Column Water Vapor Research Articles

Evaluating Tropical Precipitation Relations in CMIP6 Models with ARM Data

Abstract A set of diagnostics based on simple, statistical relationships between precipitation and the thermodynamic environment in observations is implemented to assess phase 6 of the Coupled Model Intercomparison Project (CMIP6) model behavior with respect to precipitation. Observational data from the Atmospheric Radiation Measurement (ARM) permanent field observational sites are augmented with satellite observations of precipitation and temperature as an observational baseline. A robust relationship across observational datasets between column water vapor (CWV) and precipitation, in which conditionally averaged precipitation exhibits a sharp pickup at some critical CWV value, provides a useful convective onset diagnostic for climate model comparison. While a few models reproduce an appropriate precipitation pickup, most models begin their pickup at too low CWV and the increase in precipitation with increasing CWV is too weak. Convective transition statistics compiled in column relative humidity (CRH) partially compensate for model temperature biases—although imperfectly since the temperature dependence is more complex than that of column saturation. Significant errors remain in individual models and weak pickups are generally not improved. The conditional-average precipitation as a function of CRH can be decomposed into the product of the probability of raining and mean precipitation during raining times (conditional intensity). The pickup behavior is primarily dependent on the probability of raining near the transition and on the conditional intensity at higher CRH. Most models roughly capture the CRH dependence of these two factors. However, compensating biases often occur: model conditional intensity that is too low at a given CRH is compensated in part by excessive probability of precipitation.

Evaluation of tropical water vapour from CMIP6 global climate models using the ESA CCI Water Vapour climate data records

Abstract. The tropospheric water vapour data record generated within the ESA Climate Change Initiative Water Vapour project (ESA TCWV-COMBI) is used to evaluate the interannual variability of global climate models (CMIP6 framework under AMIP scenarios) and reanalysis (ECMWF ERA5). The study focuses on the tropical belt, with a separation of oceanic and continental situations. The intercomparison is performed according to the probability density function (PDF) of the total column water vapour (TCWV) defined yearly from the daily scale, as well as its evolution with respect to large-scale overturning circulation. The observational diagnostic relies on the decomposition of the tropical atmosphere into percentile of the PDF and into dynamical regimes defined from the atmospheric vertical velocity. Large variations are observed in the patterns among the data records over tropical land, while oceanic situations show more similarities in both interannual variations and percentile extremes. The signatures of El Niño and La Niña events, driven by sea surface temperatures, are obvious over the oceans. Differences also occur over land for both trends (a strong moistening is observed in the ESA TCWV-COMBI data record, which is absent in CMIP6 models and ERA5) and extreme years. The discrepancies are probably associated with the scene selection applied in the data process. Since the results are sensitive to the scene selection applied in the data process, discrepancies are observed among the datasets. Therefore, the normalization process is employed to analyse the time evolution with respect to the mean state. Other sources of differences, linked to the models and their parametrizations, are highlighted.

Seasonal heavy precipitation sensitivity to moisture corrections in the western Mediterranean across resolutions

The controlling role of atmospheric water vapour for heavy precipitation leading to extreme events has been widely demonstrated, along with the existing gap of adequate moisture observations and the frequent biases present in model simulations concerning this fundamental variable. In this study, we profit from a state-of-the-art dense network of GPS measurements over Europe retrieving a homogenized GPS-derived Zenith Total Delay (GPS-ZTD) data set up to 10 min of temporal resolution, to assess the seasonal sensitivity of convection-related processes and heavy precipitation modelling to atmospheric humidity corrections. For this purpose, we perform nudging experiments with the COSMO-CLM model at two spatial resolutions, 7 km (parameterized convection) and 2.8 km (explicitly resolved convection) covering the autumn period of 2012, when the Special Observation Period (SOP) 1 of the Hydrological Cycle in the Mediterranean Experiment (HyMeX) program took place in the Western Mediterranean, which is our area of interest.The benefits and disadvantages of GPS-ZTD nudging and resulting moisture corrections are disentangled. The impact on high-resolution parameterized versus convection-permitting simulations is compared. A process-understanding methodology and a local-to-regional approach are used. Our results show a beneficial impact on the seasonal scale at both model grid spacings improving the representation of the chain of processes leading to heavy precipitation, contrary to the non-systematic improvement at the event and sub-event scales. The correction of atmospheric moisture entails a reduction of about 10% in the total column water vapour and corrections on single locations up to 10 mm counteracting the model wet bias across scales. The location, structure, and amount of total precipitation are positively affected. Particularly, the combination of high-resolution atmospheric humidity observations and fine convection-permitting simulations shows great potential for correction of the precipitation daily cycle, key for accurate precipitation modelling. The difference in the density of local and upstream observational networks and the lack of information on the vertical stratification of moisture are identified weaknesses, which could be determinants in obtaining more accurate corrections on seasonal to sub-seasonal scales after assimilation strategies.

Analysis of the main source regions of moisture transport events with the new ESA CCI/CM‐SAF total column water vapour climate data record (v2)

AbstractThis study makes use of the new total column water vapour data record (CDR‐2 (v2)), developed by the European Space Agency (ESA) in coordination with the Satellite Application Facility on Climate Monitoring (CM SAF), to analyse the adequacy of the integrated vertical water vapour column (IWV) data provided by the European Centre for Medium‐Range Weather Forecasts (ECMWF) ERA5 and ERA‐Interim reanalyses in regions of critical interest for moisture transport mechanisms. This information is critical for the initialization of moisture transport models—both Eulerian and Lagrangian—used to study the main mechanisms and predict the future evolution of moisture transport events. In particular, almost 40,000 atmospheric river (AR) and nocturnal low‐level jet (NLLJ) events identified on a global scale between 2002 and 2017 have been used to study the variability between the cited reanalyses and CDR‐2, in terms of both bias in the observed values of IWV during each particular event and daily temporal correlation fields. Although some notable discrepancies are reported in the main tropical rainforest regions, it is observed that, in regions of high interest for both ARs and NLLJs, the degree of agreement between the reanalyses and CDR‐2 is high. The bias observed in the regions of interest is generally low, and the temporal correlation in the IWV fields is above 0.8 in most areas. ERA5 appears to show slightly better performance than ERA‐Interim when resolving the moisture column, and both show greater similarity to CDR‐2 in the midlatitudes compared with tropical regions. The probability density functions constructed on an event‐to‐event basis reinforce these ideas. We conclude that the evaluations presented here using CDR‐2 serve to strengthen avaliable evidence that the ECMWF reanalyses can safely be used in the initializations of Lagrangian dispersion models and Eulerian moisture tracer simulations—commonly used for the analysis of main advection mechanisms—in the vast majority of regions critical to the study of ARs and LLJs. They can also safely be used for the detection of moisture source–sink regions in the study of the global hydrological cycle in these regions.

Significant increase in water vapour over India and Indian Ocean: Implications for tropospheric warming and regional climate forcing.

The increase in greenhouse gases (GHGs) due to anthropogenic activities enhances regional and global temperatures. The most abundant GHG, i.e., water vapour, has a vital positive feedback on the global warming and Earth's climate system. This study focuses on the spatial and temporal changes in water vapour in the troposphere over India and Indian Ocean as derived from the ground-based, satellite and reanalyses data, and assesses the impact on water vapour changes on the regional climate by analysing radiative effects. The analyses show that the annual mean column water vapour (CWV) is high over the northern Indian Ocean, Bay of Bengal and Peninsular India, ranging from 30 to 60 kg/m2. Most regions show significant positive trends in the annual mean CWV, about 0.1-0.2 kg/m2/yr. There is a significant positive trend in water vapour in the troposphere (except 200 hPa) over the India land regions, with the highest values at 1000 hPa (0.034 g/kg/yr). The corresponding water vapour radiative effect (WVRE) is about 20-80 W/m2, depending on seasons and regions. This study, therefore, indicates that the increase in tropospheric water vapour over India and Indian Ocean could affect the regional temperature and climate.

Analysis of global trends of total column water vapour from multiple years of OMI observations

Abstract. In this study, we investigate trends in total column water vapour (TCWV) retrieved from measurements of the Ozone Monitoring Instrument (OMI) for the time range between January 2005 to December 2020. The trend analysis reveals, on global average, an annual increase in the TCWV amount of approximately +0.054 kg m−2 yr−1 or +0.21 % yr−1. After the application of a Z test (to the significance level of 5 %) and a false discovery rate (FDR) test to the results of the trend analysis, mainly positive trends remain, in particular over the northern subtropics in the eastern Pacific. Combining the relative TCWV trends with trends in air temperature, we also analyse trends in relative humidity (RH) on the local scale. This analysis reveals that the assumption of temporally invariant RH is not always fulfilled, as we obtain increasing and decreasing RH trends over large areas of the ocean and land surface and also observe that these trends are not limited to arid and humid regions, respectively. For instance, we find decreasing RH trends over the (humid) tropical Pacific Ocean in the region of the Intertropical Convergence Zone. Interestingly, these decreasing RH trends in the tropical Pacific Ocean coincide well with decreasing trends in precipitation. Moreover, by combining the trends of TCWV, surface temperature, and precipitation, we derive trends for the global water vapour turnover time (TUT) of approximately +0.02 d yr−1. Also, we obtain a TUT rate of change of around 8.4 % K−1, which is 2 to 3 times higher than the values obtained in previous studies.

Columnar aerosol optical depth, water vapor and ozone over a semi-arid urban location of western India: Potential sources and direct radiative effects

Continuous measurements of Aerosol Optical Depth (AOD), columnar water vapor (WV) and Ozone (O3) concentrations were made over a semi-arid urban location Rajkot (22.29°N, 70.74°E, and 142 m AMSL) in the western India. The annual mean values of AOD, WV and O3 were 0.37 ± 0.15, 1.44 ± 0.6 gm cm−2 and 243 ± 18 DU during 2016–2019. AOD showed large seasonal variability mostly dominated by abundant coarse mode aerosols (Angstrom Exponent, AE < 0.65 and AOD ∼ 0.37) during pre-monsoon and monsoon, mainly caused by wind-blown and transported dust from nearby Thar and Kutch desert. Mixture of fine and coarse mode aerosols were abundant (AE = 1.13 ± 0.26) during post-monsoon influenced by both anthropogenic and natural sources. Columnar WV were also higher in pre-monsoon and monsoon seasons. The O3 concentrations is higher during pre-monsoon (247 DU) and lower during monsoon (230 DU). The vertical distribution of aerosol extinction coefficient derived from satellite (CALIPSO) based observations revealed the dominance of dust aerosols (∼74 %) in pre-monsoon; smoke and polluted dust in winter and post monsoon period. The trajectory clusters and concentration weighted trajectory maps also indicated the combined effect of anthropogenic (especially from the northern part of India) and natural aerosol sources during the post-monsoon period. Associated with the higher anthropogenic components during post-monsoon season, aerosol direct radiative forcing depicted surface dimming as high as − 48.7 ± 12.6 Wm−2, resulting in atmospheric forcing of 42.6 ± 12.5 Wm−2.

Advances in Land–Ocean Heat Fluxes Using Remote Sensing

Advanced remote sensing technology has provided spatially distributed variables for estimating land–ocean heat fluxes, allowing for practical applications in drought monitoring, water resources management, and climate assessment. This Special Issue includes several research studies using state-of-the-art algorithms for estimating downward longwave radiation, surface net radiation, latent heat flux, columnar atmospheric water vapor, fractional vegetation cover, and grassland aboveground biomass. This Special Issue intends to help scientists involved in global change research and practices better comprehend the strengths and disadvantages of the application of remote sensing for monitoring surface energy, water, and carbon budgets. The studies published in this Special Issue can be applied by natural resource management communities to enhance the characterization and assessment of land–ocean biophysical variables, as well as for more accurately partitioning heat flux into soil and vegetation based on the existing and forthcoming remote sensing data.

The annual cycle of water vapor above gale crater as retrieved by CRISM and compared to ChemCam passive sky spectroscopy

Using spectra returned from the Compact Reconnaissance Imaging Spectrometer for Mars (CRISM) aboard the Mars Reconnaissance Orbiter (MRO), we report atmospheric water vapor column abundances at a spatial resolution of ~ 1 km from observations taken over Gale crater, the landing site of the Mars Science Laboratory (MSL). The observations span a little more than 3 Mars years (MY) from MY 28 at Ls = 116° (October 04, 2006) to MY 31 at Ls = 101° (April 24, 2012). The retrievals were performed using an improved algorithm to correct for the changes in wavelength in the cross-track direction of CRISM images, also known as the “spectral smile”, allowing for a larger fraction of area from each observation to be covered. The water vapor column abundances are retrieved using the strong absorption band of H2O centered at 2600 nm. The seasonal cycle of water vapor above Gale is compared against that from reprocessed and new MSL Curiosity rover ChemCam passive sky observations using updated gas absorption parameters and covering the period between MY 31 at Ls = 291° (March 30, 2013) and MY 35 at Ls = 42° (June 20, 2019). The retrieved annual variation of water vapor from CRISM and ChemCam both display a similar trend with respect to the timing of the maximum and minimum in water abundance, with only minor differences caused by the interannual differences in water vapor and the different retrieval approaches between both instruments, addressing a previously existing discrepancy between ChemCam and CRISM water vapor column abundances.

Diagnosing Instantaneous Forcing and Feedbacks of Downwelling Longwave Radiation at the Surface: A Simple Methodology and Its Application to CMIP5 Models

Abstract Climate models predict large increases in downwelling longwave radiation (DLR) at Earth’s surface as atmospheric CO2 concentrations increase. Here we introduce a novel methodology that allows these increases to be decomposed into direct radiative forcing due to enhanced CO2 and feedbacks due to subsequent changes in atmospheric properties. For the first time, we develop explicit analytic expressions for the radiative forcing and feedbacks, which are calculable from time-mean fields of near-surface air temperature, specific humidity, pressure, total column water vapor, and total cloud fraction. Our methodology captures 90%–98% of the variance in changes in clear-sky and all-sky DLR in five CMIP5 models, with a typical error of less than 10%. The longwave feedbacks are decomposed into contributions from changes in temperature, specific humidity, water vapor height scale, and cloud fraction. We show that changes in specific humidity and height scale are closely linked to changes in near-surface air temperature and therefore, in the global average, that 90% of the increase in all-sky DLR may be attributed to a feedback from increasing near-surface air temperature. Mean-state clouds play a major role in changes in DLR by masking the clear-sky longwave and enhancing the temperature feedback via increased blackbody radiation. The impact of changes in cloud cover (the cloud feedback) on the DLR is small (∼2%) in the global average, but significant in particular geographical regions.

Satellite measurements of peroxyacetyl nitrate from the Cross-Track Infrared Sounder: comparison with ATom aircraft measurements

Abstract. We present an overview of an optimal estimation algorithm to retrieve peroxyacetyl nitrate (PAN) from single-field-of-view Level 1B radiances measured by the Cross-Track Infrared Sounder (CrIS). CrIS PAN retrievals show peak sensitivity in the mid-troposphere, with degrees of freedom for signal less than or equal to 1.0. We show comparisons with two sets of aircraft measurements from the Atmospheric Tomography Mission (ATom), the PAN and Trace Hydrohalocarbon ExpeRiment (PANTHER) and the Georgia Tech chemical ionization mass spectrometer (GT-CIMS). We find a systematic difference between the two aircraft datasets, with vertically averaged mid-tropospheric values from the GT-CIMS around 14 % lower than equivalent values from PANTHER. However, the two sets of aircraft measurements are strongly correlated (R2 value of 0.92) and do provide a consistent view of the large-scale variation of PAN. We demonstrate that the retrievals of PAN from CrIS show skill in measurement of these large-scale PAN distributions in the remote mid-troposphere compared to the retrieval prior. The standard deviation of individual CrIS–aircraft differences is 0.08 ppbv, which we take as an estimate of the uncertainty of the CrIS mid-tropospheric PAN for a single satellite field of view. The standard deviation of the CrIS–aircraft comparisons for averaged CrIS retrievals (median of 20 satellite coincidences with each aircraft profile) is lower at 0.05 ppbv. This would suggest that the retrieval error is reduced with averaging, although not with the square root of the number of observations. We find a negative bias of the order of 0.1 ppbv in the CrIS PAN results with respect to the aircraft measurements. This bias shows a dependence on column water vapor. We provide a water-vapor-dependent bias correction for use with the CrIS PAN data.

Global Tropical Precipitation Relationships to Free-Tropospheric Water Vapor Using Radio Occultations

Abstract The transition to deep convection and associated precipitation is often studied in relationship to the associated column water vapor owing to the wide availability of these data from various ground or satellite-based products. Based on radiosonde and ground-based global navigation satellite system (GNSS) data examined at limited locations and model comparison studies, water vapor at different vertical levels is conjectured to have different relationships to convective intensity. Here, the relationship between precipitation and water vapor in different free-tropospheric layers is investigated using globally distributed GNSS radio occultation (RO) temperature and moisture profiles collocated with GPM IMERG precipitation across the tropical latitudes. A key feature of the RO measurement is its ability to directly sense in and near regions of heavy precipitation and clouds. Sharp pickups (i.e., sudden increases) of conditionally averaged precipitation as a function of water vapor in different tropospheric layers are noted for a variety of tropical ocean and land regions. The layer-integrated water vapor value at which this pickup occurs has a dependence on temperature that is more complex than constant RH, with larger subsaturation at warmer temperatures. These relationships of precipitation to its thermodynamic environment for different layers can provide a baseline for comparison with climate model simulations of the convective onset. Furthermore, vertical profiles before, during, and after convection are consistent with the hypothesis that the lower troposphere plays a causal role in the onset of convection, while the upper troposphere is moistened by detrainment from convection.

Impact of thresholds on nonstationary frequency analyses of peak over threshold extreme rainfall series in Pearl River Basin, China

Nonstationary frequency analysis of peak over threshold (POT) extreme rainfall series is crucial in hydrology. Most previous studies on the nonstationary frequency analyses of POT extreme rainfall series used only a single threshold, ignoring the differences in the statistical characteristics of POT extreme rainfall series extracted with different thresholds. This study investigated the impact of thresholds on the nonstationary frequency analyses of POT extreme rainfall series using a three-step method. First, the non-stationarities in POT extreme rainfall series extracted with different thresholds were assessed using the Iterative Mann-Kendall test and the Mood test. Second, the nonstationary POT extreme rainfall series were modeled using the generalized Pareto distribution (GPD) with the scale parameter to be linked with physical covariates. Third, the uncertainty in the parameters and return level of the optimal model for the POT extreme rainfall series were evaluated using the Markov chain Monte Carlo method. This method was applied to the daily rainfall at 48 stations in the Pearl River Basin (PRB) from 1979 to 2020. By comparing the optimal models of POT extreme rainfall series extracted with different percentile thresholds, we can draw the following conclusions. (1) As the threshold increases from the 90th percentile to the 99.7th percentile, the percentage of nonstationary POT extreme rainfall series gradually decreases from 92% to 13%, with most stations with a significant increasing trend changing to most stations with a significant decreasing trend, especially when the threshold is greater than the 98th percentile. (2) The uncertainty in the scale parameter, shape parameter, and return level increases as the threshold increases, and increases significantly when the threshold is greater than the 98th percentile, especially for the scale and shape parameters. (3) The 98th percentile is suggested as the optimal threshold for the PRB; (4) For the 98th percentile threshold, the total column water vapor in the convective indices is the most significant covariate for the entire PRB.

Spatiotemporal Modes Characteristics and SARIMA Prediction of Total Column Water Vapor over China during 2002–2022 Based on AIRS Dataset

The total column water vapor (TCWV) is a relatively active component in the atmosphere and an important detection object of climate change. Exploring the spatiotemporal modes characteristics of TCWV and predicting its changing trends can provide a reference for human beings to deal with climate change and formulate corresponding countermeasures. The TCWV data over China region by using the Atmospheric Infrared Sounder (AIRS) dataset from 2002 to 2022 were obtained. The empirical orthogonal function (EOF) analysis, linear regression, Mann-Kendall (M-K) mutation test, Seasonal Autoregressive Integrated Moving Average (SARIMA) model and other methods were used to discuss the spatiotemporal modes characteristics of TCWV in the China region on the monthly, seasonal, and annual scales and verify the rationality of the forecast of the monthly average trend of TCWV in the next year. The obtained results show that: (1) The annual and seasonal scales spatial distributions of TCWV in China are roughly consistent, with obvious latitudinal distribution characteristics. That is, the TCWV in the low latitude region, especially in the tropical region, is larger, and it gradually decreases with the increase of the latitude. Furthermore, the TCWV in the eastern region is higher than that in the western region at the same latitude; (2) The EOF analysis results show that its first mode can better reflect the typical distribution characteristics of the southeast-northwest positive distribution in China; (3) From 2002 to 2022, the TCWV in China shows an upward trend and the TCWV increases at a rate of 0.0413 kg/m2 per year, which may be related to the long-term increase of air temperature in recent years; (4) The inter-monthly variation of TCWV shows a slightly positive skewed ‘bell-shaped’ curve, with the maximum in summer, the minimum in winter and the similar distribution in spring and autumn. As can be seen from the M-K curves of the four seasons, each season has different mutation points; (5) Forecasting the TCWV was done using time series monthly average values from September 2002 to February 2022. SARIMA (3, 1, 3) × (0, 1, 1, 12) was identified as the best model. This model passed the residual normality test and the forecasting evaluation statistics show that MAPE = 2.65%, MSE = 0.3229 and the R2-score = 0.9949. As demonstrated by the results, the SARIMA model is a good model for forecasting TCWV in the China region.

Relative Merits of Optimal Estimation and Non-Linear Retrievals of Sea-Surface Temperature from MODIS

We compared the results of an Optimal Estimation (OE) based approach for the retrieval of the skin sea surface temperature (SSTskin) with those of the traditional non-linear sea surface temperature (NLSST) algorithm. The retrievals were from radiance measurements in two infrared channels of the Moderate Resolution Imaging Spectroradiometer (MODIS) on the NASA satellite Aqua. The OE used a reduced state vector of SST and total column water vapor (TCWV). The SST and atmospheric profiles of temperature and humidity from ERA5 provided prior knowledge, and we made reasonable assumptions about the variance of these fields. An atmospheric radiative transfer model was used as the forward model to simulate the MODIS measurements. The performances of the retrieval approaches were assessed by comparison with in situ measurements. We found that the OESST reduces the satellite–in situ bias, but mostly for retrievals with an already small bias between in situ and the prior SST. The OE approach generally fails to improve the SST retrieval when that difference is large. In such cases, the NLSST often provides a better estimate of the SST than the OE. The OESST also underperforms NLSST in areas that include large horizontal SST gradients.

Recent rainfall conditions in the Congo Basin

In the Congo Basin, a drying trend in the April–May–June rains prevailed between 1979 and 2014, accompanied by a decline in forest productivity. This article examines the subsequent years, in order to determine whether rainfall conditions have improved and to examine meteorological factors governing conditions in those years. It is shown that a wetter period, comparable to that of 1979–1993, spanned the years 2016–2020. However, the meteorological factors responsible for the wetter conditions appear to be significantly different from those related to the earlier wet period. The wetter conditions of 1979–1993 were associated with changes in the tropical Walker circulation, in moisture flux and flux divergence, and in Pacific sea-surface temperatures (SST), namely a warmer central and eastern Pacific and a cooler western Pacific, compared to the dry phase in 2000–2014. This resulted in a lower-than-average trans-Pacific SST gradient. In contrast, SSTs were almost ubiquitously higher in the 2016–2020 period than in either prior period. However, there was some reduction in the trans-Pacific gradient. The Walker circulation and moisture flux/flux divergence were not factors in this episode. The major factors provoking the return to wetter years appear to be an increase in convective available potential energy and in total column water vapor. This could be related to the general warming of the oceans and land.

Water Vapor on Mars: A Refined Climatology and Constraints on the Near‐Surface Concentration Enabled by Synergistic Retrievals

AbstractWith the utilization of a novel synergistic approach, we constrain the vertical distribution of water vapor on Mars with measurements from nadir‐pointing instruments. Water vapor column abundances were retrieved simultaneously with PFS (sensing the thermal infrared range) and SPICAM (sensing the near‐infrared range) on Mars Express, yielding distinct yet complementary sensitivity to different parts of the atmospheric column. We show that by exploiting a spectral synergy retrieval approach, we obtain more accurate water vapor column abundances compared to when only one instrument is used, providing a new and highly robust reference climatology from Mars Express. We present a composite global data set covering all seasons and latitudes, assembled from colocated observations sampled from seven Martian years. The synergy also offers a way to study the vertical partitioning of water, which has remained out of the scope of nadir observations made by single instruments covering a single spectral interval. Special attention is given to the north polar region, with extra focus on the sublimation of the seasonal polar cap during the late spring and summer seasons. Column abundances from the Mars Climate Database were found to be significantly higher than synergistically retrieved values, especially in the summer Northern Hemisphere. Deviations between synergy and model in both magnitude and meridional variation of the vertical confinement were also discovered, suggesting that certain aspects of the transport and dynamics of water vapor are not fully captured by current models.

Integrating Reanalysis and Satellite Cloud Information to Estimate Surface Downward Long-Wave Radiation

The estimation of downward long-wave radiation (DLR) at the surface is very important for the understanding of the Earth’s radiative budget with implications in surface–atmosphere exchanges, climate variability, and global warming. Theoretical radiative transfer and observationally based studies identify the crucial role of clouds in modulating the temporal and spatial variability of DLR. In this study, a new machine learning algorithm that uses multivariate adaptive regression splines (MARS) and the combination of near-surface meteorological data with satellite cloud information is proposed. The new algorithm is compared with the current operational formulation used by the European Organization for the Exploitation of Meteorological Satellites (EUMETSAT) Satellite Application Facility on Land Surface Analysis (LSA-SAF). Both algorithms use near-surface temperature and dewpoint temperature along with total column water vapor from the latest European Centre for Medium-range Weather Forecasts (ECMWF) reanalysis ERA5 and satellite cloud information from the Meteosat Second Generation. The algorithms are trained and validated using both ECMWF-ERA5 and DLR acquired from 23 ground stations as part of the Baseline Surface Radiation Network (BSRN) and the Atmospheric Radiation Measurement (ARM) user facility. Results show that the MARS algorithm generally improves DLR estimation in comparison with other model estimates, particularly when trained with observations. When considering all the validation data, root mean square errors (RMSEs) of 18.76, 23.55, and 22.08 W·m−2 are obtained for MARS, operational LSA-SAF, and ERA5, respectively. The added value of using the satellite cloud information is accessed by comparing with estimates driven by ERA5 total cloud cover, showing an increase of 17% of the RMSE. The consistency of MARS estimate is also tested against an independent dataset of 52 ground stations (from FLUXNET2015), further supporting the good performance of the proposed model.

Amplified wintertime Barents Sea warming linked to intensified Barents oscillation

In recent decades, the Barents Sea has warmed more than twice as fast as the rest of the Arctic in winter, but the exact causes behind this amplified warming remain unclear. In this study, we quantify the wintertime Barents Sea warming (BSW, for near-surface air temperature) with an average linear trend of 1.74 °C decade−1 and an interdecadal change around 2003 based on a surface energy budget analysis using the ERA5 reanalysis dataset from 1979–2019. Our analysis suggests that the interdecadal change in the wintertime near-surface air temperature is dominated by enhanced clear-sky downward longwave radiation (CDLW) associated with increased total column water vapor. Furthermore, it is found that a mode of atmospheric variability over the North Atlantic region known as the Barents oscillation (BO) strongly contributed to the BSW with a stepwise jump in 2003. Since 2003, the BO turned into a strengthened and positive phase, characteristic of anomalous high pressure over the North Atlantic and South of the Barents Sea, which promoted two branches of heat and moisture transport from southern Greenland along the Norwegian Sea and from the Eurasian continent to the Barents Sea. This enhanced the water vapor convergence over the Barents Sea, resulting in BSW through enhanced CDLW. Our results highlight the atmospheric circulation related to the BO as an emerging driver of the wintertime BSW through enhanced meridional atmospheric heat and moisture transport over the North Atlantic Ocean.

A Deep-Neural-Network-Based Aerosol Optical Depth (AOD) Retrieval from Landsat-8 Top of Atmosphere Data

The 30 m resolution Landsat data have been used for high resolution aerosol optical depth (AOD) retrieval based on radiative transfer models. In this paper, a Landsat-8 AOD retrieval algorithm is proposed based on the deep neural network (DNN). A total of 6390 samples were obtained for model training and validation by collocating 8 years of Landsat-8 top of atmosphere (TOA) data and aerosol robotic network (AERONET) AOD data acquired from 329 AERONET stations over 30°W–160°E and 60°N–60°S. The Google Earth Engine (GEE) cloud-computing platform is used for the collocation to avoid a large download volume of Landsat data. Seventeen predictor variables were used to estimate AOD at 500 nm, including the seven bands TOA reflectance, two bands TOA brightness (BT), solar and viewing zenith and azimuth angles, scattering angle, digital elevation model (DEM), and the meteorological reanalysis total columnar water vapor and ozone concentration. The leave-one-station-out cross-validation showed that the estimated AOD agreed well with AERONET AOD with a correlation coefficient of 0.83, root-mean-square error of 0.15, and approximately 61% AOD retrievals within 0.05 + 20% of the AERONET AOD. Theoretical comparisons with the physical-based methods and the adaptation of the developed DNN method to Sentinel-2 TOA data with a different spectral band configuration are discussed.