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# 11
König, Rolf • Schreiner, Patrick • Dahle, Christoph
Abstract: As a convenience to users who wish to use a replacement value for C(2,0) of GFZ's GRACE/GRACE-FO RL06 GSM products, a monthly GFZ C(2,0) estimate time series is provided. These estimates are obtained from the analysis of Satellite Laser Ranging (SLR) data to the following five geodetic satellites: LAGEOS-1 and 2, Starlette, Stella and Ajisai. Starting from March 2012, the LARES satellite is added so that six geodetic satellites are included. The individual satellites are combined on normal equation level using relative weights which are based on a variance component estimation. Gravity field coefficients up to degree and order 5 plus coefficients C(6,1) and S(6,1) have been simultaneously solved together with all other (non-gravity) parameters. The background models used in the SLR analysis is consistent with the GFZ GRACE/GRACE-FO RL06 processing, including the use of the same Atmosphere-Ocean De-aliasing product AOD1B RL06. IMPORTANT REMARKS: It is advised to use these estimates to replace the C(2,0) values from the GFZ RL06 GSM files. These estimates are not intended to be used with the GRACE RL05 or earlier products. This data set is regularly updated in order to extend the time series on an operational basis. As long as the version number has not changed, all previously available data records have not been changed! See line 'UPDATE HISTORY' in the header of the data file for details about the current time span and version. SPECIAL NOTES: C(2,0) estimates are provided continuously for each month. However, the SLR data was processed in 7-day batches aligned to GPS weeks. Several weekly SLR normal equations were then accumulated to obtain a monthly solution; GPS weeks covering two calendar months were assigned to that calendar month where the majority of days within the week belong to. Thus, the beginning date for these 'monthly' solutions does not necessarily match the first day of a calendar month, but will be within a few days of that corresponding date. Moreover, in most cases, a different number of days was used for the SLR solution than for the corresponding GRACE/GRACE-FO solution. For particular periods, the GRACE/GRACE-FO solutions might span significantly less than one month or cover more than one calendar month. In these cases, a specially dedicated SLR estimate was generated which is based on approximately the same interval so that the epoch of the SLR estimate is close to the epoch of the GRACE/GRACE-FO solution. To distinguish the different cases of C(2,0) estimates mentioned above (monthly vs. specially dedicated) and to easily recognize whether a C(2,0) estimate matches an existing GRACE/GRACE-FO solution, the following flags are appended to each data record:- ' m': C(2,0) estimate represents a monthly solution for a month where no GRACE/ GRACE-FO solution is available.- 'Gm': C(2,0) estimate represents a monthly solution and a corresponding GRACE/ GRACE-FO solution is available.- 'G*': C(2,0) estimate is specially dedicated for a GRACE/GRACE-FO solution as described above; the effective period of data used is additionally provided by a string '<yymmdd>_<YYMMDD>'.
# 12
Dahle, Christoph • Murböck, Michael
Abstract: Post-processed GRACE/GRACE-FO spherical harmonic coefficients of GFZ RL06 Level-2 GSM products representing an estimate of Earth's gravity field variations during the specified timespan. Post-processing steps comprise: (1) subtraction of a long-term mean field; (2) optionally, decorrelation and smoothing with VDK filter (anisotropic filter taking the actual error covariance information of the underlying GSM coefficients into account, see Horvath et al. (2018)); (3) replacement of coefficient C20 and its uncertainty by values estimated from Satellite Laser Ranging (SLR); (4) subtraction of linear trend caused by Glacial Isostatic Adjustment (GIA) as provided by a numerical model; (5) insertion of coefficients of degree 1; and (6) removal of estimated signal with 161 days period. These coefficients represent signals caused by water mass redistribution over the continents and in the oceans. These post-processed GRACE/GRACE-FO GSM products are denoted as Level-2B products. There are multiple variants of Level-2B products available that differ by the characteristics of the anisotropic filter applied. These variants are distinguishable by the following strings in the product file names: - 'NFIL': Level-2B product is not filtered- 'VDK2': Level-2B product is filtered with VDK2- 'VDK3': Level-2B product is filtered with VDK3- 'VDK5': Level-2B product is filtered with VDK5 The individual data sets and models used during the post-processing steps mentioned above are provided as well (in the aux_data folder): - 'GRAVIS-2B_2002095-2016247_GFZOP_0600_NFIL_0001.gz': Long-term mean field calculated as unweighted average of the 156 available GFZ RL06 GSM products in the period from 2002/04 up to and including 2016/08.- 'GFZ_RL06_C20_SLR.dat': C20 time series from SLR (http://doi.org/10.5880/GFZ.GRAVIS_06_C20_SLR)- 'GRAVIS-2B_GIA_ICE5G_VILMA.gz': Model for subtraction of linear trend caused by GIA- 'GRAVIS-2B_DEG1_v01.dat': Degree-1 time series Detailed information about the product is provided in the header of the data file.
# 13
Reyer, Christopher • Chang, Jinfeng • Chen, Min • Forrest, Matt • François, Louis • (et. al.)
Abstract: The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) provides a framework for the collation of a set of consistent, multi-sector, multi-scale climate-impact simulations, based on scientifically and politically relevant historical and future scenarios. This framework serves as a basis for robust projections of climate impacts, as well as facilitating model evaluation and improvement, allowing for advanced estimates of the biophysical and socio-economic impacts of climate change at different levels of global warming. It also provides a unique opportunity to consider interactions between climate impacts across sectors. ISIMIP2b is the second simulation round of the second phase of ISIMIP. ISIMIP2b considers impacts on different sectors at the global and regional scales: water, fisheries and marine ecosystems, energy supply and demand, forests, biomes, agriculture, agro-economic modeling, terrestrial biodiversity, permafrost, coastal infrastructure, health and lakes. ISIMIP2b simulations focus on separating the impacts and quantifying the pure climate change effects of historical warming (1861-2005) compared to pre-industrial reference levels (1661-1860); and on quantifying the future (2006-2099) and extended future (2006-2299) impact projections accounting for low (RCP2.6), mid-high (RCP6.0) and high (RCP8.5) greenhouse gas emissions, assuming either constant (year 2005) or dynamic population, land and water use and -management, economic development, bioenergy demand, and other societal factors. The scientific rationale for the scenario design is documented in Frieler et al. (2017). The ISIMIP2b bias-corrected observational climate input data (Lange, 2018; Frieler et al., 2017) consists of an updated version of the observational dataset EWEMBI at daily temporal and 0.5° spatial resolution, which better represents the CMIP5 GCM ensemble in terms of both spatial model resolution and equilibrium climate sensitivity. The bias correction methods (Lange, 2018; Frieler et al., 2017; Lange, 2016) were applied to CMIP5 output of GDFL-ESM2M, HadGEM2-ES, IPSL-CM5A-LP and MIROC5. Access to the input data for the impact models, and further information on bias correction methods, is provided through a central ISIMIP archive (see https://www.isimip.org/gettingstarted/isimip2b-bias-correction). This entry refers to the ISIMIP2b simulation data from eight global vegetation (biomes) models:CARAIBCLM4.5,DLEM,LPJmL,ORCHIDEE,VEGAS,VISIT,LPJ-GUESS ----------------------------------------------------------------------------The Inter-Sectoral Impact Model Intercomparison Project (ISIMIP) simulation data is under continuous review and improvement, and updates are thus likely to happen. All changes and caveats are documented under https://www.isimip.org/outputdata/output-data-changelog/ (ISIMIP Changelog) and https://www.isimip.org/outputdata/dois-isimip-data-sets/ (ISIMIP DOI publications).----------------------------------------------------------------------------
The ISIMIP2b biomes outputs are based on simulations from 8 global vegetation (biomes) models (see listing) according to the ISIMIP2b protocol (https://www.isimip.org/protocol/#isimip2b). The biomes models simulate biogeochemical processes, biogeography and ecosystem dynamics of natural vegetation and managed lands based on soil, climate and land-use information. A more detailed description of the models and model-specific amendments of the protocol are available here: https://www.isimip.org/impactmodels/
# 14
van Dongen, Renee • Scherler, Dirk • Wittmann, Hella • von Blanckenburg, Friedhelm
Abstract: Concentrations of in-situ-produced cosmogenic 10Be in river sediment are widely used to estimate catchment-average denudation rates. Typically, the 10Be concentrations are measured in the sand fraction of river sediment. However, the grain size of bedload sediment in most bedrock rivers covers a much wider range. Where 10Be concentrations depend on grain size, denudation rate estimates based on the sand fraction alone are potentially biased. To date, knowledge about catchment attributes that may induce grain-size-dependent 10Be concentrations is incomplete or has only been investigated in modelling studies. Here we present an empirical study on the occurrence of grain-size-dependent 10Be concentrations and the potential controls of hillslope angle, precipitation, lithology, and abrasion. We first conducted a study focusing on the sole effect of precipitation in four granitic catchments located on a climate gradient in the Chilean Coastal Cordillera. We found that observed grain size dependencies of 10Be concentrations in the most-arid and most-humid catchments could be explained by the effect of precipitation on both the scouring depth of erosion processes and the depth of the mixed soil layer. Analysis of a global dataset of published 10Be concentrations in different grain sizes (n=73 catchments) – comprising catchments with contrasting hillslope angles, climate, lithology, and catchment size – revealed a similar pattern. Lower 10Be concentrations in coarse grains (defined as “negative grain size dependency”) emerge frequently in catchments which likely have thin soil and where deep-seated erosion processes (e.g. landslides) excavate grains over a larger depth interval. These catchments include steep (> 25°) and humid catchments (> 2000mm yr-1). Furthermore, we found that an additional cause of negative grain size dependencies may emerge in large catchments with weak lithologies and long sediment travel distances (> 2300–7000 m, depending on lithology) where abrasion may lead to a grain size distribution that is not representative for the entire catchment. The results of this study can be used to evaluate whether catchment-average denudation rates are likely to be biased in particular catchments. Samples from the Chilean Coastal Cordillera were processed in the Helmholtz Laboratory for the Geochemistry of the Earth Surface (HELGES). 10Be/9Be ratios were measured at the University of Cologne and normalized to the KN01-6-2 and KN01-5-3 standards. Denudation rates were calculated using a time-independent scaling scheme according to Lal (1991) and Stone (2002) (St scaling scheme) and the SLHL production rate of 4.01 at g-1 yr-1 as reported by Phillips et al. (2016) The global compilation exists of studies that measured 10Be concentrations in different grain sizes from the same sample location. We only included river basins of <5000 km2 which measured 10Be concentrations in at least one sand-sized fraction <2 mm and at least one coarser fraction >2 mm. Catchment parameters have been recalculated using a 90-m SRTM DEM. The data are presented in Excel and csv tables. Table S1 describes the characteristics of the samples catchments, Table S2 includes the grain size dependent 10Be-concentrations measured during this study and Table 3 the global compilation of grain size dependent 10Be-concentrations. All samples of this study (the Chilean Coastal Cordillera) are assigned with International Geo Sample Numbers (IGSN). The IGSN links are included in Table S2 and in the Related References Section on the DOI Landing Page. The data are described in detail in the data description file and in van Dongen et al. (2018) to which they are supplementary material to.
# 15
Böhm, Christoph
Abstract: This data set contains cloud base heights derived from the Multi-angle Imaging SpectroRadiometer (MISR) which is placed on the Terra satellite. The cloud base heights are provided globally on a 0.25x0.25 degree longitude latitude grid for a three year period (2007-2009). Daily files comprise retrievals from the illuminated portion of about 14 Terra revolutions around the Earth. The newly developed MISR cloud base height (MIBase) algorithm (Boehm et al., in review 2018, DOI:10.5194/amt-2018-317) retrieves the cloud base height from the MISR Level 2TC Cloud Product (MIL2TCSP). The MIL2TCSP product provides the cloud top height and a stereo-derived cloud mask at 1.1 km horizontal resolution. The MIBase algorithm evaluates the ensemble of the cloud top height retrievals for each grid box in case some cloud gaps occur within the area and a sufficient amount of cloud top height retrievals marked high confidence cloud according to the stereo-derived cloud mask is provided. The 15th percentile of the cloud top height distribution within the respective grid box yields the cloud base height. The algorithm has been calibrated and validated against ground-based ceilometer measurements across the continental USA over the course of two years. An important strength of MIBase is the geometric approach which is applied to create the cloud top height product from MISR measurements. Neither a calibration nor auxiliary data are necessary to obtain the cloud top height product which is the starting point for the MIBase algorithm. In consequence, retrievals are possible over all kinds of terrain even above ice. A disadvantage is the threshold height which MISR requires to create the stereo-derived cloud mask. Therefore, depending on the terrain variability in the vicinity of the measurement, MIBase is not capable of deriving cloud base heights below at least 560 m (flat terrain). The algorithm requires a broken cloud scene. For complete overcast within the chosen MIBase cell, the cloud base height cannot be retrieved. Therefore, climatologies derived from this algorithm would be biased towards cloud types for which MISR is able to observe the surface through cloud gaps. The given cloud base height estimates represent the situation during morning hours since MISR is carried on board the Terra satellite in a Sun-synchronous orbit with an equatorial overpass time at around 10:30 local solar time. Data gaps (due to unavailable input data): 2008-10-01 - 2008-10-15 and 2008-12-20 - 2008-12-22
# 16
Reyers, Mark
Abstract: This file contains simulated daily accumulated total precipitation (convective + non-convective) from the Regional Climate Model WRF for the Atacama Desert. The horizontal resolution is 10km, output is stored for yearly time frames as NETCDF, given time is the end of the accumulation period in UTC. A double one-way nesting has been performed using ERA-Interim as boundary conditions.
# 17
Blanchet, Cécile L.
Abstract: The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. It was compiled to help assessing sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools (see supplemental references). The database has been built by incorporating data from the literature and the SedDB database and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, a specific attention has been devoted to provide geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates, with different precision standard) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help splitting the dataset in specific categories. We defined categories associated with the sample location ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores.csv"). The dataset will be updated bi-annually and might be extended to reach a global geographical extent and/or add other type of samples. This dataset contains two csv tables: "Dataset_Nd_Sr_isotopes.csv" and "Age_determination_Sediment_Cores.csv". "Dataset_Nd_Sr_isotopes.csv" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores.csv" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A human readable data description is provided in portable document format, as well. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (see supplemental references).
# 18
Blanchet, Cécile L.
Abstract: The database presented here contains radiogenic neodymium and strontium isotope ratios measured on both terrestrial and marine sediments. It was compiled to help assessing sediment provenance and transport processes for various time intervals. This can be achieved by either mapping sediment isotopic signature and/or fingerprinting source areas using statistical tools (e.g. Blanchet, 2018b, 2018a). The database has been built by incorporating data from the literature and the SedDB database and harmonizing the metadata, especially units and geographical coordinates. The original data were processed in three steps. Firstly, a specific attention has been devoted to provide geographical coordinates to each sample in order to be able to map the data. When available, the original geographical coordinates from the reference (generally DMS coordinates, with different precision standard) were transferred into the decimal degrees system. When coordinates were not provided, an approximate location was derived from available information in the original publication. Secondly, all samples were assigned a set of standardized criteria that help splitting the dataset in specific categories. We defined categories associated with the sample location ("Region", "Sub-region", "Location", which relate to location at continental to city/river scale) or with the sample types (terrestrial samples – “aerosols”, “soil sediments”, “river sediments”, “rocks” - or marine samples –“marine sediment” or “trap sample”). Thirdly, samples were discriminated according to their deposition age, which allowed to compute average values for specific time intervals (see attached table "Age_determination_Sediment_Cores_V2.txt"). A first version of the database was published in September 2018 and presented data for the African sector. A second version was published in April 2019, in which the dataset has been extended to reach a global extent. The dataset will be further updated bi-annually to increase the geographical resolution and/or add other type of samples. This dataset consists of two tab separated tables: "Dataset_Nd_Sr_isotopes_V2.txt" and "Age_determination_Sediment_Cores_V2.txt". "Dataset_Nd_Sr_isotopes_V2.txt" contains the assembled dataset of marine and terrestrial Nd and/or Sr concentration and isotopes, together with sorting criteria and geographical locations. "Age_determination_Sediment_Cores_V2.txt" contains all background information concerning the determination of the isotopic signature of specific time intervals (depth interval, number of samples, mean and standard deviation). Column headers are explained in respective metadata comma-separated files. A full reference list is provided in the file “References_Database_Nd_Sr_isotopes_V2.rtf”. Finally, R code for mapping the data and running statistical analyses is also available for this dataset (Blanchet, 2018b, 2018a).
# 19
Broerse, Taco • Norder, Ben • Picken, Stephen • Govers, Rob • Willingshofer, Ernst • (et. al.)
Abstract: This dataset provides strain and strain rate data on mixtures of plasticine, silicone oils and iron powder that has been used in slab break-of analogue experiments in the Tectonic Laboratory (TecLab) at Utrecht University (NL) as an analogue for viscously deforming lithosphere. The materials have been analyzed in a creep and recovery test, applying a parallel plate setup using an AR-G2 rheometer (by TA Instruments). The materials can in general be described as viscoelastic materials with a power-law rheology (see previous work on plasticine-silicone polymer mixtures Weijermars [1986], Sokoutis [1987], Boutelier et al. [2008]). For a couple of the tested materials we find a complementary Newtonian behavior at the low end of the tested stress levels, with a transition to power-law behavior at increasing stress. Furthermore, the materials exhibit elastic and anelastic (recoverable) deformation. The corresponding paper (Broerse et al., 2018) describes the rheology, while this supplement describes the raw data and important details of the measurement setup. The raw data concerns mostly (uncorrected) strain and strain rate data. The rheometry has been performed at the Advanced Soft Matter group at the Department of Chemical Engineering, Delft University of Technology, The Netherlands.
# 20
Caricchi, Chiara • Lucchi, Renata Giulia • Sagnotti, Leonardo • Macrì, Patrizia • Di Roberto, Alessio • (et. al.)
Abstract: This data publication includes the paleomagnetic and rock magnetic dataset from two Calypso giant piston cores collected at the crest of the Bellsund (GS191-01PC) and Isfjorden (GS191-02PC) sediment drifts during the Eurofleets-2 PREPARED cruise, on board the R/V G.O. Sars (Lucchi et al., 2014). These sediments drift are located on the eastern side of the Fram Strait (western Spitsbergen margin).The dataset gave the opportunity to define the behavior of past geomagnetic field at high latitude and to constrain the palaeoclimatic events that occurred in a time framework spanning Marine Isotope Stage (MIS) 3 to Holocene (Caricchi et al., in press). The data are provided as raw data in .dat format and interpreted data in .xlx and tab-delimited text formats. The raw data files can be opened using a text-editor, MS Excel or equivalent software. The interpreted data are presented as a metadata table with definitions of the column heads and 5 individual tables with the content: - Metadata: definition of columns heads - Rock Magnetic-Paleomag Data 01: down-core variation of rock magnetic and paleomagnetic parameters [k (10E-05 SI); ARM (A/m); ARM/k (A/m); MDF (mT); ΔGRM/ΔNRM; NRM (A/m); MAD (°); Incl PCA (°); Decl PCA (°)] for Core GS191-01PC - Rock Magnetic-Paleomag Data 02: down core variation of rock magnetic and paleomagnetic data [k (10E-05 SI); ARM (A/m); ARM/k (A/m); MDF (mT); ΔGRM/ΔNRM; NRM (A/m); MAD (°); Incl PCA (°) Decl PCA (°)] for Core GS191-02PC - Cores Correlation: Depth of Core GS191-02PC and depth of Core GS191-02PC correlated to Core GS191-01PC, NRM (A/m); ARM(A/m) and RPI down-core variations for core GS191-02PC; Depth of Core GS191-01PC NRM (A/m); ARM(A/m) and RPI down-core variations for core GS191-01PC; tie points values. - Age Model 01: age model for Core GS191-01PC - Age Model 02: age model for Core GS191-01PC
Raw data were measured at the paleomagnetic laboratory of INGV and have been analysed by DAIE software (Sagnotti, 2013). The obtained along-core variation of rock magnetic and paleomagnetic trends have been integrated with the distribution of characteristic lithofacies and the 14C ages in order to define high-resolution correlation between the cores. Core to core correlation has been computed by means of StratFit software (Sagnotti and Caricchi, 2018). The correlation process is based on the Excel forecast function and linear regression between subsequent couples of selected tie-points. This process results in the estimate of the equivalent depth of the correlated curve (core GS191-02 PC) into the depth scale of the “master” curve (GS191-01PC). Using the same method and taking into account the constraints provided by the calibrated radiocarbon ages and the litostratigraphic information, PREPARED cores have been compared to RPI and inclination variations expected at the core sites according to global geomagnetic field models (SHA.DIF.14k of Pavón-Carrasco et al., 2014; GGF.110k of Panovska et al., 2018).
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