporarily slowed the warming over the period studied (1988–2014). It is not known for sure what other differences between observations are caused by but it seems possible that decreases in humidity in some data sets may not be real and we are confident that water vapor is increasing with warming of climate. Key Points: Tropospheric water vapor increased globally since 1979 in observations, reanalyses, and CMIP6 simulationsInternal climate variability suppressed water vapor increases during 1988–2014Observations‐based data and simulations display consistent representations of moisture variability but not long term trends [ABSTRACT FROM AUTHOR]

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 mmd-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 Eocean-∇⋅(vq)ocean with Pocean 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. [ABSTRACT FROM AUTHOR]

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 three 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 inter-annual 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/day and that there is good agreement between land and ocean mean E-P, vertically integrated moisture 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 (over-ocean VIMD, i.e., ∇Qocean) is taken into account. Correlation of Eocean - ∇Qocean with Pocean yields R2 = 0.86 for ERA5, but smaller R2 are found for satellite data sets. Climatological global yearly 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 are 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 themselves and their associated uncertainties need to be further investigated. [ABSTRACT FROM AUTHOR]

The tropospheric water vapor profile record from the Atmospheric Infrared Sounder (AIRS) now spans over a decade, making it a valuable resource for climate studies. To be considered as a Climate Data Record, it is key that the ultimate performance of these observations are understood. The Global Energy and Water cycle Exchanges (GEWEX) water vapor assessment has been tasked with characterizing the current state of the art in water vapor products currently available for climate analysis. Within the scope of this exercise, water vapor profiles from AIRS have been assessed using collocated characterized in situ measurements of tropospheric water vapor between 2007 and 2012. We first show how previously published methods for correcting radiosondes can be applied to global records, which show high correlations to Global Climate Observing System Reference Upper‐Air Network (GRUAN) performance at pressures between the surface and 250 hPa. We go further and show the first comparison of uncertainties from both the newly created Characterized Radiosonde Measurement (CRM) and GRUAN data sets. Global estimates of AIRS water vapor profile (wet/dry) biases relative to GRUAN and CRM are within 6 ± 0.3% ppmv and 15 ± 0.1% ppmv below 300 hPa, respectively. The CRM record allows latitudinal analysis for the first time, which when examined shows sensitivity to changes in absolute concentration due to large‐scale circulation in the International Tropical Convergence Zone. This paper advances the use of state‐of‐the‐art in situ records for characterizing absolute performance, recognizing that long‐term stability needs further research. Key Points: Altitudes below 250‐hPa corrected operational radiosondes show similar uncertainty performance to GRUANAIRS tropospheric water vapor biases relative to GRUAN are within 6 ± 0.3% ppmv below 300 hPaEstimated collocation uncertainty in the Northern Hemisphere shown to reduce to below 1% ppmv for calculated yearly biases [ABSTRACT FROM AUTHOR]

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 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 seven specific humidity and seven temperature data records covering the period 1988-2009. The G-VAP data archive is referenced under the following digital object identifier (doi): doi:10.5676/EUM_SAF_CM/GVAP/V001. Within G-VAP, the characterisation 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 on systematic differences in TCWV between the classes. [ABSTRACT FROM AUTHOR]

Radiosonde soundings from the GRUAN data record are shown to be consistent with IASI measured radiances via the LBLRTM radiative transfer model in the part of the spectrum that is mostly affected by water vapour absorption in the upper troposphere (from 700 hPa up). This result is key to have consistency between radiosonde and satellite measurements for climate data records, since GRUAN, IASI and LBLRTM constitute reference measurements in each of their fields. This is specially the case for night time radiosonde measurements. Although the sample size is small (16 cases), day time GRUAN radiosonde measurements seem to have a small dry bias of 2.5% in absolute terms of relative humidity, located mainly in the upper troposphere, with respect to LBLRTM and IASI. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

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. [ABSTRACT FROM AUTHOR]

Water vapour is a key greenhouse gas in the Earth climate system. In this golden age of satellite remote sensing, global observations of water vapour fields are made from numerous instruments measuring in the ultraviolet/visible, through the infrared bands, to the microwave regions of the electromagnetic spectrum. While these observations provide a wealth of information on columnar, free-tropospheric and upper troposphere/lower stratosphere water vapour amounts, there is still an observational gap regarding resolved bulk planetary boundary layer (PBL) concentrations. In this study we demonstrate the ability of the Greenhouse Gases Observing SATellite (GOSAT) to bridge this gap from highly resolved measurements in the shortwave infrared (SWIR). These new measurements of near surface columnar water vapour are free of topographic artefacts and are interpreted as a proxy for bulk PBL water vapour. Validation (over land surfaces only) of this new data set against global radiosondes show low biases that vary seasonally between −2% to 5%. Analysis on broad latitudinal bands show biases between −3% and 2% moving from high latitudes to the equatorial regions. Finally, with the extension of the GOSAT program out to at least 2027, we discuss the potential for a new GOSAT PBL water vapour Climate Data Record (CDR). [ABSTRACT FROM AUTHOR]