Showing papers in "Earth System Science Data in 2019"
École Normale Supérieure1, University of Exeter2, Norwich Research Park3, Alfred Wegener Institute for Polar and Marine Research4, Wageningen University and Research Centre5, University of Groningen6, Max Planck Society7, Ludwig Maximilian University of Munich8, Commonwealth Scientific and Industrial Research Organisation9, Centre national de la recherche scientifique10, Stanford University11, Karlsruhe Institute of Technology12, Cooperative Institute for Marine and Atmospheric Studies13, Atlantic Oceanographic and Meteorological Laboratory14, Bjerknes Centre for Climate Research15, Geophysical Institute, University of Bergen16, Japan Agency for Marine-Earth Science and Technology17, University of Maryland, College Park18, National Institute of Water and Atmospheric Research19, National Oceanic and Atmospheric Administration20, Appalachian State University21, Flanders Marine Institute22, Augsburg College23, ETH Zurich24, Leibniz Institute of Marine Sciences25, University of East Anglia26, Woods Hole Research Center27, University of Illinois at Urbana–Champaign28, University of Hong Kong29, Utrecht University30, Netherlands Environmental Assessment Agency31, University of Paris32, University of Tasmania33, Hobart Corporation34, University of Bern35, National Center for Atmospheric Research36, University of Reading37, Cooperative Institute for Research in Environmental Sciences38, National Institute for Environmental Studies39, Russian Academy of Sciences40, Goddard Space Flight Center41, Leibniz Institute for Baltic Sea Research42, Princeton University43, Met Office44, Lund University45, Auburn University46, Food and Agriculture Organization47, VU University Amsterdam48
TL;DR: In this article, the authors describe data sets and methodology to quantify the five major components of the global carbon budget and their uncertainties, including emissions from land use and land use change, and show that the difference between the estimated total emissions and the estimated changes in the atmosphere, ocean, and terrestrial biosphere is a measure of imperfect data and understanding of the contemporary carbon cycle.
Abstract: . Accurate assessment of anthropogenic carbon dioxide ( CO2 ) emissions and
their redistribution among the atmosphere, ocean, and terrestrial biosphere
– the “global carbon budget” – is important to better understand the
global carbon cycle, support the development of climate policies, and
project future climate change. Here we describe data sets and methodology to
quantify the five major components of the global carbon budget and their
uncertainties. Fossil CO2 emissions ( EFF ) are based on energy
statistics and cement production data, while emissions from land use change
( ELUC ), mainly deforestation, are based on land use and land use change
data and bookkeeping models. Atmospheric CO2 concentration is measured
directly and its growth rate ( GATM ) is computed from the annual changes
in concentration. The ocean CO2 sink ( SOCEAN ) and terrestrial
CO2 sink ( SLAND ) are estimated with global process models
constrained by observations. The resulting carbon budget imbalance
( BIM ), the difference between the estimated total emissions and the
estimated changes in the atmosphere, ocean, and terrestrial biosphere, is a
measure of imperfect data and understanding of the contemporary carbon
cycle. All uncertainties are reported as ±1σ . For the last
decade available (2009–2018), EFF was 9.5±0.5 GtC yr −1 ,
ELUC 1.5±0.7 GtC yr −1 , GATM 4.9±0.02 GtC yr −1 ( 2.3±0.01 ppm yr −1 ), SOCEAN 2.5±0.6 GtC yr −1 , and SLAND 3.2±0.6 GtC yr −1 , with a budget
imbalance BIM of 0.4 GtC yr −1 indicating overestimated emissions
and/or underestimated sinks. For the year 2018 alone, the growth in EFF was
about 2.1 % and fossil emissions increased to 10.0±0.5 GtC yr −1 , reaching 10 GtC yr −1 for the first time in history,
ELUC was 1.5±0.7 GtC yr −1 , for total anthropogenic
CO2 emissions of 11.5±0.9 GtC yr −1 ( 42.5±3.3 GtCO2 ). Also for 2018, GATM was 5.1±0.2 GtC yr −1 ( 2.4±0.1 ppm yr −1 ), SOCEAN was 2.6±0.6 GtC yr −1 , and SLAND was 3.5±0.7 GtC yr −1 , with a BIM of 0.3 GtC. The global atmospheric CO2 concentration reached 407.38±0.1 ppm averaged over 2018. For 2019, preliminary data for the first 6–10 months indicate a reduced growth in EFF of +0.6 % (range of
−0.2 % to 1.5 %) based on national emissions projections for China, the
USA, the EU, and India and projections of gross domestic product corrected
for recent changes in the carbon intensity of the economy for the rest of
the world. Overall, the mean and trend in the five components of the global
carbon budget are consistently estimated over the period 1959–2018, but
discrepancies of up to 1 GtC yr −1 persist for the representation of
semi-decadal variability in CO2 fluxes. A detailed comparison among
individual estimates and the introduction of a broad range of observations
shows (1) no consensus in the mean and trend in land use change emissions
over the last decade, (2) a persistent low agreement between the different
methods on the magnitude of the land CO2 flux in the northern
extra-tropics, and (3) an apparent underestimation of the CO2
variability by ocean models outside the tropics. This living data update
documents changes in the methods and data sets used in this new global
carbon budget and the progress in understanding of the global carbon cycle
compared with previous publications of this data set (Le Quere et
al., 2018a, b, 2016, 2015a, b, 2014, 2013). The data generated by
this work are available at https://doi.org/10.18160/gcp-2019 (Friedlingstein
et al., 2019).
1,414 citations
TL;DR: Peng et al. as mentioned in this paper presented a 0.5 × 1.5 cm dataset with a bilinear interpolation method for air temperatures at 2 cm (minimum, maximum, and mean proxy monthly temperatures, TMPs) and precipitation (PRE) for China.
Abstract: . High-spatial-resolution and long-term climate data are
highly desirable for understanding climate-related natural processes. China
covers a large area with a low density of weather stations in some (e.g.,
mountainous) regions. This study describes a 0.5 ′ ( ∼ 1 km)
dataset of monthly air temperatures at 2 m (minimum, maximum, and mean proxy monthly temperatures, TMPs)
and precipitation (PRE) for China in the period of 1901–2017. The dataset
was spatially downscaled from the 30 ′ Climatic Research Unit (CRU) time
series dataset with the climatology dataset of WorldClim using delta spatial
downscaling and evaluated using observations collected in 1951–2016 by 496
weather stations across China. Prior to downscaling, we evaluated the
performances of the WorldClim data with different spatial resolutions and
the 30 ′ original CRU dataset using the observations, revealing that their
qualities were overall satisfactory. Specifically, WorldClim data exhibited
better performance at higher spatial resolution, while the 30 ′ original CRU
dataset had low biases and high performances. Bicubic, bilinear, and
nearest-neighbor interpolation methods employed in downscaling processes
were compared, and bilinear interpolation was found to exhibit the best
performance to generate the downscaled dataset. Compared with the
evaluations of the 30 ′ original CRU dataset, the mean absolute error of the new dataset (i.e., of the 0.5 ′ dataset downscaled by bilinear interpolation) decreased by 35.4 %–48.7 % for TMPs and by 25.7 % for PRE. The root-mean-square error decreased by 32.4 %–44.9 % for TMPs and by 25.8 % for PRE. The Nash–Sutcliffe efficiency coefficients increased by
9.6 %–13.8 % for TMPs and by 31.6 % for PRE, and correlation
coefficients increased by 0.2 %–0.4 % for TMPs and by 5.0 % for PRE. The new dataset could provide detailed climatology data and annual trends of all climatic variables across China, and the results could be evaluated well using observations at the station. Although the new dataset was not evaluated before 1950 owing to data unavailability, the quality of the new
dataset in the period of 1901–2017 depended on the quality of the original
CRU and WorldClim datasets. Therefore, the new dataset was reliable, as the
downscaling procedure further improved the quality and spatial resolution of
the CRU dataset and was concluded to be useful for investigations related
to climate change across China. The dataset presented in this article has
been published in the Network Common Data Form (NetCDF) at
https://doi.org/10.5281/zenodo.3114194 for precipitation (Peng,
2019a) and https://doi.org/10.5281/zenodo.3185722 for air temperatures at 2 m
(Peng, 2019b) and includes 156 NetCDF files compressed in zip
format and one user guidance text file.
833 citations
TL;DR: The European Space Agency's Climate Change Initiative for Soil Moisture (ESA CCI SM) merging algorithm generates consistent and quality-controlled long-term (1978-2018) climate data records for soil moisture, which serves thousands of scientists and data users worldwide as discussed by the authors.
Abstract: . The European Space Agency's Climate Change Initiative
for Soil Moisture (ESA CCI SM) merging algorithm generates consistent
quality-controlled long-term (1978–2018) climate data records for soil
moisture, which serves thousands of scientists and data users worldwide. It
harmonises and merges soil moisture retrievals from multiple satellites into
(i) an active-microwave-based-only product, (ii) a passive-microwave-based-only product and (iii) a combined
active–passive product, which are sampled to daily global images on a
0.25 ∘ regular grid. Since its first release in 2012 the algorithm has
undergone substantial improvements which have so far not been thoroughly
reported in the scientific literature. This paper fills this gap by reviewing
and discussing the science behind the three major ESA CCI SM merging
algorithms, versions 2 ( https://doi.org/10.5285/3729b3fbbb434930bf65d82f9b00111c ;
Wagner et al. , 2018 ), 3 ( https://doi.org/10.5285/b810601740bd4848b0d7965e6d83d26c ;
Dorigo et al. , 2018 ) and 4 ( https://doi.org/10.5285/dce27a397eaf47e797050c220972ca0e ;
Dorigo et al. , 2019 ), and provides an outlook on the expected improvements
planned for the next algorithm, version 5.
522 citations
335 citations
TL;DR: The Global Fire Atlas as mentioned in this paper is a new dataset that tracks the dynamics of individual wildfires to determine the timing and location of ignitions, fire size and duration and daily expansion, fire line length, speed, and direction of spread.
Abstract: . Natural and human-ignited fires affect all major
biomes, altering ecosystem structure, biogeochemical cycles and atmospheric
composition. Satellite observations provide global data on spatiotemporal
patterns of biomass burning and evidence for the rapid changes in global fire
activity in response to land management and climate. Satellite imagery also
provides detailed information on the daily or sub-daily position of fires
that can be used to understand the dynamics of individual fires. The Global
Fire Atlas is a new global dataset that tracks the dynamics of individual
fires to determine the timing and location of ignitions, fire size and
duration, and daily expansion, fire line length, speed, and direction of spread.
Here, we present the underlying methodology and Global Fire Atlas results for
2003–2016 derived from daily moderate-resolution (500 m) Collection 6
MCD64A1 burned-area data. The algorithm identified 13.3 million individual
fires over the study period, and estimated fire perimeters were in good
agreement with independent data for the continental United States. A small
number of large fires dominated sparsely populated arid and boreal
ecosystems, while burned area in agricultural and other human-dominated
landscapes was driven by high ignition densities that resulted in numerous
smaller fires. Long-duration fires in boreal regions and natural
landscapes in the humid tropics suggest that fire season length exerts a
strong control on fire size and total burned area in these areas. In arid
ecosystems with low fuel densities, high fire spread rates resulted in
large, short-duration fires that quickly consumed available fuels.
Importantly, multiday fires contributed the majority of burned area in all
biomass burning regions. A first analysis of the largest, longest and
fastest fires that occurred around the world revealed coherent regional
patterns of extreme fires driven by large-scale climate forcing. Global Fire
Atlas data are publicly available through http://www.globalfiredata.org (last access: 9 August 2018) and https://doi.org/10.3334/ORNLDAAC/1642 , and
individual fire information and summary data products provide new
information for benchmarking fire models within ecosystem and Earth system
models, understanding vegetation–fire feedbacks, improving global emissions
estimates, and characterizing the changing role of fire in the Earth system.
333 citations
TL;DR: A set of large-scale gridded datasets representing population counts or densities is presented, compares and discusses and focuses on data properties, methodological approaches and relative quality aspects that are important to fully understand the characteristics of the data with regard to the intended uses.
Abstract: . Population data represent an essential component in
studies focusing on human–nature interrelationships, disaster risk
assessment and environmental health. Several recent efforts have produced
global- and continental-extent gridded population data which are becoming
increasingly popular among various research communities. However, these data
products, which are of very different characteristics and based on different
modeling assumptions, have never been systematically reviewed and compared,
which may impede their appropriate use. This article fills this gap and
presents, compares and discusses a set of large-scale (global and
continental) gridded datasets representing population counts or densities.
It focuses on data properties, methodological approaches and relative
quality aspects that are important to fully understand the characteristics
of the data with regard to the intended uses. Written by the data producers
and members of the user community, through the lens of the “fitness for
use” concept, the aim of this paper is to provide potential data users with
the knowledge base needed to make informed decisions about the
appropriateness of the data products available in relation to the target
application and for critical analysis.
319 citations
TL;DR: Ghiggi et al. as discussed by the authors introduced a global gridded monthly reconstruction of runoff covering the period from 1902 to 2014, and trained a machine learning algorithm that predicts monthly runoff rates based on antecedent precipitation and temperature from an atmospheric reanalysis.
Abstract: . Freshwater resources are of high societal relevance, and understanding their
past variability is vital to water management in the context of ongoing
climate change. This study introduces a global gridded monthly
reconstruction of runoff covering the period from 1902 to 2014. In situ
streamflow observations are used to train a machine learning algorithm that
predicts monthly runoff rates based on antecedent precipitation and
temperature from an atmospheric reanalysis. The accuracy of this
reconstruction is assessed with cross-validation and compared with an
independent set of discharge observations for large river basins. The
presented dataset agrees on average better with the streamflow observations
than an ensemble of 13 state-of-the art global hydrological model runoff
simulations. We estimate a global long-term mean runoff of 38 452 km 3 yr −1 in agreement with previous assessments. The temporal coverage of
the reconstruction offers an unprecedented view on large-scale features of
runoff variability in regions with limited data coverage, making it an
ideal candidate for large-scale hydro-climatic process studies, water
resource assessments, and evaluating and refining existing hydrological
models. The paper closes with example applications fostering the
understanding of global freshwater dynamics, interannual variability,
drought propagation and the response of runoff to atmospheric
teleconnections. The GRUN dataset is available at
https://doi.org/10.6084/m9.figshare.9228176
(Ghiggi et al.,
2019).
301 citations
TL;DR: The development history and future plans of ICGEM are presented, including those from the 1960s to the 1990s, as well as the most recent ones, which have been developed using data from dedicated satellite gravity missions, CHAMP, GRACE, GOCE, advanced processing methodologies, and additional data sources such as satellite altimetry and terrestrial gravity.
Abstract: . The International Centre for Global Earth Models (ICGEM,
http://icgem.gfz-potsdam.de/ , last access: 6 May 2019) hosted at the
GFZ German Research Centre for Geosciences (GFZ) is one of the five services
coordinated by the International Gravity Field Service (IGFS) of the
International Association of Geodesy (IAG). The goal of the ICGEM service is
to provide the scientific community with a state-of-the-art archive of static
and temporal global gravity field models of the Earth, and develop and
operate interactive calculation and visualization services of gravity field
functionals on user-defined grids or at a list of particular points via its
website. ICGEM offers the largest collection of global gravity field models,
including those from the 1960s to the 1990s, as well as the most recent ones,
which have been developed using data from dedicated satellite gravity
missions, CHAMP, GRACE, GOCE, advanced processing methodologies, and
additional data sources such as satellite altimetry and terrestrial gravity.
The global gravity field models have been collected from different
institutions at international level and after a validation process made
publicly available in a standardized format with DOI numbers assigned through
GFZ Data Services. The development and maintenance of such a unique platform
is crucial for the scientific community in geodesy, geophysics, oceanography,
and climate research. In this article, we present the development history and
future plans of ICGEM and its current products and essential services. We
present the ICGEM's data by means of Earth's static, temporal, and
topographic gravity field models as well as the gravity field models of other
celestial bodies together with examples produced by the ICGEM's calculation
and 3-D visualization services and give an insight into how the ICGEM service
can additionally contribute to the needs of research and society.
262 citations
TL;DR: Humphrey and Gudmundsson as discussed by the authors used a statistical model trained with GRACE observations to reconstruct past climate-driven changes in terrestrial water storage (TWS) from historical and near-real-time meteorological datasets at daily and monthly scales.
Abstract: . The amount of water stored on continents is an important constraint for
water mass and energy exchanges in the Earth system and exhibits large
inter-annual variability at both local and continental scales. From 2002 to
2017, the satellites of the Gravity Recovery and Climate Experiment
(GRACE) mission have observed changes in terrestrial water storage (TWS) with an
unprecedented level of accuracy. In this paper, we use a statistical model
trained with GRACE observations to reconstruct past climate-driven changes
in TWS from historical and near-real-time meteorological datasets at daily
and monthly scales. Unlike most hydrological models which represent water
reservoirs individually (e.g., snow, soil moisture) and usually provide
a single model run, the presented approach directly reconstructs total TWS
changes and includes hundreds of ensemble members which can be used to
quantify predictive uncertainty. We compare these data-driven TWS estimates
with other independent evaluation datasets such as the sea level budget,
large-scale water balance from atmospheric reanalysis, and in situ streamflow
measurements. We find that the presented approach performs overall as well
or better than a set of state-of-the-art global hydrological models (Water
Resources Reanalysis version 2). We provide reconstructed TWS anomalies at a
spatial resolution of 0.5 ∘ , at both daily and monthly scales over
the period 1901 to present, based on two different GRACE products and three
different meteorological forcing datasets, resulting in six reconstructed TWS
datasets of 100 ensemble members each. Possible user groups and applications
include hydrological modeling and model benchmarking, sea level budget
studies, assessments of long-term changes in the frequency of droughts, the
analysis of climate signals in geodetic time series, and the interpretation
of the data gap between the GRACE and GRACE Follow-On missions. The
presented dataset is published at https://doi.org/10.6084/m9.figshare.7670849 (Humphrey and Gudmundsson, 2019) and updates will be
published regularly.
238 citations
TL;DR: In this paper, the authors report the first global gridded maps of geological CH4 sources, including emission and isotopic data, including the four main categories of natural CH4 emission: (a) terrestrial hydrocarbon macro-seeps, includingmud volcanoes, (b) submarine (offshore) seeps, (c) diffuse micro-seepage and (d) geothermal manifestations, and (e) terrestrial sources.
Abstract: Methane ( CH4 ) is a powerful greenhouse gas, whose
natural and anthropogenic emissions contribute ∼20 % to global
radiative forcing Its atmospheric budget (sources and sinks), however, has
large uncertainties Inverse modelling, using atmospheric CH4
trends, spatial gradients and isotopic source signatures, has recently
improved the major source estimates and their spatial–temporal variation
Nevertheless, isotopic data lack CH4 source representativeness for
many sources, and their isotopic signatures are affected by incomplete
knowledge of the spatial distribution of some sources, especially those
related to fossil (radiocarbon-free) and microbial gas This gap is
particularly wide for geological CH4 (geo- CH4 ) seepage,
ie the natural degassing of hydrocarbons from the Earth's crust While
geological seepage is widely considered a major source of atmospheric
CH4 , it has been largely neglected in 3-D inverse CH4
budget studies given the lack of detailed a priori gridded emission maps
Here, we report for the first time global gridded maps of geological
CH4 sources, including emission and isotopic data The 1 ∘ × 1 ∘ maps include the four main categories of natural
geo- CH4 emission: (a) onshore hydrocarbon macro-seeps, including
mud volcanoes, (b) submarine (offshore) seeps, (c) diffuse microseepage and
(d) geothermal manifestations An inventory of point sources and area sources
was developed for each category, defining areal distribution (activity),
CH4 fluxes (emission factors) and its stable C isotope composition
( δ13C - CH4 ) These parameters were determined
considering geological factors that control methane origin and seepage (eg
petroleum fields, sedimentary basins, high heat flow regions, faults,
seismicity) The global geo-source map reveals that the regions with the
highest CH4 emissions are all located in the Northern Hemisphere,
in North America, in the Caspian region, in Europe and in the East Siberian
Arctic Shelf The globally gridded CH4 emission estimate
(37 Tg yr −1 exclusively based on data and modelling specifically
targeted for gridding, and 43–50 Tg yr −1 when extrapolated to also
account for onshore and submarine seeps with no location specific
measurements available) is compatible with published ranges derived using
top-down and bottom-up procedures Improved activity and emission factor data
allowed previously published mud volcanoes and microseepage emission
estimates to be refined The emission-weighted global mean
δ13C - CH4 source signature of all geo- CH4
source categories is about −49 ‰ This value
is significantly lower than those attributed so far in inverse studies to
fossil fuel sources ( −44 ‰) and geological seepage
( −38 ‰) It is expected that using this updated, more
13C -depleted, isotopic signature in atmospheric modelling will
increase the top-down estimate of the geological CH4 source The
geo- CH4 emission grid maps can now be used to improve atmospheric
CH4 modelling, thereby improving the accuracy of the fossil fuel
and microbial components Grid csv (comma-separated values) files are
available at https://doiorg/1025925/4j3f-he27
186 citations
TL;DR: Li et al. as mentioned in this paper used multiple altimetric missions and Landsat satellite data to create high-temporal-resolution lake water level and storage change time series at weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km 2 and 2 lakes smaller than 100 km 2 ) on the Tibetan Plateau (TP).
Abstract: . The Tibetan Plateau (TP), known as Asia's water tower, is
quite sensitive to climate change, which is reflected by changes in hydrologic state
variables such as lake water storage. Given the extremely limited ground
observations on the TP due to the harsh environment and complex terrain, we
exploited multiple altimetric missions and Landsat satellite data to create
high-temporal-resolution lake water level and storage change time series at
weekly to monthly timescales for 52 large lakes (50 lakes larger than 150 km 2 and 2 lakes larger than 100 km 2 ) on the TP during 2000–2017. The data sets are available online at https://doi.org/10.1594/PANGAEA.898411 (Li et al., 2019). With
Landsat archives and altimetry data, we developed water levels from lake
shoreline positions (i.e., Landsat-derived water levels) that cover the
study period and serve as an ideal reference for merging multisource lake
water levels with systematic biases being removed. To validate the
Landsat-derived water levels, field experiments were carried out in two
typical lakes, and theoretical uncertainty analysis was performed based on
high-resolution optical images (0.8 m) as well. The RMSE of the
Landsat-derived water levels is 0.11 m compared with the in situ
measurements, consistent with the magnitude from theoretical analysis
(0.1–0.2 m). The accuracy of the Landsat-derived water levels that can be
derived in relatively small lakes is comparable with most altimetry data.
The resulting merged Landsat-derived and altimetric lake water levels can
provide accurate information on multiyear and short-term monitoring of lake
water levels and storage changes on the TP, and critical information on lake overflow flood monitoring and prediction as the expansion of some TP lakes
becomes a serious threat to surrounding residents and infrastructure.
TL;DR: The Theia Snow collection as mentioned in this paper provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations, including the main mountain regions in western Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco.
Abstract: . The Theia Snow
collection routinely provides high-resolution maps of the snow-covered area from Sentinel-2 and Landsat-8 observations. The collection covers
selected areas worldwide, including the main mountain regions in western
Europe (e.g. Alps, Pyrenees) and the High Atlas in Morocco. Each product of
the Theia Snow collection contains four classes: snow, no snow, cloud and no data.
We present the algorithm to generate the snow products and provide an
evaluation of the accuracy of Sentinel-2 snow products using in situ snow depth
measurements, higher-resolution snow maps and visual control. The results
suggest that the snow is accurately detected in the Theia snow collection
and that the snow detection is more accurate than the Sen2Cor outputs (ESA
level 2 product). An issue that should be addressed in a future release is
the occurrence of false snow detection in some large clouds. The snow maps
are currently produced and freely distributed on average 5 d after the image
acquisition as raster and vector files via the Theia portal
( https://doi.org/10.24400/329360/F7Q52MNK ).
TL;DR: Zheng et al. as mentioned in this paper used a multi-species atmospheric Bayesian inversion approach to attribute satellite-observed atmospheric CO variations to its sources and sinks in order to achieve a full closure of the global CO budget during 2000-2017.
Abstract: . Atmospheric carbon monoxide (CO) concentrations have been
decreasing since 2000, as observed by both satellite- and ground-based
instruments, but global bottom-up emission inventories estimate increasing
anthropogenic CO emissions concurrently. In this study, we use a
multi-species atmospheric Bayesian inversion approach to attribute
satellite-observed atmospheric CO variations to its sources and sinks in
order to achieve a full closure of the global CO budget during 2000–2017.
Our observation constraints include satellite retrievals of the total column
mole fraction of CO, formaldehyde (HCHO), and methane ( CH4 ) that are
all major components of the atmospheric CO cycle. Three inversions (i.e.,
2000–2017, 2005–2017, and 2010–2017) are performed to use the observation
data to the maximum extent possible as they become available and assess the
consistency of inversion results to the assimilation of more trace gas
species. We identify a declining trend in the global CO budget since 2000
(three inversions are broadly consistent during overlapping periods), driven
by reduced anthropogenic emissions in the US and Europe (both likely from
the transport sector), and in China (likely from industry and residential
sectors), as well as by reduced biomass burning emissions globally,
especially in equatorial Africa (associated with reduced burned areas). We
show that the trends and drivers of the inversion-based CO budget are not
affected by the inter-annual variation assumed for prior CO fluxes. All
three inversions contradict the global
bottom-up inventories in the world's top two emitters: for the sign of
anthropogenic emission trends in China (e.g., here - 0.8 ± 0.5 % yr −1 since 2000, while the prior gives 1.3±0.4 % yr −1 )
and for the rate of anthropogenic emission increase in South Asia (e.g.,
here 1.0±0.6 % yr −1 since 2000, smaller than 3.5±0.4 % yr −1 in the prior inventory). The posterior model CO
concentrations and trends agree well with independent ground-based
observations and correct the prior model bias. The comparison of the three
inversions with different observation constraints further suggests that the
most complete constrained inversion that assimilates CO, HCHO, and CH4
has a good representation of the global CO budget, and therefore matches best
with independent observations, while the inversion only assimilating CO
tends to underestimate both the decrease in anthropogenic CO emissions and
the increase in the CO chemical production. The global CO budget data from
all three inversions in this study can be accessed from
https://doi.org/10.6084/m9.figshare.c.4454453.v1 (Zheng et al., 2019).
Geophysical Institute, University of Bergen1, Leibniz Institute of Marine Sciences2, Princeton University3, Scripps Institution of Oceanography4, Leibniz Institute for Baltic Sea Research5, Pacific Marine Environmental Laboratory6, Joint Institute for the Study of the Atmosphere and Ocean7, Rio de Janeiro State University8, University of Groningen9, Alfred Wegener Institute for Polar and Marine Research10, Japan Meteorological Agency11, Bjerknes Centre for Climate Research12, Silver Spring Networks13, University of Paris14, Japan Agency for Marine-Earth Science and Technology15, Spanish National Research Council16, University of Bremen17, Polish Academy of Sciences18, University of Tasmania19, Atlantic Oceanographic and Meteorological Laboratory20
TL;DR: The Global Ocean Data Analysis Project (GLODAPv2, v2.2019) as discussed by the authors provides regular compilations of surface to bottom ocean biogeochemical data, with an emphasis on seawater inorganic carbon chemistry.
Abstract: . The Global Ocean Data Analysis Project (GLODAP) is a
synthesis effort providing regular compilations of surface to bottom ocean
biogeochemical data, with an emphasis on seawater inorganic carbon chemistry
and related variables determined through chemical analysis of water samples.
This update of GLODAPv2, v2.2019, adds data from 116 cruises to the previous
version, extending its coverage in time from 2013 to 2017, while also adding
some data from prior years. GLODAPv2.2019 includes measurements from more
than 1.1 million water samples from the global oceans collected on 840
cruises. The data for the 12 GLODAP core variables (salinity, oxygen,
nitrate, silicate, phosphate, dissolved inorganic carbon, total alkalinity,
pH, CFC-11, CFC-12, CFC-113, and CCl4 ) have undergone extensive quality
control, especially systematic evaluation of bias. The data are available in
two formats: (i) as submitted by the data originator but updated to WOCE
exchange format and (ii) as a merged data product with adjustments applied
to minimize bias. These adjustments were derived by comparing the data from
the 116 new cruises with the data from the 724 quality-controlled cruises of
the GLODAPv2 data product. They correct for errors related to measurement,
calibration, and data handling practices, taking into account any known or
likely time trends or variations. The compiled and adjusted data product is
believed to be consistent to better than 0.005 in salinity, 1 % in oxygen,
2 % in nitrate, 2 % in silicate, 2 % in phosphate, 4 µ mol kg −1 in dissolved inorganic carbon, 4 µ mol kg −1 in total
alkalinity, 0.01–0.02 in pH, and 5 % in the halogenated transient
tracers. The compilation also includes data for several other variables,
such as isotopic tracers. These were not subjected to bias comparison or
adjustments. The original data, their documentation and DOI codes are available in the
Ocean Carbon Data System of NOAA NCEI
( https://www.nodc.noaa.gov/ocads/oceans/GLODAPv2_2019/ , last access: 17 September 2019). This
site also provides access to the merged data product, which is provided as a
single global file and as four regional ones – the Arctic, Atlantic, Indian,
and Pacific oceans – under https://doi.org/10.25921/xnme-wr20
(Olsen et al., 2019). The
product files also include significant ancillary and approximated data.
These were obtained by interpolation of, or calculation from, measured data.
This paper documents the GLODAPv2.2019 methods and provides a broad overview
of the secondary quality control procedures and results.
TL;DR: Ablain et al. as mentioned in this paper analyzed 25 years of satellite altimetry data and provided for the first time the error variance-covariance matrix for the GMSL record with a timeconsuming resolution of 10 days.
Abstract: . Satellite altimetry missions now provide more than 25 years of accurate, continuous and quasi-global measurements of sea level
along the reference ground track of TOPEX/Poseidon. These measurements are
used by different groups to build the Global Mean Sea Level (GMSL) record,
an essential climate change indicator. Estimating a realistic uncertainty in
the GMSL record is of crucial importance for climate studies, such as
assessing precisely the current rate and acceleration of sea level,
analysing the closure of the sea-level budget, understanding the causes of
sea-level rise, detecting and attributing the response of sea level to
anthropogenic activity, or calculating the Earth's energy imbalance.
Previous authors have estimated the uncertainty in the GMSL trend over the
period 1993–2014 by thoroughly analysing the error budget of the satellite
altimeters and have shown that it amounts to ±0.5 mm yr −1
(90 %
confidence level). In this study, we extend our previous results, providing a
comprehensive description of the uncertainties in the satellite GMSL record.
We analysed 25 years of satellite altimetry data and provided for the first
time the error variance–covariance matrix for the GMSL record with a time
resolution of 10 days. Three types of errors have been modelled (drifts,
biases, noises) and combined together to derive a realistic estimate of the
GMSL error variance–covariance matrix. From the latter, we derived a 90 %
confidence envelope of the GMSL record on a 10 d basis. Then we used a
least squared approach and the error variance–covariance matrix to assess
the GMSL trend and acceleration uncertainties over any 5-year time periods
and longer in between October 1992 and December 2017. Over 1993–2017, we
have found a GMSL trend of 3.35±0.4 mm yr −1 within a 90 % confidence
level (CL) and a GMSL acceleration of 0.12±0.07 mm yr −2 (90 % CL). This is in agreement (within error bars)
with previous studies. The full GMSL error variance–covariance matrix is
freely available online: https://doi.org/10.17882/58344
(Ablain et al., 2018).
TL;DR: Li et al. as mentioned in this paper presented a suite of datasets consisting of long-term hydrometeorological, snow cover and frozen ground data for investigating watershed science and functions from an integrated, distributed and multi-scale observation network in the upper reaches of the Heihe River Basin in China.
Abstract: . The alpine region is important in riverine and watershed
ecosystems as a contributor of freshwater, providing and stimulating
specific habitats for biodiversity. In parallel, recent climate change,
human activities and other perturbations may disturb hydrological processes
and eco-functions, creating the need for next-generation observational and
modeling approaches to advance a predictive understanding of such processes
in the alpine region. However, several formidable challenges, including the
cold and harsh climate, high altitude and complex topography, inhibit
complete and consistent data collection where and when it is needed, which hinders the
development of remote-sensing technologies and alpine hydrological models.
The current study presents a suite of datasets consisting of long-term
hydrometeorological, snow cover and frozen-ground data for investigating
watershed science and functions from an integrated, distributed and
multiscale observation network in the upper reaches of the Heihe River Basin
(HRB) in China. Meteorological and hydrological data were monitored from an
observation network connecting a group of automatic meteorological stations
(AMSs). In addition, to capture snow accumulation and ablation processes,
snow cover properties were collected from a snow observation superstation
using state-of-the-art techniques and instruments. High-resolution soil
physics datasets were also obtained to capture the freeze–thaw processes
from a frozen-ground observation superstation. The updated datasets were
released to scientists with multidisciplinary backgrounds (i.e., cryospheric
science, hydrology and meteorology), and they are expected to serve as a
testing platform to provide accurate forcing data and validate and evaluate
remote-sensing products and hydrological models for a broader community. The
datasets are available from the Cold and Arid Regions Science Data Center at
Lanzhou ( https://doi.org/10.3972/hiwater.001.2019.db , Li, 2019).
TL;DR: In this paper, the authors exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp) spacecraftsatellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of Meteorological Satellites (EUMETSAT) PolarSystem programme.
Abstract: . Long-term gridded precipitation products are crucial for several
applications in hydrology, agriculture and climate sciences. Currently
available precipitation products suffer from space and time inconsistency
due to the non-uniform density of ground networks and the difficulties in
merging multiple satellite sensors. The recent “bottom-up” approach that
exploits satellite soil moisture observations for estimating rainfall
through the SM2RAIN (Soil Moisture to Rain) algorithm is suited to build a consistent rainfall data
record as a single polar orbiting satellite sensor is used. Here we exploit the Advanced SCATterometer (ASCAT) on board three Meteorological Operational (MetOp)
satellites, launched in 2006, 2012, and 2018, as part of the European Organisation for the Exploitation of
Meteorological Satellites (EUMETSAT) Polar
System programme. The continuity of the scatterometer sensor is ensured
until the mid-2040s through the MetOp Second Generation Programme. Therefore, by
applying the SM2RAIN algorithm to ASCAT soil moisture observations, a long-term
rainfall data record will be obtained, starting in 2007 and lasting until the mid-2040s. The
paper describes the recent improvements in data pre-processing, SM2RAIN
algorithm formulation, and data post-processing for obtaining the
SM2RAIN–ASCAT quasi-global (only over land) daily rainfall data record at a
12.5 km spatial sampling from 2007 to 2018. The quality of the SM2RAIN–ASCAT data record
is assessed on a regional scale through comparison with high-quality
ground networks in Europe, the United States, India, and Australia. Moreover, an
assessment on a global scale is provided by using the triple-collocation (TC)
technique allowing us also to compare these data with the latest, fifth-generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis
(ERA5), the Early Run version of the Integrated Multi-Satellite Retrievals
for Global Precipitation Measurement (IMERG), and the gauge-based Global
Precipitation Climatology Centre (GPCC) products. Results show that the SM2RAIN–ASCAT rainfall data record performs relatively
well at both a regional and global scale, mainly in terms of root mean square
error (RMSE) when compared to other products. Specifically, the SM2RAIN–ASCAT data
record provides performance better than IMERG and GPCC in data-scarce
regions of the world, such as Africa and South America. In these areas, we
expect larger benefits in using SM2RAIN–ASCAT for hydrological and
agricultural applications. The limitations of the SM2RAIN–ASCAT data record consist
of the underestimation of peak rainfall events and the presence of
spurious rainfall events due to high-frequency soil moisture fluctuations
that might be corrected in the future with more advanced bias correction
techniques. The SM2RAIN–ASCAT data record is freely available at
https://doi.org/10.5281/zenodo.3405563 (Brocca et al., 2019) (recently extended to the end of
August 2019).
TL;DR: In this article, the authors released two datasets that track the enhanced landsliding induced by the 2008 Mw Â7.9 Wenchuan earthquake over a portion of the LongmenMountains, at the eastern margin of the Tibetan Plateau (Sichuan, China).
Abstract: . We release two datasets that track the enhanced landsliding induced by the
2008 Mw 7.9 Wenchuan earthquake over a portion of the Longmen
Mountains, at the eastern margin of the Tibetan Plateau (Sichuan, China). The
first dataset is a geo-referenced multi-temporal polygon-based inventory of
pre- and coseismic landslides, post-seismic remobilisations of coseismic
landslide debris and post-seismic landslides (new failures). It covers
471 km 2 in the earthquake's epicentral area, from 2005 to 2018. The
second dataset records the debris flows that occurred from 2008 to 2017 in a
larger area ( ∼17 000 km 2 ), together with information on their
triggering rainfall as recorded by a network of rain gauges. For some
well-monitored events, we provide more detailed data on rainfall, discharge,
flow depth and density. The datasets can be used to analyse, on various
scales, the patterns of landsliding caused by the earthquake. They can be
compared to inventories of landslides triggered by past or new earthquakes or
by other triggers to reveal common or distinctive controlling factors. To our
knowledge, no other inventories that track the temporal evolution of
earthquake-induced mass wasting have been made freely available thus far. Our
datasets can be accessed from https://doi.org/10.5281/zenodo.1405489 .
We also encourage other researchers to share their datasets to facilitate
research on post-seismic geological hazards.
Humboldt University of Berlin1, Alfred Wegener Institute for Polar and Marine Research2, University of Oslo3, Heidelberg University4, Russian Academy of Sciences5, Arctic and Antarctic Research Institute6, University of Leeds7, University of Exeter8, University of Grenoble9, Met Office10, University of Hamburg11
TL;DR: In this article, the authors presented the temporal data set produced between 2002 and 2017, explaining the instrumentation, calibration, processing, and data quality control of the PANGAEA dataset.
Abstract: . Most of the world's permafrost is located in the
Arctic, where its frozen organic carbon content makes it a potentially
important influence on the global climate system. The Arctic climate appears
to be changing more rapidly than the lower latitudes, but observational data
density in the region is low. Permafrost thaw and carbon release into the
atmosphere, as well as snow cover changes, are positive feedback mechanisms
that have the potential for climate warming. It is therefore particularly
important to understand the links between the energy balance, which can vary
rapidly over hourly to annual timescales, and permafrost conditions, which
changes slowly on decadal to centennial timescales. This requires long-term
observational data such as that available from the Samoylov research site in
northern Siberia, where meteorological parameters, energy balance, and
subsurface observations have been recorded since 1998. This paper presents
the temporal data set produced between 2002 and 2017, explaining the
instrumentation, calibration, processing, and data quality control.
Furthermore, we present a merged data set of the parameters, which were
measured from 1998 onwards. Additional data include a high-resolution digital
terrain model (DTM) obtained from terrestrial lidar laser scanning. Since the
data provide observations of temporally variable parameters that influence
energy fluxes between permafrost, active-layer soils, and the atmosphere
(such as snow depth and soil moisture content), they are suitable for
calibrating and quantifying the dynamics of permafrost as a component in
earth system models. The data also include soil properties beneath different
microtopographic features (a polygon centre, a rim, a slope, and a trough),
yielding much-needed information on landscape heterogeneity for use in land
surface modelling. For the record from 1998 to 2017, the average mean annual air temperature
was −12.3 ∘ C, with mean monthly temperature of the warmest month
(July) recorded as 9.5 ∘ C and for the coldest month (February)
−32.7 ∘ C. The average annual rainfall was 169 mm. The depth of
zero annual amplitude is at 20.75 m. At this depth, the temperature has
increased from −9.1 ∘ C in 2006 to −7.7 ∘ C in 2017. The presented data are freely available through the PANGAEA
( https://doi.org/10.1594/PANGAEA.891142 ) and Zenodo
( https://zenodo.org/record/2223709 , last access: 6 February 2019) websites.
TL;DR: Tang et al. as discussed by the authors proposed a global surface solar radiation (SSR) dataset using an improved physical parameterization scheme, which includes water vapor, surface pressure and ozone from ERA5 reanalysis data and albedo and aerosol from MODIS.
Abstract: . The recent release of the International Satellite Cloud
Climatology Project (ISCCP) HXG cloud products and new ERA5 reanalysis data
enabled us to produce a global surface solar radiation (SSR) dataset: a
16-year (2000–2015) high-resolution (3 h, 10 km) global SSR dataset using an
improved physical parameterization scheme. The main inputs were cloud
optical depth from ISCCP-HXG cloud products; the water vapor, surface
pressure and ozone from ERA5 reanalysis data; and albedo and aerosol from
Moderate Resolution Imaging Spectroradiometer (MODIS) products. The
estimated SSR data were evaluated against surface observations measured at 42 stations of the Baseline Surface Radiation Network (BSRN) and 90 radiation
stations of the China Meteorological Administration (CMA). Validation
against the BSRN data indicated that the mean bias error (MBE), root mean
square error (RMSE) and correlation coefficient ( R ) for the instantaneous SSR
estimates at 10 km scale were −11.5 W m −2 , 113.5 W m −2 and 0.92,
respectively. When the estimated instantaneous SSR data were upscaled to 90 km, its error was clearly reduced, with RMSE decreasing to 93.4 W m −2
and R increasing to 0.95. For daily SSR estimates at 90 km scale, the MBE,
RMSE and R at the BSRN were −5.8 W m −2 , 33.1 W m −2 and 0.95,
respectively. These error metrics at the CMA radiation stations were 2.1 W m −2 , 26.9 W m −2 and 0.95, respectively. Comparisons with other
global satellite radiation products indicated that our SSR estimates were
generally better than those of the ISCCP flux dataset (ISCCP-FD), the global
energy and water cycle experiment surface radiation budget (GEWEX-SRB), and
the Earth's Radiant Energy System (CERES). Our SSR dataset will contribute
to the land-surface process simulations and the photovoltaic applications in
the future. The dataset is available
at https://doi.org/10.11888/Meteoro.tpdc.270112 (Tang, 2019).
TL;DR: Rubino et al. as mentioned in this paper presented new measurements, including new measurements performed at the CSIRO Ice Core Extraction LABoratory (ICELAB) on air samples from ice obtained at the high-accumulation site of Law Dome (East Antarctica).
Abstract: . Ice core records of the major atmospheric greenhouse
gases ( CO2 , CH4 , N2O ) and their isotopologues covering recent
centuries provide evidence of biogeochemical variations during the
Late Holocene and pre-industrial periods and over the transition to the
industrial period. These records come from a number of ice core and firn air
sites and have been measured in several laboratories around the world and
show common features but also unresolved differences. Here we present
revised records, including new measurements, performed at the CSIRO Ice Core
Extraction LABoratory (ICELAB) on air samples from ice obtained at the high-accumulation site of Law Dome (East Antarctica). We are motivated by the
increasing use of the records by the scientific community and by recent
data-handling developments at CSIRO ICELAB. A number of cores and firn air
samples have been collected at Law Dome to provide high-resolution records
overlapping recent, direct atmospheric observations. The records have been
updated through a dynamic link to the calibration scales used in the Global
Atmospheric Sampling LABoratory (GASLAB) at CSIRO, which are periodically
revised with information from the latest calibration experiments. The
gas-age scales have been revised based on new ice-age scales and the
information derived from a new version of the CSIRO firn diffusion model.
Additionally, the records have been revised with new, rule-based selection
criteria and updated corrections for biases associated with the extraction
procedure and the effects of gravity and diffusion in the firn. All
measurements carried out in ICELAB–GASLAB over the last 25 years are now
managed through a database (the ICElab dataBASE or ICEBASE), which provides
consistent data management, automatic corrections and selection of
measurements, and a web-based user interface for data extraction. We present
the new records, discuss their strengths and limitations, and summarise their
main features. The records reveal changes in the carbon cycle and
atmospheric chemistry over the last 2 millennia, including the major
changes of the anthropogenic era and the smaller, mainly natural variations
beforehand. They provide the historical data to calibrate and test the next
inter-comparison of models used to predict future climate change (Coupled
Model Inter-comparison Project – phase 6, CMIP6). The datasets described in
this paper, including spline fits, are available at https://doi.org/10.25919/5bfe29ff807fb (Rubino et al., 2019).
National Oceanic and Atmospheric Administration1, Joint Institute for the Study of the Atmosphere and Ocean2, University of Alaska Fairbanks3, University of California, San Diego4, University of Southampton5, Bermuda Institute of Ocean Sciences6, University of Delaware7, University of Hawaii at Manoa8, Oregon State University9, University of Southern Mississippi10, Korean Ocean Research and Development Institute11, Atlantic Oceanographic and Meteorological Laboratory12, University of Puerto Rico at Mayagüez13, University of New Hampshire14, University of Washington15, University of Georgia16, Cooperative Research Centre17, Commonwealth Scientific and Industrial Research Organisation18, University of Tasmania19, Woods Hole Oceanographic Institution20
TL;DR: Sutton et al. as discussed by the authors presented a data product of 40 individual autonomous moored surface ocean pCO2 (partial pressure of CO2 ) time series established between 2004 and 2013, 17 also include autonomous pH measurements.
Abstract: . Ship-based time series, some now approaching over 3 decades long, are critical climate records that have dramatically improved our ability to characterize natural and anthropogenic drivers of ocean carbon dioxide ( CO2 ) uptake and biogeochemical processes. Advancements in autonomous marine carbon sensors and technologies over the last 2 decades have led to the expansion of observations at fixed time series sites, thereby improving the capability of characterizing sub-seasonal variability in the ocean. Here, we present a data product of 40 individual autonomous moored surface ocean pCO2 (partial pressure of CO2 ) time series established between 2004 and 2013, 17 also include autonomous pH measurements. These time series characterize a wide range of surface ocean carbonate conditions in different oceanic (17 sites), coastal (13 sites), and coral reef (10 sites) regimes. A time of trend emergence (ToE) methodology applied to the time series that exhibit well-constrained daily to interannual variability and an estimate of decadal variability indicates that the length of sustained observations necessary to detect statistically significant anthropogenic trends varies by marine environment. The ToE estimates for seawater pCO2 and pH range from 8 to 15 years at the open ocean sites, 16 to 41 years at the coastal sites, and 9 to 22 years at the coral reef sites. Only two open ocean pCO2 time series, Woods Hole Oceanographic Institution Hawaii Ocean Time-series Station (WHOTS) in the subtropical North Pacific and Stratus in the South Pacific gyre, have been deployed longer than the estimated trend detection time and, for these, deseasoned monthly means show estimated anthropogenic trends of 1.9±0.3 and 1.6±0.3 µatm yr−1 , respectively. In the future, it is possible that updates to this product will allow for the estimation of anthropogenic trends at more sites; however, the product currently provides a valuable tool in an accessible format for evaluating climatology and natural variability of surface ocean carbonate chemistry in a variety of regions. Data are available at https://doi.org/10.7289/V5DB8043 and https://www.nodc.noaa.gov/ocads/oceans/Moorings/ndp097.html (Sutton et al., 2018).
TL;DR: The seNorge dataset as mentioned in this paper is a collection of observational gridded datasets over Norway for daily total precipitation: daily mean, maximum, and minimum temperatures: daily minimum and maximum temperatures.
Abstract: . seNorge_2018 is a collection of observational gridded datasets over Norway for daily total precipitation: daily mean, maximum, and minimum temperatures.
The time period covers 1957 to 2017, and the data are presented over a high-resolution terrain-following grid with 1 km spacing in both meridional and zonal directions.
The seNorge family of observational gridded datasets developed at the Norwegian Meteorological Institute (MET Norway) has a 20-year-long history and seNorge_2018 is its newest member, the first providing daily minimum and maximum temperatures.
seNorge datasets are used for a wide range of applications in climatology, hydrology, and meteorology.
The observational dataset is based on MET Norway's climate data, which have been integrated by the “European Climate Assessment and Dataset” database.
Two distinct statistical interpolation methods have been developed, one for temperature and the other for precipitation.
They are both based on a spatial scale-separation approach where, at first, the analysis (i.e., predictions) at larger spatial scales is estimated. Subsequently they are used to infer the small-scale details down to a spatial scale comparable to the local observation density.
Mean, maximum, and minimum temperatures are interpolated separately; then physical consistency among them is enforced.
For precipitation, in addition to observational data, the spatial interpolation makes use of information provided by a climate model.
The analysis evaluation is based on cross-validation statistics and comparison with a previous seNorge version.
The analysis quality is presented as a function of the local station density.
We show that the occurrence of large errors in the analyses decays at an exponential rate with the increase in the station density.
Temperature analyses over most of the domain are generally not affected by significant biases.
However, during wintertime in data-sparse regions the analyzed minimum temperatures do have a bias between 2 ∘ C and 3 ∘ C.
Minimum temperatures are more challenging to represent and large errors are more frequent than for maximum and mean temperatures.
The precipitation analysis quality depends crucially on station density: the frequency of occurrence of large errors for intense precipitation is less than 5% in data-dense regions, while it is approximately 30 % in data-sparse regions.
The open-access datasets are available for public download at daily total precipitation ( https://doi.org/10.5281/zenodo.2082320 , Lussana , 2018 b ); and daily mean ( https://doi.org/10.5281/zenodo.2023997 , Lussana , 2018 c ), maximum ( https://doi.org/10.5281/zenodo.2559372 , Lussana , 2018 e ), and minimum ( https://doi.org/10.5281/zenodo.2559354 , Lussana , 2018 d ) temperatures.
University of Lisbon1, Plymouth Marine Laboratory2, EUMETSAT3, University of Paris4, Curtin University5, University of Southern Mississippi6, Bigelow Laboratory For Ocean Sciences7, Commonwealth Scientific and Industrial Research Organisation8, Université du Québec à Rimouski9, Dokuz Eylül University10, University of Bremen11, Alfred Wegener Institute for Polar and Marine Research12, Monterey Bay Aquarium Research Institute13, Oceanic Platform of the Canary Islands14, United States Naval Research Laboratory15, University of California, San Diego16, University of the Highlands and Islands17, Goddard Space Flight Center18, National Institute for Space Research19, Stockholm University20, University of California, Santa Cruz21, University of the Littoral Opal Coast22, University of Strathclyde23, University of South Florida St. Petersburg24, Marine Institute of Memorial University of Newfoundland25, National Oceanic and Atmospheric Administration26, Heriot-Watt University27, IFREMER28, National Oceanography Centre29, Woods Hole Oceanographic Institution30, Harbor Branch Oceanographic Institute31, University of Miami32, Cooperative Research Centre33
TL;DR: In this paper, the authors describe the data compiled for the validation of the ocean-colour products from the ESA OceanColour Climate Change Initiative (OC-CCI) from the period from 1997 to 2018.
Abstract: . A global compilation of in situ data is useful to evaluate the
quality of ocean-colour satellite data records. Here we describe the data
compiled for the validation of the ocean-colour products from the ESA Ocean
Colour Climate Change Initiative (OC-CCI). The data were acquired from
several sources (including, inter alia, MOBY, BOUSSOLE, AERONET-OC, SeaBASS, NOMAD,
MERMAID, AMT, ICES, HOT and GeP&CO) and span the period from 1997 to 2018.
Observations of the following variables were compiled: spectral
remote-sensing reflectances, concentrations of chlorophyll a , spectral
inherent optical properties, spectral diffuse attenuation coefficients and
total suspended matter. The data were from multi-project archives acquired
via open internet services or from individual projects, acquired directly
from data providers. Methodologies were implemented for homogenization,
quality control and merging of all data. No changes were made to the
original data, other than averaging of observations that were close in time
and space, elimination of some points after quality control and conversion
to a standard format. The final result is a merged table designed for
validation of satellite-derived ocean-colour products and available in text
format. Metadata of each in situ measurement (original source, cruise or
experiment, principal investigator) was propagated throughout the work and
made available in the final table. By making the metadata available,
provenance is better documented, and it is also possible to analyse each set
of data separately. This paper also describes the changes that were made to
the compilation in relation to the previous version (Valente et al., 2016).
The compiled data are available at
https://doi.org/10.1594/PANGAEA.898188 (Valente et al., 2019).
TL;DR: Athanasiou et al. as mentioned in this paper presented the first global dataset of nearshore slopes with a resolution of 1 km at almost 620,000 points along the global circumference of the globe.
Abstract: . Nearshore slope, defined as the cross-shore gradient of the subaqueous
profile, is an important input parameter which affects hydrodynamic and
morphological coastal processes. It is used in both local and large-scale
coastal investigations. However, due to unavailability of data, most
studies, especially those that focus on continental or global scales, have
historically adopted a uniform nearshore slope. This simplifying assumption
could however have far-reaching implications for predictions/projections
thus obtained. Here, we present the first global dataset of nearshore slopes
with a resolution of 1 km at almost 620 000 points along the global
coastline. To this end, coastal profiles were constructed using global
topo-bathymetric datasets. The results show that the nearshore slopes vary
substantially around the world. An assessment of coastline recession driven by sea level rise (SLR)
(for an arbitrary 0.5 m SLR) with a globally uniform
coastal slope of 1 : 100, as carried out in previous studies, and with the spatially
variable coastal slopes computed herein shows that, on average, the former
approach would underestimate coastline recession by about 40 %, albeit
with significant spatial variation. The final dataset has been made publicly
available at
https://doi.org/10.4121/uuid:a8297dcd-c34e-4e6d-bf66-9fb8913d983d (Athanasiou, 2019).
TL;DR: In this paper, the authors used a double logistic model to characterize the long-term mean seasonal pattern of phenology indicators of the start of season (SOS) and the end season (EOS) using all available Landsat images on the Google Earth Engine platform.
Abstract: . Medium-resolution satellite observations show great
potential for characterizing seasonal and annual dynamics of vegetation
phenology in urban domains from local to regional and global scales.
However, most previous studies were conducted using coarse-resolution data,
which are inadequate for characterizing the spatiotemporal dynamics of
vegetation phenology in urban domains. In this study, we produced an annual
vegetation phenology dataset in urban ecosystems for the conterminous United
States (US), using all available Landsat images on the Google Earth Engine
(GEE) platform. First, we characterized the long-term mean seasonal pattern
of phenology indicators of the start of season (SOS) and the end of season
(EOS), using a double logistic model. Then, we identified the annual
variability of these two phenology indicators by measuring the difference of
dates when the vegetation index in a specific year reaches the same
magnitude as its long-term mean. The derived phenology indicators agree well
with in situ observations from the PhenoCam network and Harvard Forest. Comparing with
results derived from the moderate-resolution imaging spectroradiometer
(MODIS) data, our Landsat-derived phenology indicators can provide more
spatial details. Also, we found the temporal trends of phenology indicators
(e.g., SOS) derived from Landsat and MODIS are consistent overall, but the
Landsat-derived results from 1985 offer a longer temporal span compared to
MODIS from 2001 to present. In general, there is a spatially explicit
pattern of phenology indicators from the north to the south in cities in the
conterminous US, with an overall advanced SOS in the past 3 decades. The
derived phenology product in the US urban domains at the national level is
of great use for urban ecology studies for its medium spatial resolution (30 m) and long temporal span (30 years). The data are available at
https://doi.org/10.6084/m9.figshare.7685645.v5 .
TL;DR: In this article, the authors introduce and provide access to daily (1960-2017 and hourly (1993-2017) datasets of snow and meteorological data measured at the Col de Porte site.
Abstract: . In this paper, we introduce and provide access to daily (1960–2017) and hourly (1993–2017) datasets of snow and meteorological data measured at the Col de Porte site, 1325 m a.s.l., Chartreuse, France. Site metadata and ancillary measurements such as soil properties and masks of the incident solar radiation are also provided. Weekly snow profiles are made available from September 1993 to March 2018. A detailed study of the uncertainties originating from both measurement errors and spatial variability within the measurement site is provided for several variables. We show that the estimates of the ratio of diffuse-to-total shortwave broadband irradiance is affected by an uncertainty of ±0.21 (no unit). The estimated root mean square deviation, which mainly represents spatial variability, is ±10 cm for snow depth, ±25 kg m−2 for the water equivalent of snow cover (SWE), and ±1 K for soil temperature (±0.4 K during the snow season). The daily dataset can be used to quantify the effect of climate change at this site, with a decrease of the mean snow depth (1 December to 30 April) of 39 cm from the 1960–1990 period to the 1990–2017 period (40 % of the mean snow depth for 1960–1990) and an increase in temperature of +0.90 K for the same periods. Finally, we show that the daily and hourly datasets are useful and appropriate for driving and evaluating a snowpack model over such a long period. The data are placed on the repository of the Observatoire des Sciences de l'Univers de Grenoble (OSUG) data centre: https://doi.org/10.17178/CRYOBSCLIM.CDP.2018 .
TL;DR: In this paper, the authors describe in situ meteorological forcing and evaluation data, and bias-corrected reanalysis forcing data, for cold regions' modelling at 10 sites, including one maritime, one arctic, three boreal, and five mid-latitude alpine.
Abstract: . This paper describes in situ meteorological forcing and evaluation data, and
bias-corrected reanalysis forcing data, for cold regions' modelling at 10
sites. The long-term datasets (one maritime, one arctic, three boreal, and
five mid-latitude alpine) are the reference sites chosen for evaluating
models participating in the Earth System Model-Snow Model Intercomparison
Project. Periods covered by the in situ data vary between 7 and 20 years of hourly meteorological data, with evaluation data (snow depth, snow
water equivalent, albedo, soil temperature, and surface temperature)
available at varying temporal intervals. Thirty-year (1980–2010) time series
have been extracted from a global gridded surface meteorology dataset
(Global Soil Wetness Project Phase 3) for the grid cells containing the
reference sites, interpolated to 1 h time steps and bias-corrected.
Although the correction was applied to all sites, it was most important for
mountain sites hundreds of metres higher than the grid elevations and for
which uncorrected air temperatures were too high and snowfall amounts too
low. The discussion considers the importance of data sharing to the
identification of errors and how the publication of these datasets
contributes to good practice, consistency, and reproducibility in
geosciences. The Supplement provides information on instrumentation,
an estimate of the percentages of missing values, and gap-filling methods at
each site. It is hoped that these datasets will be used as benchmarks for
future model development and that their ease of use and availability will
help model developers quantify model uncertainties and reduce model errors.
The data are published in the repository PANGAEA and are available at
https://doi.pangaea.de/10.1594/PANGAEA.897575 .
TL;DR: This paper has compiled a global whole-rock geochemical database, sourced from various existing databases and supplemented with an extensive list of individual publications, to be useful for geochemical studies requiring extensive data sets.
Abstract: . Collation and dissemination of geochemical data are critical to promote rapid, creative, and accurate research and place new results in an appropriate global context. To this end, we have compiled a global whole-rock geochemical database, sourced from various existing databases and supplemented with an extensive list of individual publications. Currently the database stands at 1 022 092 samples with varying amounts of associated sample data, including major and trace element concentrations, isotopic ratios, and location information. Spatial and temporal distribution is heterogeneous; however, temporal distributions are enhanced over some previous database compilations, particularly in ages older than ∼ 1000 Ma. Also included are a range of geochemical indices, various naming schema, and physical property estimates computed on a major element normalized version of the geochemical data for quick reference. This compilation will be useful for geochemical studies requiring extensive data sets, in particular those wishing to investigate secular temporal trends. The addition of physical properties, estimated from sample chemistry, represents a unique contribution to otherwise similar geochemical databases. The data are published in .csv format for the purposes of simple distribution, but exist in a structure format acceptable for database management systems (e.g. SQL). One can either manipulate these data using conventional analysis tools such as MATLAB®, Microsoft® Excel, or R, or upload them to a relational database management system for easy querying and management of the data as unique keys already exist. The data set will continue to grow and be improved, and we encourage readers to contact us or other database compilations within about any data that are yet to be included. The data files described in this paper are available at https://doi.org/10.5281/zenodo.2592822 ( Gard et al. , 2019 a ) .
TL;DR: In this paper, the authors presented a 1986 through 2017 estimate of Greenland Ice Sheet ice discharge through an automatic and adaptable method, as opposed to conventional hand-picked gates, where gates near the present-yeartermini and estimate problematic bed topography (ice thickness) values where necessary.
Abstract: . We present a 1986 through 2017 estimate of Greenland Ice Sheet ice discharge.
Our data include all discharging ice that flows faster than 100 m yr −1
and are generated through an automatic and adaptable method, as opposed to
conventional hand-picked gates. We position gates near the present-year
termini and estimate problematic bed topography (ice thickness) values where
necessary. In addition to using annual time-varying ice thickness, our time
series uses velocity maps that begin with sparse spatial and temporal
coverage and end with near-complete spatial coverage and 6 d updates to
velocity. The 2010 through 2017 average ice discharge through the flux gates
is ∼ 488 ± 49 Gt yr −1 . The 10 % uncertainty stems primarily
from uncertain ice bed location (ice thickness). We attribute the ∼50 Gt yr −1 differences among our results and previous studies to our
use of updated bed topography from BedMachine v3. Discharge is approximately
steady from 1986 to 2000, increases sharply from 2000 to 2005, and then is
approximately steady again. However, regional and glacier variability is more
pronounced, with recent decreases at most major glaciers and in all but one
region offset by increases in the NW (northwestern) region. As part of the
journal's living archive option, all input data, code, and results from this
study will be updated when new input data are accessible and made freely
available at https://doi.org/10.22008/promice/data/ice_discharge .