public/external-text/default.yaml
app:
title: PCIC Climate Explorer
# Bits and chunks used repeatedly in other parts of this document.
# (Referred to in other elements via `${$$.components}`.)
# For sanity, let's keep these in alphabetical order.
components:
cddCaution: |
Please note the distinction between the variable we label `cdd`,
meaning cooling degree-days, and the Climdex variable `CDD`,
meaning maximum length of dry spell, which we label `cddETCCDI`.
climdexUrl: https://www.climdex.org/learn/indices
contacts:
csg:
name: Computational Support Group
email: climate@uvic.ca
link: "[${$$.components.contacts.csg.name}](mailto:${$$.components.contacts.csg.email})"
science:
# For now, we are directing all inquiries to CSG, and will direct
# as necessary.
name: ${$$.components.contacts.csg.name}
email: ${$$.components.contacts.csg.email}
link: "[${$$.components.contacts.science.name}](mailto:${$$.components.contacts.science.email})"
gcmDefn: |
A [GCM (General Circulation Model)](http://www.ipcc-data.org/guidelines/pages/gcm_guide.html') is
a numerical model representing
physical processes in the atmosphere, ocean, cryosphere and land surface
of the Earth.
GCMs are the most advanced tools currently available for simulating the
response of the global climate system to increasing greenhouse gas
concentrations.
monthlyAnnualVarNameAmbiguityCaution: |
Unlike most Climdex variable names used in PCEX,
this variable name can refer to either an annual version
(calculation spanning a calendar year) or to the standard
Climdex monthly version. The distinction is shown in the
English description part of labels in selectors, so that the
meaning in each specific context is unambiguous.
help:
faq:
title: "# Frequently Asked Questions"
intro: |
The content of our FAQ is driven by our users' needs and questions.
At present, we have relatively few questions and answers here,
because we don't yet know what you, our user, needs to know.
Please [email us](mailto:${$$.components.contacts.csg.email})
with questions you would like to see in the FAQ.
items:
- question: Which model should I select?
answer: |
Canada's own General Circulation Model, CanESM2/CanESM5 (CMIP5/CMIP6 respectively), is the most representative
model for Canada. It is a good default choice if you do not have
specialized needs that would be better represented by a different model.
- question: What emissions scenario should I select?
answer: |
The worst-case emissions scenarios, RCP 8.5 and SSP 5-8.5
(presented in the selectors as `Historical, then RCP 8.5` and `Historical, then SSP 5-8.5` for CMIP5 and CMIP6 respectively),
are good choices for making decisions that adapt to climate change.
- question: How can I upload a polygon successfully?
answer: |
Climate Explorer's polygon upload feature currently has some known
problems. These are slated to be fixed, but in the meantime, here
is a guide to successfully uploading a polygon
## Summary
Below is a summary of recommendations and workarounds.
1. **File format**: It is probably best to upload
[GeoJSON](https://en.wikipedia.org/wiki/GeoJSON) files
(file extension `.geojson` or `.json`). Why?
1. There may be additional problems with Shapefiles.
1. GeoJSON files are human readable and easy to manipulate using
a text editor into the form you need (e.g., Multipolygon, etc.;
see below).
1. **CRS**: Express coordinates in [EPSG:4326](https://epsg.io/4326)
(a.k.a. WGS:84; standard long-lat coordinates).
1. **Polygon length**: Limit your polygons to a maximum of 100 vertices.
This will not affect the accuracy of the results you get from
Climate Explorer. If you have much more detailed polygons, there
are GIS tools that can decimate them for you.
1. **GeoJSON content type**: If it is important to average over more than one polygon,
encode that collection as a Multipolygon.
Otherwise place your polygons into separate files, and upload
each file as needed.
## File format
We have reports from users that they also have problems with
Shapefiles. We have not yet investigated these reports,
so our current recommandation is to use
[GeoJSON](https://en.wikipedia.org/wiki/GeoJSON).
GeoJSON is a text file format, employing the widely used
[JSON](https://en.wikipedia.org/wiki/JSON) standard, that is easy to
understand and modify.
If your GeoJSON file (of whatever provenance)
needs to be changed to be compatible with Climate Explorer,
it should be fairly easy to change using any text editor.
There are online tools for converting shapefiles (and other formats)
to GeoJSON. A search with the phrase "convert shapefile to geojson"
will turn up several candidates.
## CRS (Coordinate Reference System)
Coordinates in any file uploaded to Climate Explorer must be in
standard long-lat coordinates ([EPSG:4326](https://epsg.io/4326)).
It is simplest if you can generate your files directly in EPSG:4325
coordinates.
However, if your files are in a different coordinate system,
there are tools for converting them.
One such tool, useful if you are a user of Node.js, is
[reproject](https://github.com/perliedman/reproject).
## Polygon length
Climate Explorer cannot handle polygons with more than about
400 vertices (coordinates).
(In principle there is no problem with complicated polygons, but
currently there is a limitation on the length of data we can send
from the client application (in the browser) to the Climate Explorer
backend.)
If you have large polygons (many vertices), we suggest that you
define simpler (less detailed) boundaries for these polygons,
using 100 or fewer coordinates per polygon.
This is very unlikely to make any significant difference to the
results you get from Climate Explorer. Our datasets are relatively
coarse spatially, and so the set of grid cells over which spatial
averaging is done is likely to be either identical or very similar --
so similar that there would be no significant difference.
## GeoJSON content type
CE does handle both FeatureCollections and Multipolygons. However ...
* With FeatureCollections, only one (the textually first in the file)
of the polygons can be active at one time.
The others are displayed, but inactive, which is to say that they
do not participate in the spatial averaging.
There is no way at present to have all polygons in a
FeatureCollection contribute to the spatial averaging.
This is not desirable behaviour but it is currently the case.
* For technical reasons, all the polygons in a Multipolygon DO
contribute to the spatial averaging.
This may prompt you to revise how your GeoJSON files are structured.
With a text editor, it is a relatively simple process to convert a
FeatureCollection to a Multipolygon. Please refer to GeoJSON
documentation for more information on these two formats.
- question: How do I zoom in on the map?
answer: |
Click the **+** button to zoom in.
Click the **-** button to zoom out.
Alternatively, use the scroll wheel on your mouse, or, with a touch
screen, pinch to zoom in and spread to zoom out.
- question: |
What are the small triangles in the column labels of tables
like the Statistical Summary for?
answer: |
They are used to sort the table by that column.
Click on a column header to sort by that column.
Click again to change the direction of sorting.
- question: |
What is the difference between CMIP5 and CMIP6?
answer: |
Much of the data in Climate Explorer comes from the Coupled Model Intercomparison Project
(CMIP) phases 5 and 6, which houses output from numerous global climate models. These datasets are
referred to as the Canadian Downscaled Climate Scenarios - Univariate method from CMIP5 and CMIP6 (CanDCS-U5 and CanDCS-U6) respectively.
Since CMIP6 is ongoing and the associated climatologies were added to Climate Explorer very recently,
the available data for CMIP5 is much more extensive.
For CMIP5, the downscaled scenarios in Climate Explorer are constructed from 27 GCMs and 3 Representative Concentration
Pathways (RCPs). The climatologies for these scenarios can be accessed using the `Single Variable` and `Compare Variables`
tabs, and the daily model output can be accessed [here on the PCIC data portal](https://data.pacificclimate.org/portal/downscaled_gcms/map/).
For CMIP6, the downscaled scenarios in Climate Explorer are constructed from 26 GCMs and 3 Shared Socioeconomic
Pathways (SSPs). The climatologies for these scenarios can be accessed using the `Single Variable CMIP6` and `Compare Variables CMIP6`
tabs, and the daily model output can be accessed [here on the PCIC data portal](https://data.pacificclimate.org/portal/downscaled_cmip6/map/).
general:
# This chunk is partway to being ready for presenting the sections as
# separate pages. The real organization of the whole Help data block should
# be approximately as follows:
#
# help:
# components:
# ...
# pages:
# - label: <Page label>
# subpath: <Page subpath>
# title: '# <Section title>'
# content: |
# <Section content>
#
# Currently, help.general.sections contains just the content component
# of the above structure. But it's a start.
title: "# Help: General"
sections:
- |
## Overview
### What it is
PCIC Climate Explorer (PCEX) is an interactive web application that
runs in your browser. It displays data derived from climate models in a
variety of ways, including maps, graphs and data tables.
### Purpose
PCEX’s purpose is to help you do the following things:
* Discover what data derived from climate models is available through the
tool.
* Select datasets based on the model that produced the data, the
emissions scenario that drove the model, and the variable of interest.
* Visualize a selected dataset in a variety of ways, including:
* geospatially (on an interactive map)
* as one or more graphs, typically presenting change over time
* as a statistical summary table
* Download the data shown in a visualization as an Excel compatible
table or a CSV file.
### Application elements
It helps to have a little terminology for the various parts of the
PCEX user interface.
- |
## Data available in PCEX
There are four types of data available in PCEX.
(Click on a heading to expand the full explanation of each type.)
### Model output
Daily temperature and precipitation data output
by global or regional climate models based on a
combination of historical data and possible future greenhouse
gas projections. The data is averaged by month over thirty year
periods; there are six such periods from 1960 to 2100, three
historical and three projected.
This data is available for all of Canada.
* **`pr`**
Precipitation at ground level
* **`tasmax`**
Daily maximum near-surface air temperature
* **`tasmin`**
Daily minimum near-surface air temperature
* **`prsn`**
Precipitation at ground level while mean daily temperature is below freezing
### Climdex (climate extremes indices)
Measures of weather extremes calculated from model output.
Climdex defines 27 [climate extremes indices](${$$.components.climdexUrl}),
encompassing extreme precipitation, extremes of temperature,
or lack thereof in different ways.
This is a heterogeneous data collection with some monthly,
some seasonal, and some annual resolution data. The data is
averaged over 30 year periods; there are six such periods
between 1960 and 2100, three historical and three projected.
This data is available for all of Canada.
* **`altcddETCCDI`**
Dry spell duration index spanning years:
Maximum number of consecutive days in one year with less
than 1 mm of precipitation.
This is an alternative version of the Climdex variable
[`CDD`](${$$.components.climdexUrl}/#index-CDD).
It represents the full length of continuous dry spells
that extend across the (artifical) December-January boundary
of a calendar year.
${$$.components.cddCaution}
* **`altcsdiETCCDI`**
Cold spell duration index spanning years:
Count of days with at least 6 consecutive days when
daiy minimum temperature < 10th percentile.
This is an alternative version of the Climdex variable
[`CSDI`](${$$.components.climdexUrl}/#index-CSDI).
It represents the full length of continuous cold spells
that extend across the (artifical) December-January boundary
of a calendar year.
* **`altcwdETCCDI`**
Maximum length of wet spell spanning years:
Maximum number of consecutive days with at
least 1 mm of precipitation.
This is an alternative version of the Climdex variable
[`CWD`](${$$.components.climdexUrl}/#index-CWD).
It represents the full length of continuous wet spells
that extend across the (artifical) December-January boundary
of a calendar year.
* **`altwsdiETCCDI`**
Warm spell duration index spanning years:
Count of days with at least 6 consecutive days when
daily maximum temperature > 90th percentile.
This is an alternative version of the Climdex variable
[`WSDI`](${$$.components.climdexUrl}/#index-WSDI).
It represents the full length of continuous warm spells
that extend across the (artifical) December-January boundary
of a calendar year.
* **`cddETCCDI`**
Maximum length of dry spell:
Maximum number of consecutive days with less than 1 mm of
precipitation.
For details, see [Climdex variable `CDD`](${$$.components.climdexUrl}/#index-CDD).
${$$.components.cddCaution}
* **`csdiETCCDI`**
Cold spell duration index:
Annual count of days with at least 6 consecutive days when
daiy minimum temperature < 10th percentile.
For details, see [Climdex variable `CSDI`](${$$.components.climdexUrl}/#index-CSDI).
* **`cwdETCCDI`**
Maximum length of wet spell:
Maximum number of consecutive days with at least 1 mm of
precipitation.
For details, see [Climdex variable `CWD`](${$$.components.climdexUrl}/#index-CWD).
* **`dtrETCCDI`**
Mean diurnal temperature range:
Monthly mean difference between daily maximum temperature
and daily minimum temperature.
For details, see [Climdex variable `DTR`](${$$.components.climdexUrl}/#index-DTR).
* **`fdETCCDI`**
Number of frost days:
Annual count of days when daily minimum temperature.
< 0°C.
For details, see [Climdex variable `FD`](${$$.components.climdexUrl}/#index-FD).
* **`gslETCCDI`**
Growing season length.
For details, see [Climdex variable `GSL`](${$$.components.climdexUrl}/#index-GSL).
* **`idETCCDI`**
Number of icing days:
Annual count of days when daily maximum temperature.
< 0°C.
For details, see [Climdex variable `ID`](${$$.components.climdexUrl}/#index-ID).
* **`prcptotETCCDI`**
Annual total precipitation in wet days.
For details, see [Climdex variable `PRCPTOT`](${$$.components.climdexUrl}/#index-PRCPTOT).
* **`r10mmETCCDI`**
Annual count of days with at least 10 mm of precipitation.
For details, see [Climdex variable `R10mm`](${$$.components.climdexUrl}/#index-R10mm).
* **`r1mmETCCDI`**
Annual count of days with at least 1 mm of precipitation.
For details, see [Climdex variable `Rnnmm`](${$$.components.climdexUrl}/#index-Rnnmm).
* **`r20mmETCCDI`**
Annual count of days with at least 20 mm of precipitation.
For details, see [Climdex variable `R20mm`](${$$.components.climdexUrl}/#index-R20mm).
* **`r95pETCCDI`**
Annual total precipitation when daily precipitation exceeds the
95th percentile of wet day precipitation
For details, see [Climdex variable `R95pTOT`](${$$.components.climdexUrl}/#index-R95pTOT).
* **`r99pETCCDI`**
Annual total precipitation when daily precipitation exceeds the
99th percentile of wet day precipitation
For details, see [Climdex variable `R99pTOT`](${$$.components.climdexUrl}/#index-R99pTOT).
* **`rx1dayETCCDI`**
Annual Maximum 1-day Precipitation
For details, see [Climdex variable `Rx1day`](${$$.components.climdexUrl}/#index-Rx1day).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`rx1dayETCCDI`**
Monthly Maximum 1-day Precipitation
For details, see [Climdex variable `Rx1day`](${$$.components.climdexUrl}/#index-Rx1day).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`rx5dayETCCDI`**
Annual Maximum Consecutive 5-day Precipitation
For details, see [Climdex variable `Rx5day`](${$$.components.climdexUrl}/#index-Rx5day).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`rx5dayETCCDI`**
Monthly Maximum Consecutive 5-day Precipitation
For details, see [Climdex variable `Rx5day`](${$$.components.climdexUrl}/#index-Rx5day).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`sdiiETCCDI`**
Simple precipitation intensity index.
For details, see [Climdex variable `SDII`](${$$.components.climdexUrl}/#index-SDII).
* **`suETCCDI`**
Number of summer days:
Annual count of days when daily maximum temperature > 25°C.
For details, see [Climdex variable `SU`](${$$.components.climdexUrl}/#index-SU).
* **`tn10pETCCDI`**
Percentage of days when daily minimum temperature is below the
10th percentile.
For details, see [Climdex variable `TN10p`](${$$.components.climdexUrl}/#index-TN10p).
* **`tn90pETCCDI`**
Percentage of days when daily minimum temperature is above the
90th percentile
For details, see [Climdex variable `TN90p`](${$$.components.climdexUrl}/#index-TN90p).
* **`tnnETCCDI`**
Annual minimum of daily minimum temperature
For details, see [Climdex variable `TNn`](${$$.components.climdexUrl}/#index-TNn).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`tnnETCCDI`**
Monthly minimum of daily minimum temperature
For details, see [Climdex variable `TNn`](${$$.components.climdexUrl}/#index-TNn).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`tnxETCCDI`**
Annual maximum of daily minimum temperature
For details, see [Climdex variable `TNx`](${$$.components.climdexUrl}/#index-TNx).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`tnxETCCDI`**
Monthly maximum of daily minimum temperature
For details, see [Climdex variable `TNx`](${$$.components.climdexUrl}/#index-TNx).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`trETCCDI`**
Number of tropical nights:
Annual count of days when daily minimum temperature) exceeds 20°C.
For details, see [Climdex variable `TR`](${$$.components.climdexUrl}/#index-TR).
* **`tx10pETCCDI`**
Percentage of days when daily maximum temperature is below the
10th percentile
For details, see [Climdex variable `TX10p`](${$$.components.climdexUrl}/#index-TX10p).
* **`tx90pETCCDI`**
Percentage of days when daily maximum temperature is above the
90th percentile
For details, see [Climdex variable `TX90p`](${$$.components.climdexUrl}/#index-TX90p).
* **`txnETCCDI`**
Annual minimum of daily maximum temperature
For details, see [Climdex variable `TXn`](${$$.components.climdexUrl}/#index-TXn).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`txnETCCDI`**
Monthly minimum of daily maximum temperature
For details, see [Climdex variable `TXn`](${$$.components.climdexUrl}/#index-TXn).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`txxETCCDI`**
Annual maximum of daily maximum temperature
For details, see [Climdex variable `TXx`](${$$.components.climdexUrl}/#index-TXx).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`txxETCCDI`**
Monthly maximum of daily maximum temperature
For details, see [Climdex variable `TXx`](${$$.components.climdexUrl}/#index-TXx).
${$$.components.monthlyAnnualVarNameAmbiguityCaution}
* **`wsdiETCCDI`**
Warm spell duration index:
Count of days with at least 6 consecutive days when
daily maximum temperature > 90th percentile.
For details, see [Climdex variable `WSDI`](${$$.components.climdexUrl}/#index-WSDI).
### Degree-day variables
Calculated from model output, a degree-day variable
counts how many days fall below or above a given temperature
threshold multiplied by how many degrees the threshold is exceeded,
over a period of a season or year.
The data is averaged over six thirty year periods between
1960 to 2100, three historical and three projected.
This data is available for all of Canada.
A degree-day is a measure of how much
the actual temperature (usually the mean average temperature)
falls either above or below a threshold temperature that
represents a temperature of
interest (e.g., freezing, temperature at which cooling is
required).
For a given degree-day measure,
the difference is only counted when the actual
temperature is either above or below the threshold,
the condition (above, below) being given as part of the
measure's definition.
One degree day is one day with a temperature
difference from threshold of 1 degree (in Canada, °C).
A day with a temperature difference of 3 degrees represents
3 degree-days. The total degree-days over a given period
(e.g., a month, a year) is the total degree-days for each day
in that period, always respecting both the threshold and the
condition (above, below) in counting each day. This means that
when both seasonal and annual degree day data are viewed in a
chart or table, the annual values will be larger, as they
represent the sum of the seasonal values.
* **`cdd`**
Cooling Degree Days: Degree-days during the specified time
period above 18°C.
* **`hdd`**
Heating Degree Days: Degree-days during the specified time
period below 18°C.
* **`gdd`**
Growing Degree Days: Degree-days during the specified time
period above 5°C.
* **`fdd`**
Frost Degree Days: Degree-days during the specified time
period below 0°C.
### Return period variables
A return period variable describes extreme temperature or
precipitation events that would be expected to occur once
during a specified "return period," for example once every
20 years.
These datasets are calculated from model output using a
generalized extreme value distribution.
This data is available for six thirty year climatological
periods from 1960 to 2100, three historical and three
projected.
This data is available for all Canada.
* **`rp5pr`**
5-year annual maximum one day precipitation amount
* **`rp20pr`**
20-year annual maximum one day precipitation amount
* **`rp50pr`**
50-year annual maximum one day precipitation amount
* **`rp5tasmax`**
5-year annual maximum daily maximum temperature
* **`rp20tasmax`**
20-year annual maximum daily maximum temperature
* **`rp50tasmax`**
50-year annual maximum daily maximum temperature
* **`rp5tasmin`**
5-year annual minimum daily minimum temperature
* **`rp20tasmin`**
20-year annual minimum daily minimum temperature
* **`rp50tasmin`**
50-year annual minimum daily minimum temperature
### Annual maximum streamflow quantiles
The streamflow quantiles describe daily extreme streamflow events
that would be expected to occur once during a specified
"return period," for example once every 20 years.
These datasets are calculated empirically from VIC-GL
simulated streamflow driven with the CanESM2 large ensemble,
which is forced by the RCP8.5 concentration pathway scenario.
The data is available for the period 1951 to 2100 and is divided
into a baseline period of 1951-2001 and eight overlapping
thirty-year periods of 2001-2031, 2011-2041, 2021-2051,
2031-2061, 2041-2071, 2051-2081, 2061-2091 and 2071-2100.
This data is available for the Fraser River basin above
tidewater and the Peace River basin above Peace River,
Alberta. Each quantile estimate is also provided with the 95%
sampling interval provided as the 2.5th and 97.5th percentiles.
* **`rp2streamflow`**
2-year annual maximum one day streamflow
* **`rp5streamflow`**
5-year annual maximum one day streamflow
* **`rp10streamflow`**
10-year annual maximum one day streamflow
* **`rp20streamflow`**
20-year annual maximum one day streamflow
* **`rp50streamflow`**
50-year annual maximum one day streamflow
* **`rp100streamflow`**
100-year annual maximum one day streamflow
* **`rp200streamflow`**
200-year annual maximum one day streamflow
### Change factors for annual maximum atreamflow quantiles
Change factors for the streamflow quantiles describe the change
in daily extreme streamflow events for a specified return period
and are estimated as the ratio of future to baseline (1951-2000)
design flow value. Change factors are available for eight
overlapping thirty-year periods of 2001-2031, 2011-2041,
2021-2051, 2031-2061, 2041-2071, 2051-2081, 2061-2091 and
2071-2100. This data is available for the Fraser River basin
above tidewater and the Peace River basin above the town of Peace
River, Alberta. Each change factor estimate is also provided with
the 95% sampling interval provided as the 2.5th and 97.5th
percentiles.
* **`cfrp2streamflow`**
change factor for 2-year annual maximum one day streamflow
* **`cfrp5streamflow`**
change factor for 5-year annual maximum one day streamflow
* **`cfrp10streamflow`**
change factor for 10-year annual maximum one day streamflow
* **`cfrp20streamflow`**
change factor for 20-year annual maximum one day streamflow
* **`cfrp50streamflow`**
change factor for 250-year annual maximum one day streamflow
* **`cfrp100streamflow`**
change factor for 100-year annual maximum one day streamflow
* **`cfrp200streamflow`**
change factor for 200-year annual maximum one day streamflow
- |
## Models (GCMs)
${$$.components.gcmDefn}
In PCEX, models are indentified by short codes.
The following tables give the full name and provenance of these models.
#### CMIP5
* **`ACCESS1-0`**
[Australian Community Climate and Earth System Simulator
coupled model](http://www.bom.gov.au/jshess/docs/2013/bi1_hres.pdf)
* **`BNU-ESM`**
[Beijin Normal University
Earth System Model](https://www.researchgate.net/publication/262954172_Description_and_basic_evaluation_of_BNU-ESM_version_1)
* **`CCSM4`**
[U.S. National Center for Atmospheric Research
CCSM4 4.0 model](http://www.cesm.ucar.edu/models/ccsm4.0/)
* **`CESM1-CAM5`**
[Community Earth System Model version 1 that includes the Community Atmospheric Model version 5](https://journals.ametsoc.org/view/journals/clim/26/17/jcli-d-12-00572.1.xml)
* **`CNRM-CM5`**
[France Centre National de Recherches Météorologiques
(National Centre for Meteorological Research)
CNRM-CM5 model](http://www.umr-cnrm.fr/spip.php?article126&lang=en)
* **`CSIRO-Mk3-6-0`**
[Australia Commonwealth Scientific and Industrial Research Organisation
CSIRO-Mk3.6.0 model](https://confluence.csiro.au/public/CSIROMk360)
* **`CanESM2`**
[Canadian Centre for Climate Modelling and Analysis
ESM2 (Earth System Model ver. 2)](https://climate-modelling.canada.ca/climatemodeldata/cgcm4/CanESM2/index.shtml)
* **`FGOALS-g2`**
[China LASG
(Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics)
FGOALS-g2 model](http://www.lasg.ac.cn/fgoals/index2.asp)
* **`GFDL-CM3`**
[U.S. Geophysical Fluid Dynamics Laboratory
Coupled Physical Model CM3](https://www.gfdl.noaa.gov/coupled-physical-model-cm3/)
* **`GFDL-ESM2G`**
[U.S. Geophysical Fluid Dynamics Laboratory
ESM2G model](https://www.gfdl.noaa.gov/earth-system-model/)
* **`GFDL-ESM2M`**
[U.S. Geophysical Fluid Dynamics Laboratory
ESM2M model](https://www.gfdl.noaa.gov/earth-system-model/)
* **`HadGEM2-AO`**
[U.K. Met Office
HadGEM2 AO
(Troposphere, Land Surface & Hydrology,
Aerosols, Ocean & Sea-ice) model](https://www.geosci-model-dev.net/4/723/2011/gmd-4-723-2011.pdf)
* **`HadGEM2-CC`**
[U.K. Met Office
HadGEM2 CC
(Troposphere, Land Surface & Hydrology,
Aerosols, Ocean & Sea-ice, Terrestrial
Carbon Cycle, Ocean Biogeochemistry)](https://www.geosci-model-dev.net/4/723/2011/gmd-4-723-2011.pdf)
* **`HadGEM2-ES`**
[U.K. Met Office
HadGEM2 ES
(Troposphere, Land Surface & Hydrology,
Aerosols, Ocean & Sea-ice, Terrestrial
Carbon Cycle, Ocean Biogeochemistry,
Chemistry)
model](https://www.geosci-model-dev.net/4/723/2011/gmd-4-723-2011.pdf)
* **`IPSL-CM5A-LR`**
[France Institut Pierre Simon Laplace
Climate Model 5A (low resolution)](https://gmd.copernicus.org/articles/13/3011/2020/gmd-13-3011-2020.pdf)
* **`IPSL-CM5A-MR`**
[France Institut Pierre Simon Laplace
Climate Model 5A (medium resolution)](https://gmd.copernicus.org/articles/13/3011/2020/gmd-13-3011-2020.pdf)
* **`MIROC-ESM`**
[Japan Agency for Marine-Earth Science and Technology;
Atmosphere and Ocean Research Institute;
Centre for Climate System Research -
National Institute for Environmental Studies
MIROC-ESM
(Model for Interdisciplinary Research on Climate
Earth System Model)](https://www.researchgate.net/publication/253418457_MIROC-ESM_model_description_and_basic_results_of_CMIP5-20c3m_experiments)
* **`MIROC-ESM-CHEM`**
[Japan Agency for Marine-Earth Science and Technology;
Atmosphere and Ocean Research Institute;
Centre for Climate System Research -
National Institute for Environmental Studies
MIROC-ESM-CHEM
(Model for Interdisciplinary Research on Climate
Earth System Model with Atmospheric Chemistry)](https://www.researchgate.net/publication/253418457_MIROC-ESM_model_description_and_basic_results_of_CMIP5-20c3m_experiments)
* **`MIROC5`**
[Japan Agency for Marine-Earth Science and Technology;
Atmosphere and Ocean Research Institute;
Centre for Climate System Research -
National Institute for Environmental Studies
MIROC5
(Model for Interdisciplinary Research on Climate, ver. 5)](https://journals.ametsoc.org/doi/10.1175/2010JCLI3679.1)
* **`MPI-ESM-LR`**
[Germany Max Planck Institute
ESM-LR (Earth System Model - low resolution)](https://www.mpimet.mpg.de/en/science/models/mpi-esm/)
* **`MPI-ESM-MR`**
[Germany Max Planck Institute
ESM-MR (Earth System Model - medium resolution)](https://www.mpimet.mpg.de/en/science/models/mpi-esm/)
* **`MRI-CGCM3`**
[Japan Meteorological Research Institute
CCGM3 model](https://www.jstage.jst.go.jp/article/jmsj/90A/0/90A_2012-A02/_pdf)
* **`NorESM1-M`**
[Norwegian Climate Center (NCC)
Norwegian Earth System Model version 1 (medium resolution)](https://gmd.copernicus.org/articles/6/687/2013/)
* **`NorESM1-ME`**
[Norwegian Climate Center (NCC)
Norwegian Earth System Model version 1 (prognostic biogeochemical cycling)](https://gmd.copernicus.org/articles/6/687/2013/)
* **`bcc-csm1-1`**
[China Beijing Climate Center
Climate System Model version 1.1](http://forecast.bcccsm.ncc-cma.net/web/channel-63.htm)
* **`bcc-csm1-1-m`**
[China Beijing Climate Center
Climate System Model version 1.1 (moderate resolution)](http://forecast.bcccsm.ncc-cma.net/web/channel-63.htm)
* **`inmcm4`**
[Russia Institute for Numerical Mathematics
Climate Model Version 4](https://www.researchgate.net/publication/227251252_Simulating_present-day_climate_with_the_INMCM40_coupled_model_of_the_atmospheric_and_oceanic_general_circulations)
#### CMIP6
* **`ACCESS-CM2`**
[Australian Community Climate and Earth System Simulator
coupled model ver. 2](https://research.csiro.au/access/about/cm2/)
* **`ACCESS-ESM1-5`**
[Australian Community Climate and Earth System Simulator
Earth System Model ver 1.5](https://research.csiro.au/access/about/esm1-5/)
* **`BCC-CSM2-MR`**
[China Beijing Climate Center
Climate System Model version 2 (medium-resolution)](https://gmd.copernicus.org/articles/12/1573/2019/)
* **`CMCC-ESM2`**
[Italy Centro Euro-Mediterraneo sui Cambiamenti Climatici
(Euro-Mediterranean Center on Climate Change)
Earth System Model ver. 2](https://www.cmcc.it/models/cmcc-esm-earth-system-model)
* **`CNRM-CM6-1`**
[France Centre National de Recherches Météorologiques
(National Centre for Meteorological Research)
CNRM-CM6-1 model](http://www.umr-cnrm.fr/cmip6/spip.php?article11)
* **`CNRM-ESM2-1`**
[France Centre National de Recherches Météorologiques
(National Centre for Meteorological Research)
CNRM-ESM2-1 model](http://www.umr-cnrm.fr/cmip6/spip.php?article10)
* **`CanESM5`**
[Canadian Centre for Climate Modelling and Analysis
ESM5 (Earth System Model ver. 5)](https://gmd.copernicus.org/articles/12/4823/2019/gmd-12-4823-2019.html)
* **`EC-Earth3`**
[European Community
Earth System Model ver. 3](http://www.ec-earth.org/about/)
* **`EC-Earth3-Veg`**
[European Community
Earth System Model ver. 3 (vegetation configuration)](https://gmd.copernicus.org/preprints/gmd-2020-446/gmd-2020-446.pdf)
* **`FGOALS-g3`**
[China LASG
(Laboratory of Numerical Modeling for Atmospheric Sciences
and Geophysical Fluid Dynamics)
FGOALS-g3 model](https://link.springer.com/article/10.1007/s00376-020-2032-0)
* **`GFDL-ESM4`**
[U.S. Geophysical Fluid Dynamics Laboratory
ESM4 model](https://www.gfdl.noaa.gov/earth-system-esm4/)
* **`HadGEM3-GC31-LL`**
[U.K. Met Office
HadGEM3 GC 3.1
(N96ORCA1 resolution)
model](https://ukesm.ac.uk/cmip6/)
* **`INM-CM4-8`**
[Russia Institute for Numerical Mathematics
Climate Model Version 4.8](https://catalogue.ceda.ac.uk/uuid/17179dfb6bc24bbeaba902928c91e5c0)
* **`INM-CM5-0`**
[Russia Institute for Numerical Mathematics
Climate Model Version 5.0](https://catalogue.ceda.ac.uk/uuid/5b50fd3fe7b74a64be91fb7ebb8c21a9)
* **`IPSL-CM6A-LR`**
[France Institut Pierre Simon Laplace
Climate Model 6A (low resolution)](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2019MS002010)
* **`KACE-1-0-G`**
[National Institute of Meteorlogical Sciences (NIMS) and Korea Meteorlogical Administration (KMA)
KACE-1-0-G model](https://catalogue.ceda.ac.uk/uuid/db66f62b4b0748fd8b12fd2f3fa327b1)
* **`KIOST-ESM`**
[Korea Institude of Ocean Science and Technology
Earth System Model](https://www.researchgate.net/publication/351333170_Korea_Institute_of_Ocean_Science_and_Technology_Earth_System_Model_and_Its_Simulation_Characteristics)
* **`MIROC-ES2L`**
[Japan Agency for Marine-Earth Science and Technology;
Atmosphere and Ocean Research Institute;
Centre for Climate System Research -
National Institute for Environmental Studies
MIROC-ES2L
(Model for Interdisciplinary Research on Climate, version 2 for Long-term simulations)](https://gmd.copernicus.org/articles/13/2197/2020/)
* **`MIROC6`**
[Japan Agency for Marine-Earth Science and Technology;
Atmosphere and Ocean Research Institute;
Centre for Climate System Research -
National Institute for Environmental Studies
MIROC-ES2L
(Model for Interdisciplinary Research on Climate, ver. 6)](https://gmd.copernicus.org/articles/12/2727/2019/gmd-12-2727-2019.html)
* **`MPI-ESM1-2-HR`**
[Germany Max Planck Institute
ESM (Earth System Model ver 1.2 high resolution)](https://gmd.copernicus.org/articles/12/3241/2019/)
* **`MPI-ESM1-2-LR`**
[Germany Max Planck Institute
ESM (Earth System Model ver 1.2 low resolution)](https://gmd.copernicus.org/articles/12/3241/2019/)
* **`MRI-ESM2-0`**
[Japan Meteorological Research Institute
Earth System Model ver 2.0](https://www.jstage.jst.go.jp/article/jmsj/advpub/0/advpub_2019-051/_article/-char/en)
* **`NorESM2-LM`**
[Norwegian Climate Center (NCC)
Norwegian Earth System Model version 2;
2 degree resolution for the atmosphere and land components;
1 degree resolution for the ocean and sea-ice components;
CO2 concentration driven (default)](https://noresm-docs.readthedocs.io/en/latest/start.html)
* **`NorESM2-MM`**
[Norwegian Climate Center (NCC)
Norwegian Earth System Model version 2;
1 degree resolution for all model components](https://noresm-docs.readthedocs.io/en/latest/start.html)
* **`TaiESM1`**
[Taiwan Earth System Model version 1](https://agupubs.onlinelibrary.wiley.com/doi/full/10.1029/2020MS002353)
* **`UKESM1-0-LL`**
[U.K. Met Office
UK Earth System Model 1.0
(low atmosphere/ocean resolution)
model](https://ukesm.ac.uk/cmip6/)
- |
## Datasets and data filtering
PCEX has a huge base of data available—far too much
to present usefully in any single view. A selection (filtering)
process must come between data and presentation.
### Datasets
A _dataset_ is a collection of data for a specific model,
emissions scenario, variable, model run, and time period. It
comprises values of the variable for specific points in space and
time, usually over a regular spatial grid and sequence of time
points.
Specifically, a dataset is a collection of geospatial
(longitude-latitude) grids. Each geospatial grid holds the data for
a particular
time. (Conversely, you could think of a dataset as a geospatial grid
of time series, but the data is in fact stored as a grid per time
value.)
### Dataset filtering
The first step of any effort to examine the available data is
to select a smaller, more digestible subset of it to be examined.
This selection goes by the name of _dataset filtering_ or
just _filtering_.
The criteria by which datasets are filtered are:
* **Model**
Which GCM produced the base data for the dataset.
(Almost all data available in PCEX is further
processed
from this base data. Specifically, most of the data available
has
been downscaled from the relatively coarse global grid of the
GCM to
a finer grid suited to regional analysis. Other post-processing
includes forming long-term averages and forming derived
variables
such as climate indices.)
* **Emissions Scenario**
Which scenario of climate-changing emissions (greenhouse gases,
etc.) was used as an input to the model runs.
* **Variable(s)**
Which output variable(s) from the model runs
you are interested in. (For example: maximum temperature,
precipitation, number of frost-free days.)
The result of data filtering is a collection of one or
more _datasets_.
#### Distinguishing datasets within a filtered collection
Filtering (by model, emissions scenario, variable) in general
results in more than one dataset.
Individual datasets within a filtered collection are characterized by:
* **Model Run**
The following explanation is taken
from [ENES](https://portal.enes.org/data/enes-model-data/cmip5/datastructure):
> Many CMIP5 experiments, the so-called ensemble calculations,
were calculated using several initial states, initialisation
methods or physics details. Ensemble calculations facilitate
quantifying the variability of simulation data concerning a
single model. For example, climate model simulations are
dependent on the initial state. The variability we know from
weather is also existent in climate simulations. The ensemble
members with different initial states are usually called
realizations. Initialisation method and physics details may also
have an influence. Physics details may be parameterisation
constants, for example. In the CMIP5 project, ensemble members
are named in the rip-nomenclature, r for realization, i for
initialisation and p for physics, followed by an integer, e.g.
r1i1p1.
You will find all datasets in CE labelled by a rip code as above.
* **Time Period**
The time period the dataset spans.
- |
## Data presentations
PCEX presents data in several different ways. The
following is a summary of the different presentations available.
* **Data Map**
The Data Map is an interactive web map that presents one or two
datasets selected from the filtered collection. It shows a
spatial slice of the data for a specific point in time.
A single variable (or the primary variable in a comparison view)
is represented as a raster (a grid of coloured blocks)
overlaid on the base map.
Colours encode the variable’s value.
The secondary variable (in a comparison view) is represented as
a set of isolines (contours of constant value) overlaid on the
base map. Isolines are colour-coded by value.
The data map is the most complex data presentation tool, and
has a
substantial collection of generic web mapping features and data
presentation features. See below for details of these
features.
* **Data Graphs**
A data graph typically presents a non-spatial view of one or
more datasets. Typically this view is temporal, that is, it is a
graph with time as the horizontal axis.
Depending on the specific graph, more than one dataset may be
represented. This is useful for comparing datasets and/or giving
context to the dataset(s) displayed in the data map.
IMPORTANT: Spatial averaging: Data shown in all graphs is averaged
over either the entire spatial extent of the dataset or over the
spatial extent you select by drawing a polygon on the map.
* **Statistical Summary**
The Statistical Summary table presents a statistical summary of
a single dataset. The summary includes the usual statistics such
as mean, minimum, maximum, standard deviation, etc.
IMPORTANT: Spatial averaging: Data summarized in this table is
averaged over either the entire spatial extent of the dataset or
over the spatial extent you select by drawing a polygon on the
map.
- |
## Data Map features
### Map tools
On the left-hand side of the map you will find a standard
selection of web map tools.
**Note on terminology**: “Layer”: Technically a
polygon is a “map layer.” You only need to know this because it
is the terminology used in the the tooltips for the various
polygon-drawing tools. Read “layer” as “polygon.”
#### Zoom In / Zoom Out
To zoom in or out on the map:
1. Click the **+** (zoom in) or **-** (zoom out) button on the map
toolbar.
1. Alternatively, roll the mouse wheel forward (zoom in) or backward
(zoom out) with the mouse cursor over the map.
1. Alternatively, on a touch screen, spread (zoom in) or pinch
(zoom out) on the map.
#### Draw Polygon
To draw a general polygon on the map:
1. Click the **Draw Polygon** button on the map toolbar.
1. Click to place a corner of the polygon.
1. Click on the first point placed to complete the polygon.
#### Draw Rectangle
To draw a rectangular area on the map. (It is a rectangle in pixel
coordinates, not in projection coordinates.)
1. Click the **Draw Rectangle** button on the map toolbar.
1. On the map, click on one corner of the desired rectangle,
drag to the opposite corner and release.
#### Edit Polygon
To edit an existing polygon on the map:
1. Click the **Edit Polygon** button on the map toolbar.
1. The mouse cursor becomes a pointer.
**Save** and **Cancel** buttons appear next to the **Edit Polygon** button.
1. Editing handles (square boxes) appear at each polygon vertex and
at the midpoint of each polygon side. Vertex handles are opaque white;
midpoint handles are semitransparent white.
1. To move a vertex, click on its handle and drag.
Release at the desired new position.
1. To create a new vertex, click on a midpoint handle and drag.
Release at the desired new position.
1. When you have made all desired changes to the polygon, click
the **Save** button next to the **Edit Polygon** button.
To discard all changes, click the **Cancel** button.
1. The modified polygon is saved and the editing handles disappear.
#### Delete Polygons (Layers)
To delete a single polygon:
1. Click the **Delete Polygons** button on the map toolbar.
1. The mouse cursor becomes a pointer.
**Save** and **Cancel** buttons appear next to the **Edit Polygon** button.
1. Click on the polygon to delete.
1. The selected polygon disappears.
1. Click the **Save** button beside the **Delete Polygons** button.
1. To cancel (individual) deletions and close the tool, click **Cancel**.
To delete all polygons:
1. Click the **Delete Polygons** button on the map toolbar.
1. The mouse cursor becomes a pointer.
**Save** and **Cancel** buttons appear next to the **Edit Polygon** button.
1. Click the **Clear All** button.
All polygons are immediately deleted without confirmation.
#### Import Polygon
Imports a polygon defined in an external file. Accepts a zipped
Shapefile or a GeoJSON file containing a single Feature (not a
FeatureCollection).
To import a polygon:
1. Click the **Import Polygon** button on the map toolbar.
1. Click **Choose File**.
1. Navigate to and select the file containing the polygon definition.
1. Click **OK**.
1. The imported polygon is added to your map.
#### Export Polygon
Exports a polygon in one of the following formats: Shapefile,
GeoJSON, WKT, KML, GPX.
To export a polygon:
1. Click the **Export Polygon** button.
1. Click a button for the desired export file format.
1. Navigate to the desired location to save the file and enter
a name for the file.
1. Click **Save**.
1. The polygon on your map is saved to the file in the
selected format.
### Colour scales and autoscaling
On the right-hand side of the map, you will find one or two colour
scale references and the autoscale (AS) button.
#### Colour scales
A _colour scale_ is a mapping between values that points in
the dataset can take on and the colour displayed on the map to
represent that data point.
Mappings from data value to colour essentially treat colour as
one-dimensional real variable ranging from 0 (first colour) to 1
(last colour). For the purposes of discussion, we call this colour
variable the “colour index.”
In general, a colour scale maps data values lying between chosen
minimum and maximum values, V<sub>min</sub> and V<sub>max</sub>,
respectively. Those values are usually determined by the value range
of the dataset. Sometimes they are actually the dataset minimum and
maximum values; sometimes they are derived from other, comparison
datasets. To convey the maximum amount of information to the human
user, the minimum and maximum values should be close to the dataset’s
minimum and maximum values.
We offer two different types of colour scale, linear and
logarithmic. These are distinguished by how data values are mapped
onto the colour index:
* Linear: Maps the value of the data point linearly to colour
index, with V<sub>min</sub> mapping to colour index 0, and
V<sub>max</sub>
mapping to colour index 1.
* Logarithmic: Maps the logarithm of the data point linearly
to colour index, with log(V<sub>min</sub>) mapping to colour
index 0, and log(V<sub>max</sub>) mapping to colour index 1.
#### Colour scale references
A _colour scale reference_ is a map tool that shows
the relationship between a colour displayed on the map and the value
of the data (variable) represented by that colour. Three
representative data values are shown to the left of the colour band:
* Top: V<sub>max</sub>
* Middle: Mean value.
* If linear colour scale, arithmetic mean =
(V<sub>min</sub> + V<sub>max</sub>)/2.
* If logarithmic colour scale, geometric mean =
sqrt(V<sub>min</sub> * V<sub>max</sub>).
* Bottom: V<sub>min</sub>
When only one variable is displayed on the map (as a raster),
there is one colour scale reference.
When two colour-mapped variables are displayed on the map (raster and
isolines), there are two colour scale references. The upper reference
is for isolines and the lower reference is for raster.
#### Autoscaling
_Autoscaling_ readjusts the colour scale to span only the range of
data values that are currently visible on the map.
For example, suppose
the entire map shows a range of values between 0 and 100. Then you
zoom in on a small area where the range of data values lies between,
say, 20 and 30. The colour scale for the 0 to 100 range will not show
much distinction, if any, between values between 20 and 30. Click the
autoscale (**AS**) button, and the colour scale is readjusted so that
its minimum value is 20 and maximum value is 30.
To reset the colour scale to the full data range, zoom out so that
all data is in view on the map, then click **AS**.
- |
## Exported data file formats</h2>
### File formats: XSLX and CSV
Both file formats convey information as table with rows and
columns, as typically managed by spreadsheet programs.
* **XSLX**
This format is compatible with Microsoft Excel.
* **CSV**
This format is plain text in the comma-separated variables format,
with column separator being the comma (`,`).
### Content formats
Each graph or data table is exported in a table layout suitable
to its content. The following sections detail each such layout.
#### Annual Cycle graph
<table>
<thead>
<tr>
<th>Row number(s)</th>
<th>Contents</th>
</tr>
</thead>
<tbody>
<tr>
<td>1–2</td>
<td>Information identifying the dataset(s) presented in this
graph.</td>
</tr>
<tr>
<td>1</td>
<td>Names of dataset selection criteria
(e.g., Model, Emissions Scenario). These define the dataset
filter criteria and data subselections within the graph.</td>
</tr>
<tr>
<td>2</td>
<td>Values of dataset selection criteria.</td>
</tr>
<tr>
<td>3</td>
<td>blank</td>
</tr>
<tr>
<td>4–</td>
<td>Values of data points presented in this graph.</td>
</tr>
<tr>
<td>4</td>
<td>
Headings for data columns.
</td>
</tr>
<tr>
<td>5–</td>
<td>
<p>Data point values.</p>
<p>Column <code>Time Series</code> identifies the curve on
the graph,
one of yearly, seasonal, or monthly mean values.</p>
<p>The next 12 columns give the monthly values for each curve.
Note that there is a monthly value for each curve; for curves
with less than monthly resolution (seasonal, yearly),
values are repeated for the appropriate groups of months.</p>
<p>The <code>units</code> column gives the units of measure
for the data values.</p>
</td>
</tr>
</tbody>
</table>
#### Long Term Average graph
<table>
<thead>
<tr>
<th>Row number(s)</th>
<th>Contents</th>
</tr>
</thead>
<tbody>
<tr>
<td>1–2</td>
<td>Information identifying the dataset(s) presented in this
graph.</td>
</tr>
<tr>
<td>1</td>
<td>Names of dataset selection criteria
(e.g., Model, Emissions Scenario). These define the dataset
filter criteria and data subselections within the graph.</td>
</tr>
<tr>
<td>2</td>
<td>Values of dataset selection criteria.</td>
</tr>
<tr>
<td>3</td>
<td>blank</td>
</tr>
<tr>
<td>4–</td>
<td>Values of data points presented in this graph.</td>
</tr>
<tr>
<td>4</td>
<td>
Headings for data columns.
</td>
</tr>
<tr>
<td>5–</td>
<td>
<p>Data point values.</p>
<p>Column <code>Run</code> identifies the curve on the graph,
one of the <em>r-i-p</em> run codes.</p>
<p>The next 6 columns give the values for each data point
on the curve, identified by the mid-point of the averaging
period (e.g., <code>2085-01-15</code>).</p>
<p>The <code>units</code> column gives the units of measure
for the data values.</p>
</td>
</tr>
</tbody>
</table>
#### Timeseries graph
<table>
<thead>
<tr>
<th>Row number(s)</th>
<th>Contents</th>
</tr>
</thead>
<tbody>
<tr>
<td>1–2</td>
<td>Information identifying the dataset(s) presented in this
graph.</td>
</tr>
<tr>
<td>1</td>
<td>Names of dataset selection criteria
(e.g., Model, Emissions Scenario). These define the dataset
filter criteria and data subselections within the graph.</td>
</tr>
<tr>
<td>2</td>
<td>Values of dataset selection criteria.</td>
</tr>
<tr>
<td>3</td>
<td>blank</td>
</tr>
<tr>
<td>4–</td>
<td>Values of data points presented in this graph.</td>
</tr>
<tr>
<td>4</td>
<td>
Headings for data columns.
</td>
</tr>
<tr>
<td>5–</td>
<td>
<p>Data point values.</p>
<p>Column <code>Time Series</code> identifies the data series
associated with each curve on the graph.</p>
<p>Additional columns have a timestamp and the data values
associated with that timestamp.</p>
<p>The <code>units</code> column gives the units of measure
for the data values.</p>
</td>
</tr>
</tbody>
</table>
#### Change from Baseline graph
See Annual Cycle graph
#### Statistical Summary table
<Table bordered condensed responsive>
<thead>
<tr>
<th>Row number(s)</th>
<th>Contents</th>
</tr>
</thead>
<tbody>
<tr>
<td>1–2</td>
<td>Information identifying the dataset(s) presented in this
table.</td>
</tr>
<tr>
<td>1</td>
<td>Names of dataset selection criteria
(e.g., Model, Emissions Scenario). These define the dataset
filter criteria and data subselections within the graph.</td>
</tr>
<tr>
<td>2</td>
<td>Values of dataset selection criteria.</td>
</tr>
<tr>
<td>3</td>
<td>blank</td>
</tr>
<tr>
<td>4–</td>
<td>
Values shown in table.
Layout and content is the same as the table.
</td>
</tr>
<tr>
<td>4</td>
<td>
Headings for data columns.
</td>
</tr>
<tr>
<td>5–</td>
<td>
Data values.
</td>
</tr>
</tbody>
</Table>
science:
title: "# Scientific References"
content: |
## Statistically Downscaled Climate Scenarios
**Bürger, G.**,** T.Q. Murdock**, **A.T. Werner**, **S.R. Sobie**, and **A.J. Cannon**, 2012:
[Downscaling extremes - an intercomparison of multiple statistical methods for present climate](http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-11-00408.1).
_Journal of Climate_, **25**, 4366–4388. doi:10.1175/JCLI-D-11-00408.1.
**Bürger, G.**, **S.R. Sobie**, **A.J. Cannon**, **A.T. Werner**, and **T.Q. Murdock**, 2013:
[Downscaling extremes - an intercomparison of multiple methods for future climate](http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-12-00249.1).
_Journal of Climate_, **26**, 3429-3449. doi:10.1175/JCLI-D-12-00249.1.
**Cannon, A.J.**, 2015:
Selecting GCM scenarios that span the range of changes in a multimodel ensemble: application to CMIP5 climate extremes indices.
_ Journal of Climate_, **28**(3): 1260-1267. doi:10.1175/JCLI-D-14-00636.1
**Cannon, A.J.**,** S.R. Sobie**, and **T.Q. Murdock**, 2015:
[Precipitation by Quantile Mapping: How Well Do Methods Preserve Changes in Quantiles and Extremes?](https://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-14-00754.1)
_Journal of Climate_, **28**(17), 6938-6959, doi:10.1175/JCLI-D-14-00754.1.
Eyring, V., S. Bony, G.A. Meehl, C. Senior, B. Stevens, R.J. Stouffer and K.E. Taylor, 2016:
[Overview of the Coupled Model Intercomparison Project Phase 6 (CMIP6) experimental design and organization](https://gmd.copernicus.org/articles/9/1937/2016/).
Geoscientific Model Development, 9(5), 1937-1958.
Giorgi, F. and Francisco, R., 2000:
Evaluating uncertainties in the prediction of regional climate change.
_Geophysical Research Letters_, **27**(9), 1295-1298.
Gudmundsson, L., J. B. Bremnes, J. E. Haugen, and T. Engen-Skaugen, 2012:
[Technical Note: Downscaling RCM precipitation to the station scale using statistical transformations - a comparison of methods](http://www.hydrol-earth-syst-sci.net/16/3383/2012/hess-16-3383-2012.html).
_Hydrology and Earth System Sciences_, **16**, 3383–3390, doi:10.5194/hess-16-3383-2012.
**Hiebert, J.**, **A. Cannon**, **A. Schoeneberg**,** S. Sobie**, and **T. Murdock**, 2018:
[ClimDown: Climate Downscaling in R](http://joss.theoj.org/papers/87c76a9d8f39f0dafbda6388b82f5cf0).
_The Journal of Open Source Software_, **3**(22), 360, doi:10.21105/joss.00360.
Hopkinson, R.F., D.W. McKenney, E.J. Milewska, M.F. Hutchinson, P. Papadopol, and L.A. Vincent, 2011:
[Impact of Aligning Climatological Day on Gridding Daily Maximum–Minimum Temperature and Precipitation over Canada](http://journals.ametsoc.org/doi/abs/10.1175/2011JAMC2684.1)._ Journal of Applied Meteorology and Climatology_, **50**, 1654–1665. doi:10.1175/2011JAMC2684.1.
Hunter, R. D., and R. K. Meentemeyer, 2005:
[Climatologically Aided Mapping of Daily Precipitation and Temperature](http://journals.ametsoc.org/doi/abs/10.1175/JAM2295.1).
_Journal of Applied Meteorology_, **44**, 1501–1510, doi:10.1175/JAM2295.1.
Eum, H.-I., Dibike, Y., Prowse, T. and Bonsal, B., 2014, Inter-comparison of high-resolution gridded climate data sets and their implication on hydrological model simulation over the Athabasca Watershed,
_Canada. Hydrol. Process._, **28**, 4250–4271. doi: 10.1002/hyp.10236.
Li, C., Y. Fang, K. Calderia, X. Zhang, N.S. Diffenbaugh, and A.M. Michalak, 2018:
[Widespread persistent changes to temperature extremes occurred earlier than predicted](https://www.nature.com/articles/s41598-018-19288-z).
_Nature Scientific Reports_, **8**, 1007, doi:10.1038/s41598-018-19288-z
Maurer, E.P., and H.G. Hidalgo, 2008:
[Utility of daily vs. monthly large-scale climate data: an intercomparison of two statistical downscaling methods](http://www.hydrol-earth-syst-sci.net/12/551/2008/hess-12-551-2008.html).
_Hydrology and Earth System Sciences_, **12**, 2, 551-563. doi:10.5194/hess-12-551-2008.
Maurer, E., H. Hidalgo, T. Das, M. Dettinger, and D. Cayan, 2010:
[The utility of daily large-scale climate data in the assessment of climate change impacts on daily streamflow in California](http://www.hydrol-earth-syst-sci.net/14/1125/2010/hess-14-1125-2010.html).
_Hydrology and Earth System Sciences_, **14**, 6, 1125–1138, doi:10.5194/hess-14-1125-2010.
McKenney, D.W., M.F. Hutchinson, P. Papadopol, K. Lawrence, J. Pedlar, K. Campbell, E. Milewska, R. Hopkinson, D. Price, and T. Owen, 2011:
[Customized spatial climate models for North America](http://journals.ametsoc.org/doi/abs/10.1175/2011BAMS3132.1).
_Bulletin of the American Meteorological Society_, **92**, 12, 1611-1622. doi:10.1175/2011BAMS3132.1.
Meinshausen, M., et al., 2011: The RCP greenhouse gas concentrations and their extensions from 1765 to 2300.
_Climatic Change_, **109**(1-2), 213-241.
Riahi, K. et al. 2017:
[The Shared Socioeconomic Pathways and their energy, land use, and greenhouse gas emissions implications: An overview](https://www.sciencedirect.com/science/article/pii/S0959378016300681).
Global Environmental Change, 42, 153-168, doi:10.1016/j.gloenvcha.2016.05.009.
**Sobie, S.R.**, and **T.Q. Murdock**, 2017:
[High-Resolution Statistical Downscaling in Southwestern British Columbia](https://journals.ametsoc.org/doi/abs/10.1175/JAMC-D-16-0287.1).
_Journal of Applied Meteorology and Climatology_, **56**(6), 1625–1641, doi:10.1175/JAMC-D-16-0287.1.
Taylor, K.E., R.J. Stouffer, and G.A. Meehl, 2012:
[ An Overview of CMIP5 and the Experiment Design](http://journals.ametsoc.org/doi/abs/10.1175/BAMS-D-11-00094.1).
_Bulletin of the American Meteorological Society_, **93**, 485–498. doi: 10.1175/BAMS-D-11-00094.1.
van Vuuren, D.P., et al., 2011:
[The Representative Concentration Pathways: An Overview](https://link.springer.com/article/10.1007/s10584-011-0148-z).
Climatic Change, 109 (1-2), 5-31, doi:10.1007/s10584-011-0148-z.
**Werner, A.T.**, 2011:
[_BCSD downscaled transient climate projections for eight select GCMs over British Columbia, Canada_](http://www.pacificclimate.org/sites/default/files/publications/Werner.HydroModelling.FinalReport1.Apr2011.pdf).
Pacific Climate Impacts Consortium, University of Victoria, Victoria, BC, 63 pp.
**Werner, A.T.** and **A.J. Cannon**, 2015: Hydrologic extremes – an intercomparison of multiple gridded statistical downscaling methods.
_Hydrology and Earth System Sciences Discussion_, **12**, 6179-6239, doi:10.5194/hessd-12-6179-2015.
Wood, A.W., L.R Leung, V. Sridhar, and D.P. Lettenmaier, 2004:
[Hydrologic implications of dynamical and statistical approaches to downscaling climate model outputs](http://link.springer.com/article/10.1023%2FB%3ACLIM.0000013685.99609.9e).
_Climatic Change_, **62**, 189–216, doi:10.1023/B:CLIM.0000013685.99609.9e.
about:
pcex:
title: "# PCIC Climate Explorer (PCEX)"
items:
- header: Description
body: |
A tool for visualizing and downloading climate model outputs and data derived from those outputs.
The PCIC Climate Explorer (PCEX) is also fondly known as 'the marmot,' since it is an improvement
(we sincerely hope) on its predecessor, the Regional Analysis Tool, or 'RAT.' Hence our mascot in the header.
- header: Version
body: ${version}
- header: Author
body: "[Pacific Climate Impacts Consortium (PCIC)](https://pacificclimate.org/)"
- header: Terms of Use
body: |
In addition to PCIC's [terms of use](https://pacificclimate.org/terms-of-use), the data for each individual
data set is subject to the terms of use of each source organization.
For further details please refer to:
* [The Coupled Model Intercomparison Project](https://pacificclimate.org/sites/default/files/tou-cmip5-pcmdi_llnl_gov_february-19th-2014.pdf)
* [National Center for Atmospheric Research Earth System Grid](https://pacificclimate.org/sites/default/files/tou_earthsystemgrid_february-19th-2014.pdf)
#### No Warranty
The data in this tool are provided by the Pacific Climate Impacts Consortium with an open licence on an
“AS IS” basis without any warranty or representation, express or implied, as to its accuracy or completeness.
Any reliance you place upon the information contained here is your sole responsibility and strictly at
your own risk. In no event will the Pacific Climate Impacts Consortium be liable for any loss or damage
whatsoever, including, without limitation, indirect or consequential loss or damage, arising from reliance
upon the data or derived information.
credits:
title: "# Credits and Acknowledgements"
sponsors:
title: "## Sponsors"
items:
- header: Ministry of Transportation and Infrastructure (MoTI)
href: https://www2.gov.bc.ca/gov/content/governments/organizational-structure/ministries-organizations/ministries/transportation-and-infrastructure
body: Primary sponsor of the PCIC Climate Explorer (PCEX) project.
others:
title: "## Others"
items:
- header: Vancouver Island Marmot Recovery Foundation
href: https://marmots.org/
body: Use of MRF marmot graphic by kind permission.
data:
title: "## Data"
items:
- header: Environment and Climate Change Canada
href: http://www.ec.gc.ca/
body: |
We thank the Landscape Analysis and Applications section of the
Canadian Forest Service, Natural Resources Canada, for developing
and making available the Canada-wide historical daily gridded
climate dataset used as the downscaling target.
PCIC gratefully acknowledges support from
Environment and Climate Change Canada
for the development of the statistically downscaled GCM
scenarios on which much of the data presented here is based.
- header: World Climate Research Programme
href: https://www.wcrp-climate.org/
body: |
We acknowledge the World Climate Research Programme’s
Working Group on Coupled Modelling, which is responsible for
CMIP5, and we thank the climate modeling groups for producing
and making available their GCM output.
- header: U.S. Department of Energy
href: https://www.energy.gov/
body: |
For CMIP, the U.S. Department of Energy’s Program for
Climate Model Diagnosis and Intercomparison provides coordinating
support and led development of software infrastructure in
partnership with the Global Organization for
Earth System Science Portals.
contact:
title: "# Contact"
items:
- header: Feedback on Application
body: |
Please address questions and suggestions on the functioning of this
tool (the application proper) to ${$$.components.contacts.csg.link}.
- header: Scientific Questions
body: |
Please address questions about science and interpretation of the
data presented in this tool to ${$$.components.contacts.science.link}.
- header: Pacific Climate Impacts Consortium
body: See [PCIC Contact page](https://pacificclimate.org/contact-us).
team:
title: "# Team"
items:
- header: James Hiebert
href: https://pacificclimate.org/about-pcic/people/james-hiebert
body: |
Fearless leader.
Ur-Architect of PCIC information systems.
Keeper of the clan's lore and history.
- header: Lee Zeman
href: https://pacificclimate.org/about-pcic/people/lee-zeman
body: |
Front-end engineer.
Valiant contender with GIS legacy backend rebellions.
Implementor of wondrous data graphs.
Champion of the practical and effective.
- header: Rod Glover
href: https://pacificclimate.org/about-pcic/people/rod-glover
body: |
Full-stack engineer.
Implementor of fearsome data preparation tools.
Wrangler of metadata.
React refactorer and perfectionist.
Migrator of databases and devotee of the alchemical arts.
- header: Matthew Benstead
href: https://pacificclimate.org/about-pcic/people/matthew-benstead
body: |
System administrator and master of all things IT.
Docker guru.
Restorer of expired servers and ailing disk arrays.