This article extends a previous study
The 2015 United Nations Climate Change Conference in Paris (COP21) recently
set the goal of limiting global mean temperature increases to “well below
2 degrees” and to pursue efforts to limit warming to 1.5
Example of a plot displaying the functional relationship of a regional climate index (annual maximum of daily
maximum temperature,
Numerous approaches have recently been developed for identifying regional
climate signals associated with specific global warming
targets
We provide an illustration of the display used in S16 in Fig.
The S16 study, which focused on temperature and precipitation extremes for two emissions scenarios (RCP8.5 and RCP4.5),
identified that much of the absolute changes in temperature extremes and heavy precipitation events could be related almost
linearly to the changes in global mean temperature for the time period 1860–2099 (see also Fig.
As a follow-up to the S16 study, we provide several new contributions and analyses. First, we introduce a new web-based
interactive plotting framework (hereafter referred to as the DROUGHT-HEAT Regional Climate Atlas, available via
This section presents the data sources and methods used to produce the DROUGHT-HEAT Regional Climate Atlas. It is structured
as follows: Sect.
The presented regional-scale functional relationships between a range of indices and global mean temperature are derived from
global climate model (GCM) simulations from the Coupled Model Intercomparison Project Phase 5
(CMIP5;
To assess the impact of intra-model spread, we perform our analysis in two steps: using (a) only one ensemble member per model
(r1i1p1) and (b) all members available. Similar to S16, we focus on model simulations over the time period 1861–2099, as
this is the period covered by virtually all models. For the evaluation of the functional relationship with global mean
temperature beyond the end of the century, we also analyse a subset of
simulations spanning all years from 1861 to 2299. For clarity of visual display, we excluded model simulations of the RCP8.5 scenario for which no simulations exist in
the historical period. To facilitate the calculation of regional ensemble averages, all GCM output has been bilinearly
interpolated to a horizontal resolution of
List of models used in this study (in alphabetical order). Crosses (circles) indicate availability of simulations of the ensemble member r1i1p1 for the 1861–2099 (1861–2299) period. Note that the number of simulations of other ensemble members is considerably smaller.
For the ensemble member
In addition to the ETCCDI indices, we have computed three drought indices (which can be used to monitor either anomalously
dry or anomalously wet conditions) based on soil moisture, precipitation, and evapotranspiration from CMIP5 model simulations
(see Sect.
We also include mean temperature (
List of indices (in alphabetical order) as presented in the DROUGHT-HEAT Regional Climate Atlas. Crosses denote indices specifically discussed in this paper as well as indices expressed as percent changes relative to the pre-industrial reference period (1861–1880).
Yearly global mean temperatures
We apply a common land–sea mask at
To test the significance of the functional relationship between the regionally averaged indices and the global mean
temperature signal, we apply an ordinary least squares fit between
To filter out short-term climatic fluctuations, a decadal running mean is applied to the anomalies, starting with 1871–1880
(note that the year associated with each running mean period refers to the last year of that period). We then compute the
unweighed ensemble mean change of the smoothed indices
In order to yield common, model-independent values of
All plots of the regional-scale functional relationships with global mean temperature and related figures similar to those shown in the remainder of this paper are available through the web interface of the DROUGHT-HEAT Regional Climate Atlas. All plots available through this interactive interface are based on the computation of the functional relationship with global mean temperature using the S16 framework as described in the previous section.
The layout and individual components of the DROUGHT-HEAT Regional Climate Atlas are shown in
Fig.
Screenshot of the DROUGHT-HEAT Regional Climate Atlas. For demonstration, this screenshot displays the functional
relationship of
The map in the data panel shows the set of regions for which plots of the chosen diagnostic are available (SREX regions for
this study). Other region sets can be selected by using the drop-down menu on top of the map (e.g. also further regions used in
S16, such as the contiguous US, central Brazil, the Arctic, and southern Asia). Once the user has selected a region (by either
clicking on one of the polygons in the map or by selecting a global domain), the requested plot is displayed in the main
panel of the website. When the appropriate selections are made, a link appears allowing the user to navigate to a set of box
plots showing the distribution of the selected index for fixed global mean temperature targets of
The atlas has been designed to be self-explanatory. Each item in the drop-down lists is accompanied by a short help text that shows up when hovering over it with the mouse. In addition, a pop-up window has been added to provide help for first-time users. Users interested in reusing the results shown in a specific plot can download the related data in comma-separated value (CSV) format.
The DROUGHT-HEAT Regional Climate Atlas is based on a number of web modules served through the Gunicorn web application
server (
The map shown in the data panel of the DROUGHT-HEAT Regional Climate Atlas (see Fig.
There are two processing layers required to produce plots within the framework. First, a locally hosted ncl script serves
static CSV files to the web server. The script writes the data points of each plot series into files inside a unique folder
which represents the diagnostic, region and index. It also generates two customizable files containing plot and series
configuration parameters for each index. In the second (server-sided) layer, the CSV files are read and processed by
JavaScript code. Finally, the Highcharts charting library (
Scaling slopes of the RCP8.5 scenario for
In the following, we demonstrate the capabilities of the DROUGHT-HEAT Regional Climate Atlas by presenting some selected results. We also discuss some more in-depth analyses considering specific features of the assessed functional relationships between regional climate and global mean temperature changes.
Figure
Functional relationships with global mean temperature for the
indices
Like Fig.
For the precipitation-based indices discussed here, the responses are often less pronounced and subject to larger inter-model
uncertainties (Fig.
Overall, the functional relationship is very similar for the four emission scenarios (Figs.
Table
Response of indices
Like Fig.
Figures
A broader overview of the significance in differences between
Significance of differences of
The functional relationships and uncertainty ranges discussed so far are based on one ensemble member (r1i1p1) of the applied
models (see Table
Functional relationships with global mean temperature for the indices
While most CMIP5 model simulations end by the end of the 21st century, a few simulations are available up to the year
2299 (see Table
Like Fig.
The long-term functional relationship of changes in temperature-related indices to changes in global mean temperature is
similar (i.e. mostly linear in the ensemble mean) to the one shown in Fig.
We have developed the “DROUGHT-HEAT Regional Climate Atlas”, a new interactive web interface available via
With the selected results presented here, we have demonstrated that a number of regionally averaged climate indices show
a distinct linear relationship with global mean temperatures both in the ensemble mean and in individual CMIP5 model
realizations, as also illustrated in S16 for a more limited set of indices and emissions scenarios. The linear relationship
is particularly obvious for the analysed temperature-derived indices and still present for a number of drought and water-cycle
indices. We note, however, that some analyses display departures from such linear relationships, in particular in the
case of indices showing a low signal to noise in projections (e.g. in several regions for mean precipitation, dry spell
lengths, soil moisture anomalies, and precipitation minus evapotranspiration). Such departures are generally more pronounced
in the RCP2.6 scenario, because of the weak overall forcing in that emission scenario, and possibly also because of
differences in aerosol forcing in RCP2.6 compared to the other emission scenarios
Projected changes in the indices are overall larger in a
The DROUGHT-HEAT Regional Climate Atlas has been designed to be easily expanded both in terms of functionality (e.g. adding support for additional plot types) and in terms of the number and type of supported data sets and diagnostics. By these means, we facilitate an easy extension of the platform to include graphical material from upcoming publications within the scope of the DROUGHT-HEAT project and beyond.
All code used to prepare the results discussed within this study is available upon request from the first author.
All data produced within this study are available via the
website
Response of indices
Like Fig.
Like Fig.
Like Fig.
Like Fig.
Like Fig.
Like Fig.
Number of models simulating a specific global mean temperature (
The authors declare that they have no conflict of interest.
R. Wartenburger and S. I. Seneviratne acknowledge the European Research Council (ERC) “DROUGHT-HEAT” project funded by the European Community's Seventh Framework Programme (grant agreement FP7-IDEAS-ERC-617518).
This study contributes to the World Climate Research Programme (WCRP) Grand Challenge on Extremes.
We acknowledge the World Climate Research Programme's Working Group on
Coupled Modelling, which is responsible for CMIP, and we thank the
climate modelling groups (listed in Table