Environmental variables and multivariate seabed classification maps via k means clustering of Potter Cove, Antarctica, link to input and result files in GeoTIFF format

This study subdivides the Potter Cove, King George Island, Antarctica, into seafloor regions using multivariate statistical methods. These regions are categories used for comparing, contrasting and quantifying biogeochemical processes and biodiversity between ocean regions geographically but also regions under development within the scope of global change. The division obtained is characterized by the dominating components and interpreted in terms of ruling environmental conditions. The analysis includes in total 42 different environmental variables, interpolated based on samples taken during Australian summer seasons 2010/2011 and 2011/2012. The statistical errors of several interpolation methods (e.g. IDW, Indicator, Ordinary and Co-Kriging) with changing settings have been compared and the most reasonable method has been applied. The multivariate mathematical procedures used are regionalized classification via k means cluster analysis, canonical-correlation analysis and multidimensional scaling. Canonical-correlation analysis identifies the influencing factors in the different parts of the cove. Several methods for the identification of the optimum number of clusters have been tested and 4, 7, 10 as well as 12 were identified as reasonable numbers for clustering the Potter Cove. Especially the results of 10 and 12 clusters identify marine-influenced regions which can be clearly separated from those determined by the geological catchment area and the ones dominated by river discharge.

The CLIMATE data base of monthly normals for globally gridded climatic variables (version 2.1)

This database (Leemans & Cramer 1991) contains monthly averages of mean temperature, temperature range, precipitation, rain days and sunshine hours for the terrestrial surface of the globe, gridded at 0.5 degree longitude/latitude resolution. All grd-files contain the same 62483 pixels in the same order, with 30' latitude and longitude resolution. The coordinates are in degree-decimals and indicate the SW corner of each pixel. Topography is from ETOPO5 and indicates modal elevation. Data were generated from a large data base, using the partial thin-plate splining algorithm (Hutchinson & Bischof 1983). This version is widely used around the globe, notably by all groups participating in the IGBP NPP model intercomparison.

Mean dynamic topography and oceanographic parameters estimated from an inverse model and satellite geodesy, with link to model result in one single NetCDF file (392 MB), including the inverse data error covariance

Geostrophic surface velocities can be derived from the gradients of the mean dynamic topography-the difference between the mean sea surface and the geoid. Therefore, independently observed mean dynamic topography data are valuable input parameters and constraints for ocean circulation models. For a successful fit to observational dynamic topography data, not only the mean dynamic topography on the particular ocean model grid is required, but also information about its inverse covariance matrix. The calculation of the mean dynamic topography from satellite-based gravity field models and altimetric sea surface height measurements, however, is not straightforward. For this purpose, we previously developed an integrated approach to combining these two different observation groups in a consistent way without using the common filter approaches (Becker et al. in J Geodyn 59(60):99-110, 2012, doi:10.1016/j.jog.2011.07.0069; Becker in Konsistente Kombination von Schwerefeld, Altimetrie und hydrographischen Daten zur Modellierung der dynamischen Ozeantopographie, 2012, http://nbn-resolving.de/nbn:de:hbz:5n-29199). Within this combination method, the full spectral range of the observations is considered. Further, it allows the direct determination of the normal equations (i.e., the inverse of the error covariance matrix) of the mean dynamic topography on arbitrary grids, which is one of the requirements for ocean data assimilation. In this paper, we report progress through selection and improved processing of altimetric data sets. We focus on the preprocessing steps of along-track altimetry data from Jason-1 and Envisat to obtain a mean sea surface profile. During this procedure, a rigorous variance propagation is accomplished, so that, for the first time, the full covariance matrix of the mean sea surface is available. The combination of the mean profile and a combined GRACE/GOCE gravity field model yields a mean dynamic topography model for the North Atlantic Ocean that is characterized by a defined set of assumptions. We show that including the geodetically derived mean dynamic topography with the full error structure in a 3D stationary inverse ocean model improves modeled oceanographic features over previous estimates.

Euphausiid respiration model revamped, link to model results

Euphausiids constitute major biomass component in shelf ecosystems and play a fundamental role in the rapid vertical transport of carbon from the ocean surface to the deeper layers during their daily vertical migration (DVM). DVM depth and migration patterns depend on oceanographic conditions with respect to temperature, light and oxygen availability at depth, factors that are highly dependent on season in most marine regions. Changes in the abiotic conditions also shape Euphausiid metabolism including aerobic and anaerobic energy production. Here we introduce a global krill respiration model which includes the effect of latitude (LAT), the day of the year of interest (DoY), and the number of daylight hours on the day of interest (DLh), in addition to the basal variables that determine ectothermal oxygen consumption (temperature, body mass and depth) in the ANN model (Artificial Neural Networks). The newly implemented parameters link space and time in terms of season and photoperiod to krill respiration. The ANN model showed a better fit (r**2=0.780) when DLh and LAT were included, indicating a decrease in respiration with increasing LAT and decreasing DLh. We therefore propose DLh as a potential variable to consider when building physiological models for both hemispheres. We also tested for seasonality the standard respiration rate of the most common species that were investigated until now in a large range of DLh and DoY with Multiple Linear Regression (MLR) or General Additive model (GAM). GAM successfully integrated DLh (r**2= 0.563) and DoY (r**2= 0.572) effects on respiration rates of the Antarctic krill, Euphausia superba, yielding the minimum metabolic activity in mid-June and the maximum at the end of December. Neither the MLR nor the GAM approach worked for the North Pacific krill Euphausia pacifica, and MLR for the North Atlantic krill Meganyctiphanes norvegica remained inconclusive because of insufficient seasonal data coverage. We strongly encourage comparative respiration measurements of worldwide Euphausiid key species at different seasons to improve accuracy in ecosystem modelling.

ERA-interim reanalysis debiased at FLUXNET sites

(preliminary) Exchanges of carbon, water and energy between the land surface and the atmosphere are monitored by eddy covariance technique at the ecosystem level. Currently, the FLUXNET database contains more than 500 sites registered and up to 250 of them sharing data (Free Fair Use dataset). Many modelling groups use the FLUXNET dataset for evaluating ecosystem model's performances but it requires uninterrupted time series for the meteorological variables used as input. Because original in-situ data often contain gaps, from very short (few hours) up to relatively long (some months), we develop a new and robust method for filling the gaps in meteorological data measured at site level. Our approach has the benefit of making use of continuous data available globally (ERA-interim) and high temporal resolution spanning from 1989 to today. These data are however not measured at site level and for this reason a method to downscale and correct the ERA-interim data is needed. We apply this method on the level 4 data (L4) from the LaThuile collection, freely available after registration under a Fair-Use policy. The performances of the developed method vary across sites and are also function of the meteorological variable. On average overall sites, the bias correction leads to cancel from 10% to 36% of the initial mismatch between in-situ and ERA-interim data, depending of the meteorological variable considered. In comparison to the internal variability of the in-situ data, the root mean square error (RMSE) between the in-situ data and the un-biased ERA-I data remains relatively large (on average overall sites, from 27% to 76% of the standard deviation of in-situ data, depending of the meteorological variable considered). The performance of the method remains low for the Wind Speed field, in particular regarding its capacity to conserve a standard deviation similar to the one measured at FLUXNET stations.

Raster maps for 29 environmental variables in three geographical regions

Aim: Greater understanding of the processes underlying biological invasions is required to determine and predict invasion risk. Two subspecies of olive (Olea europaea subsp. europaea and Olea europaea subsp. cuspidata) have been introduced into Australia from the Mediterranean Basin and southern Africa during the 19th century. Our aim was to determine to what extent the native environmental niches of these two olive subspecies explain the current spatial segregation of the subspecies in their non-native range. We also assessed whether niche shifts had occurred in the non-native range, and examined whether invasion was associated with increased or decreased occupancy of niche space in the non-native range relative to the native range. Location: South-eastern Australia, Mediterranean Basin and southern Africa. Methods: Ecological niche models (ENMs) were used to quantify the similarity of native and non-native realized niches. Niche shifts were characterized by the relative contribution of niche expansion, stability and contraction based on the relative occupancy of environmental space by the native and non-native populations. Results: Native ENMs indicated that the spatial segregation of the two subspecies in their non-native range was partly determined by differences in their native niches. However, we found that environmentally suitable niches were less occupied in the non-native range relative to the native range, indicating that niche shifts had occurred through a contraction of the native niches after invasion, for both subspecies. Main conclusions: The mapping of environmental factors associated with niche expansion, stability or contraction allowed us to identify areas of greater invasion risk. This study provides an example of successful invasions that are associated with niche shifts, illustrating that introduced plant species are sometimes readily able to establish in novel environments. In these situations the assumption of niche stasis during invasion, which is implicitly assumed by ENMs, may be unreasonable.

Icebergs in the Amundsen Sea (Antarctica) determined from ENVISAT ASAR WSM data using object-based image analysis approach, link to ESRI GDB files

ENVISAT ASAR WSM images with pixel size 150 × 150 m, acquired in different meteorological, oceanographic and sea ice conditions were used to determined icebergs in the Amundsen Sea (Antarctica). An object-based method for automatic iceberg detection from SAR data has been developed and applied. The object identification is based on spectral and spatial parameters on 5 scale levels, and was verified with manual classification in four polygon areas, chosen to represent varying environmental conditions. The algorithm works comparatively well in freezing temperatures and strong wind conditions, prevailing in the Amundsen Sea during the year. The detection rate was 96% which corresponds to 94% of the area (counting icebergs larger than 0.03 km**2), for all seasons. The presented algorithm tends to generate errors in the form of false alarms, mainly caused by the presence of ice floes, rather than misses. This affects the reliability since false alarms were manually corrected post analysis.

Objective Mapping of Argo data in the Weddell Gyre: a gridded dataset of upper ocean water properties, link to data files in NetCDF format

The Weddell Gyre plays a crucial role in the regulation of climate by transferring heat into the deep ocean through deep and bottom water mass formation. However, our understanding of Weddell Gyre water mass properties is limited to regions of data availability, primarily along the Prime Meridian. The aim is to provide a dataset of the upper water column properties of the entire Weddell Gyre. Objective mapping was applied to Argo float data in order to produce spatially gridded, time composite maps of temperature and salinity for fixed pressure levels ranging from 50 to 2000 dbar, as well as temperature, salinity and pressure at the level of the sub-surface temperature maximum. While the data are currently too limited to incorporate time into the gridded structure, the data are extensive enough to produce maps of the entire region across three time composite periods (2002-2005, 2006-2009 and 2010-2013), which can be used to determine how representative conclusions drawn from data collected along general RV transect lines are on a gyre scale perspective. The time composite data sets are provided as netCDF files; one for each time period. Mapped fields of conservative temperature, absolute salinity and potential density are provided for 41 vertical pressure levels. The above variables as well as pressure are provided at the level of the sub-surface temperature maximum. Corresponding mapping errors are also included in the netCDF files. Further details are provided in the global attributes, such as the unit variables and structure of the corresponding data array (i.e. latitude x longitude x vertical pressure level). In addition, all files ending in "_potTpSal" provide mapped fields of potential temperature and practical salinity.

Gridded climate data from 5 Global Climate Models (GCM) of the Last Glacial Maximum (LGM) downscaled to 30 arc seconds for Europe, with links to NetCDF files

Studies on the impact of historical, current and future global change require very high-resolution climate data (less or equal 1km) as a basis for modelled responses, meaning that data from digital climate models generally require substantial rescaling. Another shortcoming of available datasets on past climate is that the effects of sea level rise and fall are not considered. Without such information, the study of glacial refugia or early Holocene plant and animal migration are incomplete if not impossible. Sea level at the last glacial maximum (LGM) was approximately 125m lower, creating substantial additional terrestrial area for which no current baseline data exist. Here, we introduce the development of a novel, gridded climate dataset for LGM that is both very high resolution (1km) and extends to the LGM sea and land mask. We developed two methods to extend current terrestrial precipitation and temperature data to areas between the current and LGM coastlines. The absolute interpolation error is less than 1°C and 0.5 °C for 98.9% and 87.8% of all pixels for the first two 1 arc degree distance zones. We use the change factor method with these newly assembled baseline data to downscale five global circulation models of LGM climate to a resolution of 1km for Europe. As additional variables we calculate 19 'bioclimatic' variables, which are often used in climate change impact studies on biological diversity. The new LGM climate maps are well suited for analysing refugia and migration during Holocene warming following the LGM.

Using ground-penetrating radar, topography and classification of vegetation to model the sediment and active layer thickness in a periglacial lake catchment, Western Greenland, link to shapefiles

The geometries of a catchment constitute the basis for distributed physically based numerical modeling of different geoscientific disciplines. In this paper results from ground-penetrating radar (GPR) measurements, in terms of a 3D model of total sediment thickness and active layer thickness in a periglacial catchment in western Greenland, is presented. Using the topography, thickness and distribution of sediments is calculated. Vegetation classification and GPR measurements are used to scale active layer thickness from local measurements to catchment scale models. Annual maximum active layer thickness varies from 0.3 m in wetlands to 2.0 m in barren areas and areas of exposed bedrock. Maximum sediment thickness is estimated to be 12.3 m in the major valleys of the catchment. A method to correlate surface vegetation with active layer thickness is also presented. By using relatively simple methods, such as probing and vegetation classification, it is possible to upscale local point measurements to catchment scale models, in areas where the upper subsurface is relatively homogenous. The resulting spatial model of active layer thickness can be used in combination with the sediment model as a geometrical input to further studies of subsurface mass-transport and hydrological flow paths in the periglacial catchment through numerical modelling.

Long-term eddy-covariance measurements from FINO2 platform above the Baltic Sea

The estimation of the carbon dioxide (CO2) fluxes above the open ocean plays an important role for the determination of the global carbon cycle. A frequently used method therefore is the eddy-covariance technique, which is based on the theory of the Prandl-layer with height-constant fluxes in the atmospheric boundary layer. To test the assumption of the constant flux layer, in 2008 measurements of turbulent heat and CO2 fluxes were started within the project Surface Ocean Processes in the Anthropocene (SOPRAN) at the research platform FINO2. The FINO2 platform is situated in the South-west of the Baltic Sea, in the tri-border region between Germany, Denmark, and Sweden. In the frame of the Research project SOPRAN, the platform was equipped with additional sensors in June 2008. A combination of 3-component sonic anemometers (USA-1) and open-path infrared gas analyzers for absolute humidity (H2O) and CO2 (LICOR 7500) were installed at a 9m long boom directed southward of the platform in two heights, at 6.8 and 13.8m above sea surface. Additionally slow temperature and humidity sensors were installed at each height. The gas analyzer systems were calibrated before the installation and worked permanently without any calibration during the first measurement period of one and a half years. The comparison with the measurements of the slow sensors showed for both instruments no significant long-term drift in H2O and CO2. Drifts on smaller time scales (in the order of days) due to the contamination with sea salt, were cleaned naturally by rain. The drift of both quantities had no influence on the fluctuation, which, in contrast to the mean values, are important for the flux estimation. All data were filtered due to spikes, rain, and the influence of the mast. The data set includes the measurements of all sensors as average over 30 minutes each for one and a half years, June 2008 to December 2009, and 10 month from November 2011 to August 2012. Additionally derived quantities for 30 minutes intervals each, like the variances for the fast-sensor variables, as well as the momentum, sensible and latent heat, and CO2 flux are presented.

High-resolution dynamical downscaling products for the North Slope of Alaska and surrounding areas, links to model result files

Climatic changes are most pronounced in northern high latitude regions. Yet, there is a paucity of observational data, both spatially and temporally, such that regional-scale dynamics are not fully captured, limiting our ability to make reliable projections. In this study, a group of dynamical downscaling products were created for the period 1950 to 2100 to better understand climate change and its impacts on hydrology, permafrost, and ecosystems at a resolution suitable for northern Alaska. An ERA-interim reanalysis dataset and the Community Earth System Model (CESM) served as the forcing mechanisms in this dynamical downscaling framework, and the Weather Research & Forecast (WRF) model, embedded with an optimization for the Arctic (Polar WRF), served as the Regional Climate Model (RCM). This downscaled output consists of multiple climatic variables (precipitation, temperature, wind speed, dew point temperature, and surface air pressure) for a 10 km grid spacing at three-hour intervals. The modeling products were evaluated and calibrated using a bias-correction approach. The ERA-interim forced WRF (ERA-WRF) produced reasonable climatic variables as a result, yielding a more closely correlated temperature field than precipitation field when long-term monthly climatology was compared with its forcing and observational data. A linear scaling method then further corrected the bias, based on ERA-interim monthly climatology, and bias-corrected ERA-WRF fields were applied as a reference for calibration of both the historical and the projected CESM forced WRF (CESM-WRF) products. Biases, such as, a cold temperature bias during summer and a warm temperature bias during winter as well as a wet bias for annual precipitation that CESM holds over northern Alaska persisted in CESM-WRF runs. The linear scaling of CESM-WRF eventually produced high-resolution downscaling products for the Alaskan North Slope for hydrological and ecological research, together with the calibrated ERA-WRF run, and its capability extends far beyond that. Other climatic research has been proposed, including exploration of historical and projected climatic extreme events and their possible connections to low-frequency sea-atmospheric oscillations, as well as near-surface permafrost degradation and ice regime shifts of lakes. These dynamically downscaled, bias corrected climatic datasets provide improved spatial and temporal resolution data necessary for ongoing modeling efforts in northern Alaska focused on reconstructing and projecting hydrologic changes, ecosystem processes and responses, and permafrost thermal regimes. The dynamical downscaling methods presented in this study can also be used to create more suitable model input datasets for other sub-regions of the Arctic.

Modeled monthly local topographic lapse rates, Pacific Northwest, USA

An emerging approach to downscaling the projections from General Circulation Models (GCMs) to scales relevant for basin hydrology is to use output of GCMs to force higher-resolution Regional Climate Models (RCMs). With spatial resolution often in the tens of kilometers, however, even RCM output will likely fail to resolve local topography that may be climatically significant in high-relief basins. Here we develop and apply an approach for downscaling RCM output using local topographic lapse rates (empirically-estimated spatially and seasonally variable changes in climate variables with elevation). We calculate monthly local topographic lapse rates from the 800-m Parameter-elevation Regressions on Independent Slopes Model (PRISM) dataset, which is based on regressions of observed climate against topographic variables. We then use these lapse rates to elevationally correct two sources of regional climate-model output: (1) the North American Regional Reanalysis (NARR), a retrospective dataset produced from a regional forecasting model constrained by observations, and (2) a range of baseline climate scenarios from the North American Regional Climate Change Assessment Program (NARCCAP), which is produced by a series of RCMs driven by GCMs. By running a calibrated and validated hydrologic model, the Soil and Water Assessment Tool (SWAT), using observed station data and elevationally-adjusted NARR and NARCCAP output, we are able to estimate the sensitivity of hydrologic modeling to the source of the input climate data. Topographic correction of regional climate-model data is a promising method for modeling the hydrology of mountainous basins for which no weather station datasets are available or for simulating hydrology under past or future climates.