A 60-year ocean colour data set from the continuous plankton recorder

The phytoplankton colour index (PCI) of the Continuous Plankton Recorder (CPR) survey is an in situ measure of ocean colour, which is considered a proxy of the phytoplankton biomass. PCI has been extensively used to describe the major spatiotemporal patterns of phytoplankton in the North Atlantic Ocean and North Sea since 1931. Regardless of its wide application, the lack of an adequate evaluation to test the PCI's quantitative nature is an important limitation. To address this concern, a field trial over the main production season has been undertaken to assess the numerical values assigned by previous investigations for each category of the greenness of the PCI. CPRs were towed across the English Channel from Roscoff to Plymouth consecutively for each of 8 months producing 76 standard CPR samples, each representing 10 nautical miles of tow. The results of this experiment test and update the PCI methodology, and confirm the validity of this long-term in situ ocean colour data set. In addition, using a 60-year time series of the PCI of the western English Channel, a comparison is made between the previous and the current revised experimental calculations of PCI.

Order Flow and the Monetary Model of Exchange Rates: Evidence from a Novel Data Set

We propose an exchange rate model that is a hybrid of the conventional specification with monetary fundamentals and the Evans–Lyons microstructure approach. We estimate a model augmented with order flow variables, using a unique data set: almost 100 monthly observations on interdealer order flow on dollar/euro and dollar/yen. The augmented macroeconomic, or “hybrid,” model exhibits greater in-sample stability and out of sample forecasting improvement vis-à-vis the basic macroeconomic and random walk specifications.

The geriatric minimum data set for clinical trials (GMDS)

To overcome the weak evidence base coming from often poor and insufficient clinical research in older people, a minimum data set to achieve harmonisation is highly advisable. This will lead to uniform nomenclature and to the standardisation of the assessment tools. Our primary objective was to develop a Geriatric Minimum Data Set (GMDS) for clinical research.

On Variable Selection of a Nonlinear Non-parametric System with a Limited Data Set: A Stepwise Algorithm

his paper considers a problem of identification for a high dimensional nonlinear non-parametric system when only a limited data set is available. The algorithms are proposed for this purpose which exploit the relationship between the input variables and the output and further the inter-dependence of input variables so that the importance of the input variables can be established. A key to these algorithms is the non-parametric two stage input selection algorithm.

A Meteorological and Snow observational data set from Snoqualmie Pass (921 m), Washington Cascades, U.S.

We introduce a quality controlled observational atmospheric, snow, and soil data set from Snoqualmie Pass, Washington, U.S.A., to enable testing of hydrometeorological and snow process representations within a rain-snow transitional climate where existing observations are sparse and limited. Continuous meteorological forcing (including air temperature, total precipitation, wind speed, specific humidity, air pressure, short- and longwave irradiance) are provided at hourly intervals for a 24-year historical period (water years 1989-2012) and at half-hourly intervals for a more-recent period (water years 2013-2015), separated based on the availability of observations. Additional observations include 40-years of snow board new snow accumulation, multiple measurements of total snow depth, and manual snow pits, while more recent years include sub-daily surface temperature, snowpack drainage, soil moisture and temperature profiles, and eddy co-variance derived turbulent heat flux. This data set is ideal for testing hypotheses about energy balance, soil and snow processes in the rain-snow transition zone. Plots of live data can be found here: http://depts.washington.edu/mtnhydr/cgi/plot.cgi

When a data set can be considered compositional?

Traditionally, compositional data has been identified with closed data, and the simplex has been considered as the natural sample space of this kind of data. In our opinion, the emphasis on the constrained nature of compositional data has contributed to mask its real nature. More crucial than the constraining property of compositional data is the scale-invariant property of this kind of data. Indeed, when we are considering only few parts of a full composition we are not working with constrained data but our data are still compositional. We believe that it is necessary to give a more precise definition of composition. This is the aim of this oral contribution

Mid-Holocene land-surface conditions in northern Africa and the Arabian peninsula: a data set for the analysis of biogeophysical feedbacks in the climate system

Large changes in the extent of northern subtropical arid regions during the Holocene are attributed to orbitally forced variations in monsoon strength and have been implicated in the regulation of atmospheric trace gas concentrations on millenial timescales. Models that omit biogeophysical feedback, however, are unable to account for the full magnitude of African monsoon amplification and extension during the early to middle Holocene (˜9500–5000 years B.P.). A data set describing land-surface conditions 6000 years B.P. on a 1° × 1° grid across northern Africa and the Arabian Peninsula has been prepared from published maps and other sources of palaeoenvironmental data, with the primary aim of providing a realistic lower boundary condition for atmospheric general circulation model experiments similar to those performed in the Palaeoclimate Modelling Intercomparison Project. The data set includes information on the percentage of each grid cell occupied by specific vegetation types (steppe, savanna, xerophytic woods/scrub, tropical deciduous forest, and tropical montane evergreen forest), open water (lakes), and wetlands, plus information on the flow direction of major drainage channels for use in large-scale palaeohydrological modeling.

The WFDEI meteorological forcing data set: WATCH Forcing Data methodology applied to ERA-Interim reanalysis data

The WFDEI meteorological forcing data set has been generated using the same methodology as the widely used WATCH Forcing Data (WFD) by making use of the ERA-Interim reanalysis data. We discuss the specifics of how changes in the reanalysis and processing have led to improvement over the WFD. We attribute improvements in precipitation and wind speed to the latest reanalysis basis data and improved downward shortwave fluxes to the changes in the aerosol corrections. Covering 1979–2012, the WFDEI will allow more thorough comparisons of hydrological and Earth System model outputs with hydrologically and phenologically relevant satellite products than using the WFD.

ERA-Interim/Land: a global land surface reanalysis data set

ERA-Interim/Land is a global land surface reanalysis data set covering the period 1979–2010. It describes the evolution of soil moisture, soil temperature and snowpack. ERA-Interim/Land is the result of a single 32-year simulation with the latest ECMWF (European Centre for Medium-Range Weather Forecasts) land surface model driven by meteorological forcing from the ERA-Interim atmospheric reanalysis and precipitation adjustments based on monthly GPCP v2.1 (Global Precipitation Climatology Project). The horizontal resolution is about 80 km and the time frequency is 3-hourly. ERA-Interim/Land includes a number of parameterization improvements in the land surface scheme with respect to the original ERA-Interim data set, which makes it more suitable for climate studies involving land water resources. The quality of ERA-Interim/Land is assessed by comparing with ground-based and remote sensing observations. In particular, estimates of soil moisture, snow depth, surface albedo, turbulent latent and sensible fluxes, and river discharges are verified against a large number of site measurements. ERA-Interim/Land provides a global integrated and coherent estimate of soil moisture and snow water equivalent, which can also be used for the initialization of numerical weather prediction and climate models.

A new data set of soil mineralogy for dust-cycle modeling

The mineralogy of airborne dust affects the impact of dust particles on direct and indirect radiative forcing, on atmospheric chemistry and on biogeochemical cycling. It is determined partly by the mineralogy of the dust-source regions and partly by size-dependent fractionation during erosion and transport. Here we present a data set that characterizes the clay and silt-sized fractions of global soil units in terms of the abundance of 12 minerals that are important for dust–climate interactions: quartz, feldspars, illite, smectite, kaolinite, chlorite, vermiculite, mica, calcite, gypsum, hematite and goethite. The basic mineralogical information is derived from the literature, and is then expanded following explicit rules, in order to characterize as many soil units as possible. We present three alternative realizations of the mineralogical maps, taking the uncertainties in the mineralogical data into account. We examine the implications of the new database for calculations of the single scattering albedo of airborne dust and thus for dust radiative forcing.

Current model capabilities for simulating black carbon and sulfate concentrations in the Arctic atmosphere: a multi-model evaluation using a comprehensive measurement data set

The concentrations of sulfate, black carbon (BC) and other aerosols in the Arctic are characterized by high values in late winter and spring (so-called Arctic Haze) and low values in summer. Models have long been struggling to capture this seasonality and especially the high concentrations associated with Arctic Haze. In this study, we evaluate sulfate and BC concentrations from eleven different models driven with the same emission inventory against a comprehensive pan-Arctic measurement data set over a time period of 2 years (2008–2009). The set of models consisted of one Lagrangian particle dispersion model, four chemistry transport models (CTMs), one atmospheric chemistry-weather forecast model and five chemistry climate models (CCMs), of which two were nudged to meteorological analyses and three were running freely. The measurement data set consisted of surface measurements of equivalent BC (eBC) from five stations (Alert, Barrow, Pallas, Tiksi and Zeppelin), elemental carbon (EC) from Station Nord and Alert and aircraft measurements of refractory BC (rBC) from six different campaigns. We find that the models generally captured the measured eBC or rBC and sulfate concentrations quite well, compared to previous comparisons. However, the aerosol seasonality at the surface is still too weak in most models. Concentrations of eBC and sulfate averaged over three surface sites are underestimated in winter/spring in all but one model (model means for January–March underestimated by 59 and 37 % for BC and sulfate, respectively), whereas concentrations in summer are overestimated in the model mean (by 88 and 44 % for July–September), but with overestimates as well as underestimates present in individual models. The most pronounced eBC underestimates, not included in the above multi-site average, are found for the station Tiksi in Siberia where the measured annual mean eBC concentration is 3 times higher than the average annual mean for all other stations. This suggests an underestimate of BC sources in Russia in the emission inventory used. Based on the campaign data, biomass burning was identified as another cause of the modeling problems. For sulfate, very large differences were found in the model ensemble, with an apparent anti-correlation between modeled surface concentrations and total atmospheric columns. There is a strong correlation between observed sulfate and eBC concentrations with consistent sulfate/eBC slopes found for all Arctic stations, indicating that the sources contributing to sulfate and BC are similar throughout the Arctic and that the aerosols are internally mixed and undergo similar removal. However, only three models reproduced this finding, whereas sulfate and BC are weakly correlated in the other models. Overall, no class of models (e.g., CTMs, CCMs) performed better than the others and differences are independent of model resolution.

The BALTIMOS (BALTEX Integrated Model System) fi eld experiments: A comprehensive atmospheric boundary layer data set for model validation over the open and ice- covered Baltic Sea

A comprehensive atmospheric boundary layer (ABL) data set was collected in eight fi eld experiments (two during each season) over open water and sea ice in the Baltic Sea during 1998–2001 with the primary objective to validate the coupled atmospheric- ice-ocean-land surface model BALTIMOS (BALTEX Integrated Model System). Measurements were taken by aircraft, ships and surface stations and cover the mean and turbulent structure of the ABL including turbulent fl uxes, radiation fl uxes, and cloud conditions. Measurement examples of the spatial variability of the ABL over the ice edge zone and of the stable ABL over open water demonstrate the wide range of ABL conditions collected and the strength of the data set which can also be used to validate other regional models.