The European Centre for Medium-Range Weather Forecasts (ECMWF) recently released the first 7-year segment of its latest atmospheric reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes relative to the former ERA-Interim atmospheric reanalysis including higher spatial and temporal resolutions as well as a more recent model and data assimilation system. ERA-5 is foreseen to replace ERA-Interim reanalysis and one of the main goals of this study is to assess whether ERA-5 can enhance the simulation performances with respect to ERA-Interim when it is used to force a land surface model (LSM). To that end, both ERA-5 and ERA-Interim are used to force the ISBA (Interactions between Soil, Biosphere, and Atmosphere) LSM fully coupled with the Total Runoff Integrating Pathways (TRIP) scheme adapted for the CNRM (Centre National de Recherches Météorologiques) continental hydrological system within the SURFEX (SURFace Externalisée) modelling platform of Météo-France. Simulations cover the 2010–2016 period at half a degree spatial resolution.
The ERA-5 impact on ISBA LSM relative to ERA-Interim is evaluated using remote sensing and in situ observations covering a substantial part of the land surface storage and fluxes over the continental US domain. The remote sensing observations include (i) satellite-driven model estimates of land evapotranspiration, (ii) upscaled ground-based observations of gross primary production, (iii) satellite-derived estimates of surface soil moisture and (iv) satellite-derived estimates of leaf area index (LAI). The in situ observations cover (i) soil moisture, (ii) turbulent heat fluxes, (iii) river discharges and (iv) snow depth. ERA-5 leads to a consistent improvement over ERA-Interim as verified by the use of these eight independent observations of different land status and of the model simulations forced by ERA-5 when compared with ERA-Interim. This is particularly evident for the land surface variables linked to the terrestrial hydrological cycle, while variables linked to vegetation are less impacted. Results also indicate that while precipitation provides, to a large extent, improvements in surface fields (e.g. large improvement in the representation of river discharge and snow depth), the other atmospheric variables play an important role, contributing to the overall improvements. These results highlight the importance of enhanced meteorological forcing quality provided by the new ERA-5 reanalysis, which will pave the way for a new generation of land-surface developments and applications.
Observing and simulating the response of land biophysical variables to extreme events is a major scientific challenge in relation to the adaptation to climate change. To that end, land surface models (LSMs) constrained by high-quality gridded atmospheric variables and coupled with river-routing models are essential (Schellekens et al., 2017; Dirmeyer et al., 2006). Such LSMs should represent land surface biogeophysical variables like surface and root zone soil moisture (SSM and RZSM, respectively), biomass, and leaf area index (LAI) in a way that is fully consistent with the representation of surface and energy flux as well as river discharge simulations. Land surface simulations, such as those from the Global Soil Wetness Project (GSWP, Dirmeyer et al., 2002, 2006; Dirmeyer, 2011), combined with seasonal forecasting systems have been of paramount importance in triggering progress in land-related predictability as documented in the Global Land–Atmosphere Coupling Experiments (GLACE; Koster et al., 2009a, 2011). The land surface state estimates used in those studies were generally obtained with offline (or stand-alone) model simulations, forced by 3-hourly meteorological fields from atmospheric reanalysis. In the past decade, several improved global atmospheric reanalyses of the satellite era (1979–onwards) have been produced that enable new applications of offline land surface simulations. Amongst them are NASA's Modern Era Retrospective Analysis for Research and Applications (MERRA; Rienecker et al., 2011, and MERRA2; Gelaro et al., 2017) as well as ECMWF's (European Centre for Medium-Range Weather Forecasts) Interim reanalysis (ERA-Interim; Dee et al., 2011). Their offline use in either LSMs or land data assimilation system (LDAS), with or without meteorological corrections (e.g. precipitations), led to global land surface variables (LSVs) reanalysis data sets that can support, for example water resources analysis (Schellekens et al., 2017), like MERRA-Land and MERRA2-Land (Reichle et al., 2011, 2017), ERA-Interim/Land (Balsamo et al., 2015), the forthcoming ERA5-Land (Muñoz-Sabater et al., 2018), the North American LDAS (NLDAS; Mitchell et al., 2004), the Global LDAS (GLDAS; Rodell et al., 2004) and LDAS-Monde (Albergel et al., 2017). The quality of those offline land surface simulations relies on the accuracy of the forcing and of the realism of the LSM itself (Balsamo et al., 2015).
ECMWF recently released the first 7-year segment of its latest atmospheric
reanalysis: ERA-5 over the period 2010–2016. ERA-5 has important changes
relative to the former ERA-Interim atmospheric reanalysis including higher
spatial and temporal resolutions as well as a better global balance of
precipitation and evaporation. As ERA-5 will eventually replace the
ERA-Interim reanalysis assessing its ability to force a LSM with respect to
ERA-Interim is highly relevant. In this study, ERA-5, ERA-Interim and a
combination of both (ERA-5 with precipitation of ERA-Interim) are used to
constrain the CO
In this study, SURFEX is applied over a data-rich area: North America
(latitudes from 20.0 to 55.0
Section 2 presents the details of two atmospheric reanalyses data sets (ERA-Interim and ERA-5), the SURFEX model configuration and the evaluation strategy with the observational data sets. Section 3 provides a set of statistical diagnostics to assess and evaluate the impact of ERA-5 on ISBA with respect to ERA-Interim. Finally, Sect. 4 provides perspectives and future research directions.
ERA-Interim is a global atmospheric reanalysis produced by ECMWF (Dee et al.,
2011). It uses the integrated forecast system (IFS) version 31r1 (more
information at
ERA-5 uses one of the most recent versions of the Earth system model and data assimilation methods applied at ECMWF, which makes it able to use modern parameterizations of Earth processes compared to older versions used in ERA-Interim. For instance, developments were done at ECMWF which allows the reanalysis to use a variational bias scheme not only for satellite observations but also for ozone, aircraft and surface pressure data. ERA-5 also benefits from reprocessed data sets that were not ready yet during the production of ERA-Interim. Two other important features of ERA-5 are the improved temporal and spatial resolutions: from 6-hourly in ERA-Interim to hourly in ERA-5, and from 79 km in the horizontal dimension and 60 levels in the vertical to 31 km and 137 levels in ERA-5. Finally, ERA-5 also provides an estimate of uncertainty through the use of a 10-member ensemble of data assimilations (EDA) at a coarser resolution (63 km horizontal resolution) and 3-hourly frequency.
This study makes use of the CO
ISBA accounts for the atmospheric CO
The ISBA 12-layer explicit snow scheme (Boon and Etchevers, 2001; Decharme et al., 2016) and its multilayer soil diffusion scheme (ISBA-Dif) are used. The later is based on the mixed form of the Richards equation (Richards, 1931) and explicitly solves the one-dimensional Fourier law. It also incorporates soil freezing processes developed by Boone et al. (2000) and Decharme et al. (2013). The total soil profile is vertically discretized; both the temperature and moisture of each soil layer are computed according to their textural and hydrological characteristics. The Brookes and Corey model (Brooks and Corey, 1966) determines the closed-form equations between the soil moisture and the soil hydrodynamic parameters, including the hydraulic conductivity and the soil matrix potential (Decharme et al., 2013). The default discretization with 14 layers over 12 m depth is used. The lower boundary of each layer being: 0.01, 0.04, 0.1, 0.2, 0.4, 0.6, 0.8, 1, 1.5, 2, 3, 5, 8 and 12 m deep (see Fig. 1 of Decharme et al., 2011). Amounts of clay, sand and organic carbon in the soil determine the thermal and hydrodynamic soil properties (Decharme et al., 2016). They are taken from the Harmonized World Soil Database (HWSD; Wieder et al., 2014). As for hydrology, the infiltration, surface evaporation and total runoff are accounted for in the soil water balance. The infiltration rate defines the discrepancy between the surface runoff and the throughfall rate. The later being defined as the sum of rainfall not intercepted by the canopy, dripping from the canopy (i.e. interception reservoir) and snow melt water. The soil evaporation affects only the superficial layer (top 1 cm) and is proportional to its relative humidity. Transpiration water from the root zone (the region where the roots are asymptotically distributed) follows the equations in Jackson et al. (1996). Canal et al. (2014) provide more information on the root density profile.
Both the surface runoff (the lateral subsurface flow in the topsoil) and a free drainage condition at the bottom soil layer contribute to ISBA total runoff. The Dunne runoff (i.e. when no further soil moisture storage is available) and lateral subsurface flow from a subgrid distribution of the topography are computed using a basic TOPMODEL approach. The Horton runoff (i.e. when rainfall has exceeded infiltration capacity) is estimated from the maximum soil infiltration capacity and a subgrid exponential distribution of the rainfall intensity.
CTRIP is driven by three prognostic equations corresponding to (i) the
groundwater, (ii) the surface stream water and (iii) the seasonal
floodplains. Streamflow velocity is computed using the Manning formula as
described in Decharme et al. (2010). When the river water level overtops the
river-bank, it fills up the floodplain reservoir which empties when the water
level drops below this threshold (Decharme et al., 2012). Occurrence of
flooding impacts the ISBA soil hydrology through infiltration, and it also
influences the overlying atmosphere via free surface-water evaporation and
precipitation interception. The groundwater scheme is based on the
two-dimensional groundwater flow equation for the piezometric head (Vergnes
and Decharme, 2012). Its coupling with ISBA enables accounting for the
presence of a water table under the soil moisture column. It allows for
upward capillary fluxes into the soil (Vergnes et al., 2014). CTRIP is
coupled to ISBA through OASIS-MCT (Voldoire et al., 2017). Once a day, ISBA
provides CTRIP with updates on runoff, drainage, groundwater and floodplain
recharges, and CTRIP feedbacks to ISBA the water table
fall or rise, floodplain fraction,
and flood potential infiltration. The current CTRIP version consists of a
global streamflow network at 0.5
Three experiments are considered for the evaluation: (i) SURFEX forced by
ERA-Interim, all atmospheric variables interpolated to
0.5
This study makes use of several in situ measurement data sets as well as satellite-derived estimates of Earth observations that are described in the next two sections. The different performance metrics used for the evaluation are also described. Their choice is of crucial interest; it is governed by the nature of the variable itself and is influenced by the purpose of the investigation and its sensitivity to the considered variables (Stanski et al., 1989). No single metric or statistic can capture all the attributes of environmental variables; some are robust with respect to some attributes while insensitive to others (Entekhabi et al., 2010). While performance metrics like the correlation coefficient (R), unbiased root mean squared differences (ubRMSD), root mean squared differences (RMSDs) and efficiency score (depending on the considered variable) are first applied to the three simulations independently, metrics like the normalized information contribution (NIC; e.g. Kumar et al., 2009) are then used to quantify improvement or degradation from one data set to another. Table 1 summarizes the different data sets used for the evaluation and the performance metrics used.
Evaluation data sets and associated metrics used in this study.
USCRN is a network of climate-monitoring stations maintained and operated by
the National Oceanic and Atmospheric Administration (NOAA). It aims at
providing climate-science-quality measurements of air temperature and
precipitation. To increase the network's capability of monitoring soil
processes and drought, soil observations were added to USCRN instrumentation.
At each USCRN station in the conterminous
United States in 2011, the USCRN team completed the installation of triplicate-configuration soil moisture and
soil temperature probes at five standard depths (5, 10, 20, 50 and 100 cm)
as prescribed by the World Meteorological Organization. The 111 stations present
data between 2009 and 2016. Stations provide data at an hourly time step.
Similar to a prior study, data sets potentially affected by frozen conditions
were masked out using an observed temperature threshold of 4
Over the period 2010–2016, river discharge from ei_S, e5ei_S and e5_S are
compared to daily streamflow data from the USGS
The Global Historical Climatology Network (GHCN) daily data set, developed to
meet the needs of climate analysis and monitoring studies that require data
at a daily time resolution, contains records from over 75 000 stations in 179
countries and territories (Menne et al., 2012a, b). Numerous daily variables
are provided, including maximum and minimum temperature, total daily
precipitation, snowfall and snow depth. In this study, over North America,
stations with daily snow depth data from 2010 to 2016, with less than 10 %
missing and at least 15 days of snow presences per year on average (to avoid
using stations always reporting zero snow depth) are used, it results in 1901
stations out of 2056. The ability of ei_S, e5ei_S and e5_S to reproduce
snow depth and its variability is assessed using the bias, correlation
coefficient (
Daily observations of sensible and latent heat fluxes from the FLUXNET-2015
data set with at least 2 years of data are used over the period 2010–2016 to evaluate
the ability of e5_S, e5ei_S and ei_S to reproduce flux variability. The
FLUXNET-2015 data set includes data collected at sites from multiple regional
flux networks as well as several improvements to the data quality control
protocols and the data processing pipeline
(
Performance metrics are applied to each individual station of each network; thereafter, network metrics are computed by providing the median values of the statistics from the individual stations within each network. For each metric, the 95 % confidence interval of the median derived from a 10 000 samples bootstrapping is provided.
In response to the GCOS (Global Climate Observing System) endorsement of soil
moisture as an essential climate variable, the European Space Agency Water
Cycle Multimission Observation Strategy (WACMOS) project and Climate Change
Initiative (CCI;
Seasonal time series of the six main land surface variables (LSVs)
evaluated in this study over the whole domain for 2010–2016:
The GEOV1 LAI used in this study is produced by the European CGLS
(
The GLEAM product uses a set of algorithms to estimate both terrestrial
evaporation and RZSM based on satellite data (Miralles et al., 2011). It is a
useful validation tool to assess model performance given that such quantities
are difficult to measure directly on large scales. Potential evaporation
rates are constrained by satellite-derived SSM data, while the global
evaporation model in GLEAM is mainly driven by various microwave
remote-sensing observations. It is now a well established data set that has
been widely used to study land–atmosphere feedbacks (e.g. Miralles et al.,
2014b; Guillod et al., 2015), as well as trends and spatial variability in
the hydrological cycle (e.g. Jasechko et al., 2013; Greve et al., 2014;
Miralles et al., 2014a; Zhang et al., 2016). This study makes use of the
latest version available, v4.0. It is a 37-year data set spanning from 1980
to 2016 and is derived from a variety of sources, such as vegetation optical
depth and snow water equivalent, satellite-derived SSM estimates,
reanalysis air temperature and radiation, and a multi-source precipitation
product (Martens et al., 2017). It is available at a spatial resolution of
0.25
The final product used in this study is a daily GPP estimate from the FLUXCOM
project (Jung et al., 2017). It is an upscaled product derived from the
FLUXNET. In FLUXCOM, selected machine-learning-based regression tools that
span the full range of commonly applied algorithms (from model tree ensembles
to multiple adaptive regression splines, to artificial neural networks, and
to kernel methods), and several representatives of each family are used to
provide a spatial upscaling of GPP at regional to global scales. It is
limited to a 0.5
Comparison of surface soil moisture with in situ observations for
ei_S, e5ei_S and e5_S over the period 2010–2016 (April to September months are
considered). Median correlations
Maps of correlation (
Seasonal time series of the six main LSVs evaluated in this study over the whole domain for 2010–2016 are illustrated on Fig. 1. They are (Fig. 1a) river discharge (although averaging this variable over the whole domain has no real meaning, it is certainly useful to appreciate the differences between the three data sets), (Fig. 1b) snow depth, (Fig. 1c) leaf area index, (Fig. 1d) liquid soil moisture in the second layer of soil (1–4 cm depth), (Fig. 1e) evapotranspiration and (Fig. 1f) gross primary production. LSVs simulated with the ISBA LSM forced by ERA-Interim (ei_S) are in blue, by ERA-5 with precipitation from ERA-Interim (e5ei_S) in green and by ERA-5 (e5_S) in red. From Fig. 1, one can see that river discharge, snow depth and surface soil moisture are the most impacted by the use of ERA-5, suggesting that precipitation is the main driver of the differences.
This section presents the results of the comparison versus in situ
observations of LSVs from model simulations using either
ei_S, e5ei_S or e5_S starting with soil moisture. The statistical scores
for 2010–2016 surface soil moisture from ei_S, e5ei_S and e5_S are
presented in Table 2. Median
Normalized information contribution scores based on efficiency
scores (NIC
Mean snow depth bias for December–January–February in
ei_S
The 172 out of 344 gauging stations retained for the evaluation according to
the criteria described in the methodology section present NSE scores in the
[
Comparison of snow depth with in situ measurements, median Bias,
ubRMSD and
The mean snow depth bias of ei_S (see Fig. 5) highlights a clear
underestimation of winter snow depth accumulation mainly over the Rocky
Mountains. This is likely a result of the underestimation of snowfall by
ei_S associated with an overestimation of snow melt due to the coarse
resolution of the ei_S reflected in a smooth topography. The replacement of
all forcing variables by e5_S but keeping ei_S precipitation (e5ei_S,
Fig. 5b) shows a slight increase in snow depth. This result justifies the
above hypothesis that part of the snow underestimation is also due to
temperature issues linked with a coarse model orography. Moving to the full
e5_S forcing, there is a clear increase in snow depth when compared with
both ei_S and e5ei_S forced simulations resulting from an increase in
snowfall in e5_S. Figure 6 presents the mean seasonal cycle of bias and
ubRMSD (Fig. 6a) and correlations (Fig. 6b) over the period 2010–2016. In
addition to the added values of e5_S in terms of the mean snow depth already
presented in Fig. 5, the temporal variability and random errors are also
improved. Comparably with what was discussed for the mean bias, e5ei_S shows
some benefits when compared with ei_S in terms of ubRMSD and correlation
(median bias, ubRMSD and
Comparison of sensible (H) and latent (LE) heat flux with in situ
observations for ei_S, e5ei_S and e5_S. Median correlations (
Scatter plots illustrating evaluation of ei_S, e5ei_S and e5_S
against in situ measurements of sensible (
Results from the comparisons between ei_S, e5ei_S, e5_S and in situ
sensible and latent flux measurements are presented in Table 4 and
illustrated by Fig. 7. The 37 stations present significant correlation values (at
Seasonal correlations for
Figure 8 illustrates the comparison between ESA CCI SSM v4 and soil moisture
from the ISBA second layer of soil over 2010–2016. Figure 8a shows seasonal
correlations on volumetric time series and Fig. 8b on anomaly time series.
Scores for ISBA LSM forced by ERA-Interim (ei_S) are in blue, ERA-5 but with
precipitation from ERA-Interim (e5ei_S) in green and ERA-5 (e5_S) in red. From
Fig. 8a, one can appreciate the added value of using ERA-5 atmospheric forcing
particularly from April to September. It is also interesting to notice that
when using all ERA-5 atmospheric fields except for the precipitation, a
similar added value is noticeable suggesting that all improvements from ERA-5
do not only come from precipitation. However, when evaluating the short-term
variability of soil moisture (i.e. removing the seasonal effect), it is
really ERA-5 that provides the best results. Correlation on volumetric
(anomaly) time series for all grid points put together over 2010–2016 are
0.668 (0.464), 0.682 (0.468) and 0.689 (0.490) for ei_S, e5ei_S and e5_S,
respectively. Additionally to the global seasonal scores, Fig. 8c and d
present maps of correlation differences between soil moisture from e5_S and
ei_S on volumetric time series and anomaly time series, respectively. Grey
areas represent areas that were flagged out for elevation greater than
1500 m above sea level. As visible on Fig. 8c and d, the use of ERA-5 mainly
leads to improvements all over the considered domain. Focusing on correlation
differences, (
Seasonal scores between ISBA LSM within SURFEX forced by either
ERA-Interim (ei_S, in blue), ERA-5 but ERA-Interim precipitation (e5ei_S, in
green) or ERA-5 (e5_S, in red) and
Figure 9 illustrates seasonal scores between ISBA LSM forced by either
ERA-Interim (ei_S in blue), ERA-5 but ERA-Interim precipitation (e5ei_S in
green) or ERA-5 (e5_S in red) for the following variables: (Fig. 9a, b) evapotranspiration estimates
from the GLEAM project over 2010–2016, (Fig. 9c, d) upscaled GPP from the
FLUXCOM project over 2010–2013 and (Fig. 9e, f) LAI estimates from the
CGLS project over 2010–2016. The left column (Fig. 9a, c and e) are
for RMSDs and the right column (Fig. 9b, d and e) for correlations. For
evapotranspiration, and to a lesser extend GPP, one can notice a decrease in
RMSD when using ERA-5 atmospheric reanalysis compared to ERA-Interim
atmospheric reanalysis; however, it fails at improving LAI. Considering
evapotranspiration, correlation (RMSD) values for all grid points put
together over 2010–2016 are 0.786 (0.927 kg m
Improvements (in red) and degradations (in blue) from the use of ERA-5 in the
ISBA LSM with respect to ERA-Interim for evapotranspiration, GPP and LAI are illustrated by Fig. 10 (respectively from
top to bottom). Figure 10a, c and e show RMSD differences while Fig. 10b, d
and f show
This study assesses the ability of the recently released ERA-5 atmospheric reanalysis to force the ISBA land surface model (LSM) with respect to ERA-Interim reanalysis over North America for 2010–2016. The results presented above using the three atmospheric reanalysis data sets (ERA-Interim, ei_S; ERA-5 but with precipitation from ERA-Interim, e5ei_S; and ERA-5, e5_S, with all meteorological variables) to force the ISBA LSM provide two important insights: (i) firstly the use of ERA-5 leads to significant improvements in the representation of the land surface variables (LSVs) linked to the terrestrial water cycle assessed in this study (surface soil moisture, river discharges, snow depth and turbulent fluxes) but failed impacting LSVs linked to the vegetation cycle (carbon uptake and LAI). Even when they are small, improvements are systematic when using ERA-5. (ii) Secondly, if most of the improvements seem to come from a better representation of the precipitation in ERA-5, the e5ei_S experiment also presents improvements with respect to the ei_S experiment and suggests that the other meteorological forcing from ERA-5 are better represented too. However, it is acknowledged that the use of 3-hourly ERA-Interim liquid and solid precipitations rescaled at an hourly time step in ERA-5 might have sometimes led to inconsistent configurations (e.g. precipitations while having a very strong net radiation).
ERA-5 has a great potential to further improve the representation of LSVs if used to force offline LDAS. In recent years, several LDAS have emerged at different spatial scales, (i) regional like the Coupled Land Vegetation LDAS (CLVLDAS; Sawada and Koike, 2014, Sawada et al., 2015) and the Famine Early Warning Systems Network (FEWSNET) LDAS (FLDAS; McNally et al., 2017), (ii) continental like the North American LDAS (NLDAS; Mitchell et al., 2004; Xia et al., 2012) and the National Climate Assessment LDAS (NCA-LDAS; Kumar et al., 2018), and (iii) global like the Global Land Data assimilation (GLDAS; Rodell et al., 2004) and more recently LDAS-Monde (Albergel et al., 2017, 2018). LDAS-Monde is a global capacity system able to sequentially assimilate satellite-derived estimates of surface soil moisture and LAI. Albergel et al. (2017) found that the main improvements of their analysis (i.e. with assimilation) when compared to an open-loop experiment (simple model run) were linked to vegetation variables and the assimilation of vegetation estimates. They have also proposed further advances on a better use of satellite-based microwave data in the assimilation system. Having LDAS-Monde analysis forced by ERA-5 atmospheric forcing should both combine the strengths of an improved atmospheric reanalysis on the terrestrial water cycle and of the assimilation of satellite-derived products on the vegetation cycle. Effort will now be concentrated on the use of ERA-5 and strengthening LDAS-Monde through the direct assimilation of satellite-based soil moisture and vegetation properties from microwave remote sensing. It will enable fostering links with potential applications like climate reanalysis of the LSVs as well as going from a monitoring system of the LSVs and extreme events (like agricultural drought) to a forecasting system. Preliminary results suggest that a LSV forecast initialized by an analysis is more robust than one initialized by a simple model run (Albergel et al., 2018). Preliminary tests over Europe also indicate similar benefits from the use of ERA-5 (not shown). When the whole ERA-5 period will be available (1979–present), in addition to the availability of the ERA-5 10-member ensemble of data assimilation (at lower spatial and temporal resolutions though), it will be possible to develop a global long-term ensemble of LSV reanalysis forced by high quality atmospheric data. It will make it possible providing uncertainties in the representation of the atmospheric forcing, while LSVs may require special considerations and perturbation methods. Capturing those uncertainties coming from the simplifications and assumptions in the LSM is of paramount interest for many applications from monitoring to forecasting.
The ERA-Interim (ERA-I) and ERA-5 datasets are distributed
by ECMWF (
CA and ED conceived and designed the experiments; CA performed the experiments; all the authors analysed the results; CA wrote the paper.
The authors declare that they have no conflict of interest.
This article is part of the special issue “Integration of Earth observations and models for global water resource assessment”. It is not associated with a conference.
Results were generated using the Copernicus Climate Change Service Information 2017. Emanuel Dutra's work was supported by the Portuguese Science Foundation (FCT) under project IF/00817/2015. Edited by: Frederiek Sperna Weiland Reviewed by: Wolfgang Wagner and one anonymous referee