ALBERT

Description: The need for open science has been recognized by the communities of meteorology and climate science. While these domains are mature in terms of applying digital technologies, the implementation of open science methodologies is less advanced. In a session on “Weather and Climate Science in the Digital Era” at the 14th IEEE International eScience Conference domain specialists and data and computer scientists discussed the road towards open weather and climate science. Roughly 80 % of the studies presented in the conference session showed the added value of open data and software. These studies included open datasets from disparate sources in their analyses or developed tools and approaches that were made openly available to the research community. Furthermore, shared software is a prerequisite for the studies which presented systems like a model coupling framework or digital collaboration platform. Although these studies showed that sharing code and data is important, the consensus among the participants was that this is not sufficient to achieve open weather and climate science and that there are important issues to address. At the level of technology, the application of the findable, accessible, interoperable, and reusable (FAIR) principles to many datasets used in weather and climate science remains a challenge. This may be due to scalability (in the case of high-resolution climate model data, for example), legal barriers such as those encountered in using weather forecast data, or issues with heterogeneity (for example, when trying to make use of citizen data). In addition, the complexity of current software platforms often limits collaboration between researchers and the optimal use of open science tools and methods. The main challenges we observed, however, were non-technical and impact the practice of science as a whole. There is a need for new roles and responsibilities in the scientific process. People working at the interface of science and digital technology – e.g., data stewards and research software engineers – should collaborate with domain researchers to ensure the optimal use of open science tools and methods. In order to remove legal boundaries on sharing data, non-academic parties such as meteorological institutes should be allowed to act as trusted agents. Besides the creation of these new roles, novel policies regarding open weather and climate science should be developed in an inclusive way in order to engage all stakeholders. Although there is an ongoing debate on open science in the community, the individual aspects are usually discussed in isolation. Our approach in this paper takes the discourse further by focusing on “open science in weather and climate research” as a whole. We consider all aspects of open science and discuss the challenges and opportunities of recent open science developments in data, software, and hardware. We have compiled these into a list of concrete recommendations that could bring us closer to open weather and climate science. We acknowledge that the development of open weather and climate science requires effort to change, but the benefits are large. We have observed these benefits directly in the studies presented in the conference and believe that it leads to much faster progress in understanding our complex world.

Description: In the context of the Arctic amplification of climate change affecting the regional atmospheric hydrological cycle, it is crucial to characterize the present-day moisture sources of the Arctic. The isotopic composition is an important tool to enhance our understanding of the drivers of the hydrological cycle due to the different molecular characteristics of water stable isotopes during phase change. This study introduces 2 years of continuous in situ water vapour and precipitation isotopic observations conducted since July 2015 in the eastern Siberian Lena delta at the research station on Samoylov Island. The vapour isotopic signals are dominated by variations at seasonal and synoptic timescales. Diurnal variations of the vapour isotopic signals are masked by synoptic variations, indicating low variations of the amplitude of local sources at the diurnal scale in winter, summer and autumn. Low-amplitude diurnal variations in spring may indicate exchange of moisture between the atmosphere and the snow-covered surface. Moisture source diagnostics based on semi-Lagrangian backward trajectories reveal that different air mass origins have contrasting contributions to the moisture budget of the Lena delta region. At the seasonal scale, the distance from the net moisture sources to the arrival site strongly varies. During the coldest months, no contribution from local secondary evaporation is observed. Variations of the vapour isotopic composition during the cold season on the synoptic timescale are strongly related to moisture source regions and variations in atmospheric transport: warm and isotopically enriched moist air is linked to fast transport from the Atlantic sector, while dry and cold air with isotopically depleted moisture is generally associated with air masses moving slowly over northern Eurasia.

Description: We used the recently developed commercially available Delta Ray isotope ratio infrared spectrometer (IRIS) to continuously measure the CO2 concentration c and its isotopic composition δ13C and δ18O in a managed beech forest in central Germany. Our objectives are (a) to characterize the Delta Ray IRIS and evaluate its internal calibration procedure and (b) to quantify the seasonal variability of c, δ13C, δ18O and the isotopic composition of nighttime net ecosystem CO2 exchange (respiration) Reco13C and Reco18O derived from Keeling plot intercepts. The analyzer's minimal Allan deviation (as a measure of precision) was below 0.01 ppm for the CO2 concentration and below 0.03 ‰ for both δ values. The potential accuracy (defined as the 1σ deviation from the respective linear regression that was used for calibration) was approximately 0.45 ppm for c, 0.24 ‰ for 13C and 0.3 ‰ for 18O. For repeated measurements of a target gas in the field, the long-term standard deviation from the mean was 0.3 ppm for c and below 0.3 ‰ for both δ values. We used measurements of nine different inlet heights to evaluate the isotopic compositions of nighttime net ecosystem CO2 exchange Reco13C and Reco18O in a 3-month measurement campaign in a beech forest in autumn 2015. During this period, an early snow and frost event occurred, coinciding with a change in the observed characteristics of both Reco13C and Reco18O. Before the first snow, Reco13C correlated significantly (p    0.1) for the period after the first snow, indicating a decoupling of δ13C of respiration from recent assimilates. For 18O, we measured a decrease of 30 ‰ within 10 days in Reco18O after the snow event, potentially reflecting the influence of 18O depleted snow on soil moisture. This decrease was 10 times larger than the corresponding decrease in δ18O in ambient CO2 (below 3 ‰) and took 3 times longer to recover (3 weeks vs. 1 week). In summary, we conclude that (1) the new Delta Ray IRIS with its internal calibration procedure provides an opportunity to precisely and accurately measure c, δ13C and δ18O at field sites and (2) even short snow or frost events might have strong effects on the isotopic composition (in particular 18O) of CO2 exchange on an ecosystem scale.

Description: This study presents the results of the Fourth Filter Radiometer Comparison that was held in Davos, Switzerland, between 28 September and 16 October 2015. Thirty filter radiometers and spectroradiometers from 12 countries participated including reference instruments from global aerosol networks. The absolute differences of all instruments compared to the reference have been based on the World Meteorological Organization (WMO) criterion defined as follows: 95% of the measured data has to be within 0.005 ± 0.001∕m (where m is the air mass). At least 24 out of 29 instruments achieved this goal at both 500 and 865 nm, while 12 out of 17 and 13 out of 21 achieved this at 368 and 412 nm, respectively. While searching for sources of differences among different instruments, it was found that all individual differences linked to Rayleigh, NO2, ozone, water vapor calculations and related optical depths and air mass calculations were smaller than 0.01 in aerosol optical depth (AOD) at 500 and 865 nm. Different cloud-detecting algorithms used have been compared. Ångström exponent calculations showed relatively large differences among different instruments, partly because of the high calculation uncertainty of this parameter in low AOD conditions. The overall low deviations of these AOD results and the high accuracy of reference aerosol network instruments demonstrated a promising framework to achieve homogeneity, compatibility and harmonization among the different spectral AOD networks in the near future.

Description: In this study, we present a novel nitrogen dioxide (NO2) differential optical absorption spectroscopy (DOAS) retrieval in the ultraviolet (UV) spectral range for observations from the Global Ozone Monitoring Instrument 2 on board EUMETSAT's MetOp-A (GOME-2A) satellite. We compare the results to those from an established NO2 retrieval in the visible (vis) spectral range from the same instrument and investigate how differences between the two are linked to the NO2 vertical profile shape in the troposphere.As expected, radiative transfer calculations for satellite geometries show that the sensitivity close to the ground is higher in the vis than in the UV spectral range. Consequently, NO2 slant column densities (SCDs) in the vis are usually higher than in the UV if the NO2 is close to the surface. Therefore, these differences in NO2 SCDs between the two spectral ranges contain information on the vertical distribution of NO2 in the troposphere. We combine these results with radiative transfer calculations and simulated NO2 fields from the TM5-MP chemistry transport model to evaluate the simulated NO2 vertical distribution.We investigate regions representative of both anthropogenic and biomass burning NO2 pollution. Anthropogenic air pollution is mostly located in the boundary layer close to the surface, which is reflected by large differences between UV and vis SCDs of  ∼  60 %. Biomass burning NO2 in contrast is often uplifted into elevated layers above the boundary layer. This is best seen in tropical Africa south of the Equator, where the biomass burning NO2 is well observed in the UV, and the SCD difference between the two spectral ranges is only  ∼  36 %. In tropical Africa north of the Equator, however, the biomass burning NO2 is located closer to the ground, reducing its visibility in the UV.While not enabling a full retrieval of the vertical NO2 profile shape in the troposphere, our results can help to constrain the vertical profile of NO2 in the lower troposphere and, when analysed together with simulated NO2 fields, can help to better interpret the model output.

Description: In this study, the quality of wind and wave data provided by the new Sentinel-3A satellite is evaluated. We focus on coastal areas, where altimeter data are of lower quality than those for the open ocean. The satellite data of Sentinel-3A, Jason-2 and CryoSat-2 are assessed in a comparison with in situ measurements and spectral wave model (WAM) simulations. The sensitivity of the wave model to wind forcing is evaluated using data with different temporal and spatial resolution, such as ERA-Interim and ERA5 reanalyses, ECMWF operational analysis and short-range forecasts, German Weather Service (DWD) forecasts and regional atmospheric model simulations -coastDat. Numerical simulations show that both the wave model forced using the ERA5 reanalyses and that forced using the ECMWF operational analysis/forecast demonstrate the best capability over the whole study period, as well as during extreme events. To further estimate the variance of the significant wave height of ensemble members for different wind forcings, especially during extreme events, an empirical orthogonal function (EOF) analysis is performed. Intercomparisons between remote sensing and in situ observations demonstrate that the overall quality of the former is good over the North Sea and Baltic Sea throughout the study period, although the significant wave heights estimated based on satellite data tend to be greater than the in situ measurements by 7cm to 26cm. The quality of all satellite data near the coastal area decreases; however, within 10km off the coast, Sentinel-3A performs better than the other two satellites. Analyses in which data from satellite tracks are separated in terms of onshore and offshore flights have been carried out. No substantial differences are found when comparing the statistics for onshore and offshore flights. Moreover, no substantial differences are found between satellite tracks under various metocean conditions. Furthermore, the satellite data quality does not depend on the wind direction relative to the flight direction. Thus, the quality of the data obtained by the new Sentinel-3A satellite over coastal areas is improved compared to that of older satellites.

Description: Quantifying the magnitude of post-depositional processes affecting the isotopic composition of surface snow is essential for a more accurate interpretation of ice core data. To achieve this, high temporal resolution measurements of both lower atmospheric water vapor and surface snow isotopic composition are required. This study presents continuous measurements of water vapor isotopes performed in East Antarctica (Kohnen station) from December 2013 to January 2014 using a laser spectrometer. Observations have been compared with the outputs of two atmospheric general circulation models (AGCMs) equipped with water vapor isotopes: ECHAM5-wiso and LMDZ5Aiso. During our monitoring period, the signals in the 2 m air temperature T, humidity mixing ratio q and both water vapor isotopes δD and δ18O are dominated by the presence of diurnal cycles. Both AGCMs simulate similar diurnal cycles with a mean amplitude 30 to 70 % lower than observed, possibly due to an incorrect simulation of the surface energy balance and the boundary layer dynamics. In parallel, snow surface samples were collected each hour over 35 h, with a sampling depth of 2–5 mm. A diurnal cycle in the isotopic composition of the snow surface is observed in phase with the water vapor, reaching a peak-to-peak amplitude of 3 ‰ for δD over 24 h (compared to 36 ‰ for δD in the water vapor). A simple box model treated as a closed system has been developed to study the exchange of water molecules between an air and a snow reservoir. In the vapor, the box model simulations show too much isotopic depletion compared to the observations. Mixing with other sources (advection, free troposphere) has to be included in order to fit the observations. At the snow surface, the simulated isotopic values are close to the observations with a snow reservoir of  ∼ 5 mm depth (range of the snow sample depth). Our analysis suggests that fractionation occurs during sublimation and that vapor–snow exchanges can no longer be considered insignificant for the isotopic composition of near-surface snow in polar regions.

Description: Pine Island Glacier is one of the fastest changing glaciers of the Antarctic Ice Sheet and therefore of scientific interest. The glacier holds enough ice to raise the global sea level significantly (~ 0.5 m) when fully melted. The question addressed by numerous modelling studies of the glacier focuses on whether the observed changes are a start of an uncontrolled and accelerating retreat. The movement of the glacier is, in the fast-flowing areas, dominated by basal motion. In modelling studies the parametrisation of the basal motion is therefore crucial. Inversion methods are commonly applied to reproduce the complex surface flow structure of Pine Island Glacier by using information of the observed surface velocity field to constrain, among other things, basal sliding. We introduce two different approaches of combining a physical parameter, the basal roughness, with basal sliding parametrisations. This way basal sliding is again connected closer to its original formulation. We show that the basal roughness is an important and helpful parameter to consider and that many features of the flow structure can be reproduced with these approaches.

Description: The shrinking Arctic sea ice cover observed during the last decades is probably the clearest manifestation of ongoing climate change. While climate models in general reproduce the sea ice retreat in the Arctic during the 20th century and simulate further sea ice area loss during the 21st century in response to anthropogenic forcing, the models suffer from large biases and the model results exhibit considerable spread. The last generation of climate models from World Climate Research Programme Coupled Model Intercomparison Project Phase 5 (CMIP5), when compared to the previous CMIP3 model ensemble and considering the whole Arctic, were found to be more consistent with the observed changes in sea ice extent during the recent decades. Some CMIP5 models project strongly accelerated (non-linear) sea ice loss during the first half of the 21st century. Here, complementary to previous studies, we compare results from CMIP3 and CMIP5 with respect to regional Arctic sea ice change. We focus on September and March sea ice. Sea ice area (SIA) variability, sea ice concentration (SIC) variability, and characteristics of the SIA seasonal cycle and interannual variability have been analysed for the whole Arctic, termed Entire Arctic, Central Arctic and Barents Sea. Further, the sensitivity of SIA changes to changes in Northern Hemisphere (NH) averaged temperature is investigated and several important dynamical links between SIA and natural climate variability involving the Atlantic Meridional Overturning Circulation (AMOC), North Atlantic Oscillation (NAO) and sea level pressure gradient (SLPG) in the western Barents Sea opening serving as an index of oceanic inflow to the Barents Sea are studied. The CMIP3 and CMIP5 models not only simulate a coherent decline of the Arctic SIA but also depict consistent changes in the SIA seasonal cycle and in the aforementioned dynamical links. The spatial patterns of SIC variability improve in the CMIP5 ensemble, particularly in summer. Both CMIP ensembles depict a significant link between the SIA and NH temperature changes. Our analysis suggests that, on average, the sensitivity of SIA to external forcing is enhanced in the CMIP5 models. The Arctic SIA variability response to anthropogenic forcing is different in CMIP3 and CMIP5. While the CMIP3 models simulate increased variability in March and September, the CMIP5 ensemble shows the opposite tendency. A noticeable improvement in the simulation of summer SIA by the CMIP5 models is often accompanied by worse results for winter SIA characteristics. The relation between SIA and mean AMOC changes is opposite in September and March, with March SIA changes being positively correlated with AMOC slowing. Finally, both CMIP ensembles demonstrate an ability to capture, at least qualitatively, important dynamical links of SIA to decadal variability of the AMOC, NAO and SLPG. SIA in the Barents Sea is strongly overestimated by the majority of the CMIP3 and CMIP5 models, and projected SIA changes are characterized by a large spread giving rise to high uncertainty.

Description: The strongest manifestation of global warming is observed in the Arctic. The warming in the Arctic during the recent decades is about twice as strong as in the global average and has been accompanied by a summer sea ice decline that is very likely unprecedented during the last millennium. Here, Arctic sea ice variability is analyzed in the ensemble of CMIP3 models. Complementary to several previous studies, we focus on regional aspects, in particular on the Barents Sea. We also investigate the changes in the seasonal cycle and interannual variability. In all regions, the models predict a reduction in sea ice area and sea ice volume during 1900–2100. Toward the end of the 21st century, the models simulate higher sea ice area variability in September than in March, whereas the variability in the preindustrial control runs is higher in March. Furthermore, the amplitude and phase of the sea ice seasonal cycle change in response to enhanced greenhouse warming. The amplitude of the sea ice area seasonal cycle increases due to the very strong sea ice area decline in September. The seasonal cycle amplitude of the sea ice volume decreases due to the stronger reduction of sea ice volume in March. Multi-model mean estimates for the late 20th century are comparable with observational data only for the entire Arctic and the Central Arctic. In the Barents Sea, differences between the multi-model mean and the observational data are more pronounced. Regional sea ice sensitivity to Northern Hemisphere average surface warming has been investigated.