ALBERT

Description: Shortwave (SW) fluxes estimated from broadband radiometry rely on empirically gathered and hemispherically resolved fields of outgoing top-of-atmosphere (TOA) radiances. This study aims to provide more accurate and precise fields of TOA SW radiances reflected from clouds over ocean by introducing a novel semiphysical model predicting radiances per narrow sun-observer geometry. This model was statistically trained using CERES-measured radiances paired with MODIS-retrieved cloud parameters as well as reanalysis-based geophysical parameters. By using radiative transfer approximations as a framework to ingest the above parameters, the new approach incorporates cloud-top effective radius and above-cloud water vapor in addition to traditionally used cloud optical depth, cloud fraction, cloud phase, and surface wind speed. A two-stream cloud albedo – serving to statistically incorporate cloud optical thickness and cloud-top effective radius – and Cox–Munk ocean reflectance were used to describe an albedo over each CERES footprint. Effective-radius-dependent asymmetry parameters were obtained empirically and separately for each viewing-illumination geometry. A simple equation of radiative transfer, with this albedo and attenuating above-cloud water vapor as inputs, was used in its log-linear form to allow for statistical optimization. We identified the two-stream functional form that minimized radiance residuals calculated against CERES observations and outperformed the state-of-the-art approach for most observer geometries outside the sun-glint and solar zenith angles between 20 and 70∘, reducing the median SD of radiance residuals per solar geometry by up to 13.2 % for liquid clouds, 1.9 % for ice clouds, and 35.8 % for footprints containing both cloud phases. Geometries affected by sun glint (constituting between 10 % and 1 % of the discretized upward hemisphere for solar zenith angles of 20 and 70∘, respectively), however, often showed weaker performance when handled with the new approach and had increased residuals by as much as 60 % compared to the state-of-the-art approach. Overall, uncertainties were reduced for liquid-phase and mixed-phase footprints by 5.76 % and 10.81 %, respectively, while uncertainties for ice-phase footprints increased by 0.34 %. Tested for a variety of scenes, we further demonstrated the plausibility of scene-wise predicted radiance fields. This new approach may prove useful when employed in angular distribution models and may result in improved flux estimates, in particular dealing with clouds characterized by small or large droplet/crystal sizes.

Description: The microwave radiometers (MWRs) on board the European Remote Sensing Satellites 1 and 2 (ERS-1 and ERS-2) and Envisat provide a continuous time series of brightness temperature observations between 1991 and 2012. Here we report on a new total column water vapour (TCWV) and wet tropospheric correction (WTC) dataset that builds on this time series. We use a one-dimensional variational approach to derive TCWV from MWR observations and ERA-Interim background information. A particular focus of this study lies on the intercalibration of the three different instruments, which is performed using constraints on liquid water path (LWP) and TCWV. Comparing our MWR-derived time series of TCWV against TCWV derived from Global Navigation Satellite System (GNSS) we find that the MWR-derived TCWV time series is stable over time. However, observations potentially affected by precipitation show a degraded performance compared to precipitation-free observations in terms of the accuracy of retrieved TCWV. An analysis of WTC shows further that the retrieved WTC is superior to purely ERA-Interim-derived WTC for all satellites and for the entire time series. Even compared to the European Space Agency's (ESA) operational WTC retrievals, which incorporate in addition to MWR additional observational data, the here-described dataset shows improvements in particular for the mid-latitudes and for the two earlier satellites, ERS-1 and ERS-2. The dataset is publicly available under doi:10.5676/DWD_EMIR/V001 (Bennartz et al., 2016).

Description: A long term mean flow field for the subpolar North Atlantic region with a horizontal resolution of approximately 25km is created by gridding Argo-derived velocity vectors using two different topography following interpolation schemes. The 10-d float displacements in the typical drift depths of 1000m to 1500m represent the flow in the Labrador Sea Water density range. Both mapping algorithms separate the flow field into potential vorticity (PV) conserving, i.e. topography following contribution and a deviating part, which we define as the eddy contribution. To verify the significance of the separation, we compare the mean flow and the eddy kinetic energy (EKE), derived from both mapping algorithms, with those obtained from multiyear mooring observations. The PV-conserving mean flow is characterized by stable boundary currents along all major topographic features including shelf breaks and basin-interior topographic ridges such as the Reykjanes Ridge or the Rockall Plateau. Mid-basin northward advection pathways from the northeastern Labrador Sea into the Irminger Sea and from the Mid Atlantic Ridge region into the Iceland basin are well-resolved. An eastward flow is present across the southern boundary of the subpolar gyre near 52°N, the latitude of the Charlie Gibbs Fracture Zone. The mid-depth EKE field resembles most of the satellite-derived surface EKE field. However, noticeable differences exist along the northward advection pathways in the Irminger Sea and the Iceland basin, where the deep EKE exceeds the surface EKE field. Further, the ratio between mean flow and the square root of the EKE, the Peclet Number, reveals distinct advection-dominated regions as well as basin interior regimes in which mixing is prevailing.

Description: New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude-longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named: AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. Together with the ensured spectral consistency and rigorous uncertainty propagation though all processing levels, the Cloud_cci datasets approach new benchmarks for climate data records of cloud properties based on passive imaging sensors. For each dataset a Digital Object Identifier has been issued: Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002 Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002 Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002 Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002 Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002 Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

Description: New cloud property datasets based on measurements from the passive imaging satellite sensors AVHRR, MODIS, ATSR2, AATSR and MERIS are presented. Two retrieval systems were developed that include components for cloud detection and cloud typing followed by cloud property retrievals based on the optimal estimation (OE) technique. The OE-based retrievals are applied to simultaneously retrieve cloud-top pressure, cloud particle effective radius and cloud optical thickness using measurements at visible, near-infrared and thermal infrared wavelengths, which ensures spectral consistency. The retrieved cloud properties are further processed to derive cloud-top height, cloud-top temperature, cloud liquid water path, cloud ice water path and spectral cloud albedo. The Cloud_cci products are pixel-based retrievals, daily composites of those on a global equal-angle latitude–longitude grid, and monthly cloud properties such as averages, standard deviations and histograms, also on a global grid. All products include rigorous propagation of the retrieval and sampling uncertainties. Grouping the orbital properties of the sensor families, six datasets have been defined, which are named AVHRR-AM, AVHRR-PM, MODIS-Terra, MODIS-Aqua, ATSR2-AATSR and MERIS+AATSR, each comprising a specific subset of all available sensors. The individual characteristics of the datasets are presented together with a summary of the retrieval systems and measurement records on which the dataset generation were based. Example validation results are given, based on comparisons to well-established reference observations, which demonstrate the good quality of the data. In particular the ensured spectral consistency and the rigorous uncertainty propagation through all processing levels can be considered as new features of the Cloud_cci datasets compared to existing datasets. In addition, the consistency among the individual datasets allows for a potential combination of them as well as facilitates studies on the impact of temporal sampling and spatial resolution on cloud climatologies.For each dataset a digital object identifier has been issued:Cloud_cci AVHRR-AM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-AM/V002Cloud_cci AVHRR-PM: https://doi.org/10.5676/DWD/ESA_Cloud_cci/AVHRR-PM/V002Cloud_cci MODIS-Terra: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Terra/V002Cloud_cci MODIS-Aqua: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MODIS-Aqua/V002Cloud_cci ATSR2-AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/ATSR2-AATSR/V002Cloud_cci MERIS+AATSR: https://doi.org/10.5676/DWD/ESA_Cloud_cci/MERIS+AATSR/V002

Description: A total of 14 years (September 2002 to September 2016) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) monthly mean cloud data are used to quantify possible changes in the cloud vertical distribution over the tropical Atlantic. For the analysis multiple linear regression techniques are used. For the investigated time period significant linear changes were found in the domain-averaged cloud-top height (CTH) (−178 m per decade), the high-cloud fraction (HCF) (−0.0006 per decade), and the low-cloud amount (0.001 per decade). The interannual variability of the time series (especially CTH and HCF) is highly influenced by the El Niño–Southern Oscillation (ENSO). Separating the time series into two phases, we quantified the linear change associated with the transition from more La Niña-like conditions to a phase with El Niño conditions (Phase 2) and vice versa (Phase 1). The transition from negative to positive ENSO conditions was related to a decrease in total cloud fraction (TCF) (−0.018 per decade; not significant) due to a reduction in the high-cloud amount (−0.024 per decade; significant). Observed anomalies in the mean CTH were found to be mainly caused by changes in HCF rather than by anomalies in the height of cloud tops themselves. Using the large-scale vertical motion ω at 500 hPa (from ERA-Interim ECMWF reanalysis data), the observed anomalies were linked to ENSO-induced changes in the atmospheric large-scale dynamics. The most significant and largest changes were found in regions with strong large-scale upward movements near the Equator. Despite the fact that with passive imagers such as MODIS it is not possible to vertically resolve clouds, this study shows the great potential for large-scale analysis of possible changes in the cloud vertical distribution due to the changing climate by using vertically resolved cloud cover and linking those changes to large-scale dynamics using other observations or model data.

Description: 14 years (September 2002 to September 2016) of Aqua Moderate Resolution Imaging Spectroradiometer (MODIS) monthly mean cloud data is analyzed to identify possible changes of the cloud vertical distribution over the Tropical Atlantic Ocean (TAO). For the analysis multiple linear regression techniques are used. Within the investigated period, no significant trend in the domain-averaged cloud vertical distribution was found. In terms of linear changes, two major phases (before and after November 2011) in the time-series of the TAO domain-average Cloud Top Height (CTH) and High Cloud Fraction (HCF) can be distinguished. While phase 1 is dominated by a significant linear increase, phase 2 is characterized by a strong, significant linear decrease. The observed trends were mainly caused by the El Niño Southern Oscillation (ENSO). The increase in CTH and HCF in phase 1, was attributed to the transition from El Niño (2002) to La Niña (2011) conditions. The strong decrease in phase 2, was caused by the opposite transition from a La Niña (2011) to a major El Niño event (2016). A comparison with the large scale vertical motion ω at 500 hPa obtained from ERA-Interim ECMWF Re-Analyses and the Nino3.4-Index indicates that the changes in HCF are induced by ENSO linked changes in the large scale vertical upward movements over regions with strong large scale ascent. A first comparison with the DARDAR data set, which combines CloudSat radar and CALIPSO lidar measurements, shows qualitatively good agreements for the interannual variability of the high cloud amount and its linear decrease in phase 2.

Description: The remote sensing of Total Column Water Vapour (TCWV) from polar orbiting, sun-synchronous satellite spectrometers such as the Medium Resolution Imaging Spectrometer (MERIS) on board of ENVISAT and the Moderate Imaging Spectroradiometer (MODIS) on board of Aqua and Terra enables observations on a high spatial resolution and a high accuracy over land surfaces. The observations serve studies about small scale variations of water vapour as well as the detection of local and global trends. However, depending on the swath width of the sensor, the temporal sampling is low and the observations of TCWV are limited to cloud-free land scenes. This study quantifies the representativeness of a single TCWV observation at the time of the satellite overpass under cloud-free conditions by investigating the diurnal cycle of TCWV using 9 years of a 2-hourly TCWV data set from global GNSS (Global Navigation Satellite Systems) stations. It turns out, that the TCWV observed at 10:30 local time (LT) is generally lower than the daily mean TCWV by 0.65 mm (4 %) on average for cloud-free cases. Averaging over all GNSS stations, the monthly mean TCWV at 10:30 LT, constrained to cases that are cloud-free, is by 5 mm (25 %) lower than the monthly mean TCWV at 10:30 LT of all cases. Additionally, the diurnal variability of TCWV is assessed. For the majority of GNSS stations, the amplitude of the averaged diurnal cycle ranges between 1 % and 5 % of the daily mean with a minimum between 6 LT and 10 LT and maximum between 16 LT and 20 LT. However, a high variability of TCWV on an individual day is detected. On average, the TCWV varies by 15 % around the daily mean.

Description: A site atmospheric state best estimate (SASBE) of the temperature profile above the GCOS (Global Climate Observing System) Reference Upper-Air Network (GRUAN) site at Lauder, New Zealand, has been developed. Data from multiple sources are combined within the SASBE to generate a high temporal resolution data set that includes an estimate of the uncertainty on every value.The SASBE has been developed to enhance the value of measurements made at the distributed GRUAN site at Lauder and Invercargill (about 180 km apart), and to demonstrate a methodology which can be adapted to other distributed sites. Within GRUAN, a distributed site consists of a cluster of instruments at different locations.The temperature SASBE combines measurements from radiosondes and automatic weather stations at Lauder and Invercargill, and ERA5 reanalysis, which is used to calculate a diurnal temperature cycle to which the SASBE converges in the absence of any measurements.The SASBE provides hourly temperature profiles at 16 pressure levels between the surface and 10 hPa for the years 1997 to 2012. Every temperature value has an associated uncertainty which is calculated by propagating the measurement uncertainties, the ERA5 ensemble standard deviations, and the ERA5 representativeness uncertainty through the retrieval chain. The SASBE has been long-term archived and is identified using the digital object identifier https://doi.org/10.5281/zenodo.1195779.The study demonstrates a method to combine data collected at distributed sites. The resulting best-estimate temperature data product for Lauder is expected to be valuable for satellite and model validation as measurements of atmospheric essential climate variables are sparse in the Southern Hemisphere. The SASBE could, for example, be used to constrain a radiative transfer model to provide top-of-the-atmosphere radiances with traceable uncertainty estimates.

Description: River water is the main source of dissolved organic carbon (DOC) in the Arctic Ocean. DOC plays an important role in the Arctic carbon cycle and its export from land to sea is expected to increase as ongoing climate change accelerates permafrost thaw. However, transport pathways and transformation of DOC in the land-to-ocean transition are mostly unknown. We collected DOC and aCDOM(λ) samples from 11 expeditions to river, coastal and offshore waters and present a new DOC-aCDOM(λ) model for the fluvial-marine transition zone in the Laptev Sea The aCDOM(λ) characteristics revealed that the DOM in samples of this dataset are primarily of terrigenous origin. Observed changes in aCDOM and its spectral slopes indicate that DOM is modified by microbial- and photo-degradation. Ocean Color Remote Sensing (OCRS) provides the absorption coefficient of colored dissolved organic matter (aCDOM(λ)sat) at λ = 440 or 443 nm, which can be used to estimate DOC concentration at high temporal and spatial resolution over large regions. We tested the statistical performance of five OCRS algorithms and evaluated the plausibility of the spatial distribution of derived aCDOM(λ)sat. The ONNS algorithm showed the best performance compared to in situ aCDOM(440) (r2 = 0.72). Additionally, we found ONNS-derived aCDOM(440), in contrast to other algorithms, to be partly independent of sediment concentration, making ONNS the most suitable aCDOM(λ)sat algorithm for the Laptev Sea region. The DOC-aCDOM(λ) model was applied to ONNS-derived aCDOM(440) and retrieved DOC concentration maps showed moderate agreement to in situ data (r2 = 0.53). The in situ and satellite-retrieved data were offset by up to several days, which may partly explain the weak correlation for this dynamic region. Satellite-derived surface water DOC concentration maps from MERIS satellite data demonstrate rapid removal of DOC within short time periods in coastal waters of the Laptev Sea, which is likely caused by physical mixing and different types of degradation processes. Using samples from all occurring water types leads to a more robust DOC-aCDOM(λ) model for the retrievals of DOC in Arctic shelf and river waters.