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Technical Document
Overview
The maps presented here are built upon raster data that are developed from climate projection outputs
through the CLIMSystem’s SimCLIM 2013 climate change analysis software. The software produces
regional climate change patterns (percent change in magnitude) by processing the raw general
circulation model (GCM) outputs from the latest Intergovernmental Panel on Climate Change’s (IPCC)
Fifth Assessment (the Coupled Model of Intercomparison Project 5) with the pattern scaling method.
Following the IPCC CMIP5 datasets, the baseline used as the reference period for comparison with
future climate change ranges from 1986 to 2005 (centered on 1995). The baseline data and the GCM
future climate outputs are first processed with the pattern scaling method, and then regridded to a
common 720 X 360 resolution global grid (i.e., 0.5 degree latitude X 0.5 degree longitude) using a
bilinear interpolation method.
The following three sets of maps are provided for the State of Massachusetts:
1) Projected percent change in future 24-hour 100-year return interval (i.e., 1% annual
exceedance probability) precipitation;
2) Projected future 24-hour 100-year return interval precipitation depth;
3) Projected annual maximum number of consecutive days > 95°F
Each map represents a combination of:
a. One of three different global warming scenarios developed for IPCC’s fifth assessment (i.e.,
Representative Concentration Pathway 4.5, 6.0 and 8.5);
b. One of four future time periods (2030, 2050, 2070 and 2100); and,
c. One of the CMIP5 model projections (10th, 50th, and 90th percentile GCMs).
Precipitation
Projected percent change in 24-hour 100-year return interval precipitation (in comparison to the
baseline period of 1986-2005) are developed from selected GCMs’ outputs at 0.5 degree latitude x 0.5
degree longitude resolution over the Unite State Geological Survey (USGS) 8-digit hydrologic unit
categories (HUCs). Massachusetts is hydrologically divided into 27 major watersheds at the 8-digit HUC
level crossing the state boundary, and also over two large watersheds across several states in New
England (the Connecticut and the Merrimack River Watersheds, they are at USGS 4-digit HUC level).
To reflect the wide range of different model outputs, projections are developed for GCMs falling closest
to the idealized 10th, 50th, and 90th percentile scalars from 22 GCMs in the SimCLIM 2013 software.
Only these 22 GCMs with output that can be used to develop precipitation scalars for the 100-yr return
interval daily event are used for precipitation analysis.
The SimCLIM software develops precipitation scalars based on daily GCM output. The scalars are
pattern-scaled and tied to the global mean temperature, with cell output expressed as the ratio of
change per degree Celsius change in global mean temperature. The scalars are specific to each GCM
and return interval event being examined – in this case, the 100-yr return interval daily precipitation.
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The GCM projection ensemble percentiles are selected from the average value of scalars over the
analysis area (i.e., Massachusetts and the Connecticut and Merrimack Watersheds) for each of 22 GCMs.
The formula used for determining the X percentile ensemble member is: Xth % = (X - 1) * X% +1. For
example, when 22 GCMs are used for the ensemble of precipitation projections, the 10th percentile
model is defined by (22-1)* 10% +1 = 3.1. For simplicity, the 3rd place GCM value is chosen in this case
as the low percentile climate change value.
In the precipitation part of the study, the GCMs selected for the 10th, 50th and 90th percentiles are:
10th% = CCSM45
50th% = IPSL_CM5A_MR
90th% = MIROC5
The percent change in magnitude for the 10th, 50th and 90th percentile GCMs is subsequently derived by
multiplying the cell-specific scalar by the global mean warming under a specific emission scenario (RCP
in this case) and time period. The percent change in magnitude is then applied to higher resolution
spatially-explicit observed data (recorded precipitation depth in recent years) to capture the variability
within a GCM cell (0.5 degree x 0.5 degree) and to project future precipitation depth for the 100-year
return interval event. In this case, National Oceanic and Atmospheric Administration (NOAA) Atlas 14
data are used. Because of the higher resolution of the NOAA Atlas data, this application step results in a
final raster resolution of about 0.5 mile (0.8 km) as depicted on the projected precipitation depth maps.
For example, if a climate station shows that the historic 24-hour 100-year return interval precipitation
event is 100 mm and the raster cell (projected percent change) has a value of 30% (increase of 30%),
and then the projected future precipitation depth is:
100 x 1.3 = 130 mm
Please note that moderate changes in future precipitation (both percent change and absolute depth
change) may not demonstrate themselves on the maps in different outer years or scenarios. As the
precipitation map legend shows, the legend classification entails a range of values in each class.
Sometimes it may appear that the class (color) doesn’t change in different outer years or scenarios on
the maps, nevertheless, it doesn’t necessary mean the values don’t change. On the contrary, they could
be changing but just within the bounds of the data in that class. On the other side, a very small change
(e.g. 0.1 inch) can result in a change in class and thus color on a map if the value is on the high end of
the class range to begin with.
High Temperature
Similar to precipitation projections, temperature projections are developed for GCMs falling closest to
the idealized 10th, 50th, and 90th percentile scalars from the 40 GCMs that are available in SimCLIM
software for monthly temperature change analysis. The percentiles are selected from the average
annual value of change in temperature by 2100 (in comparison to the baseline) under the highest
emission scenario (RCP 8.5) over the analysis area (and Massachusetts state boundaries and the
Connecticut and Merrimack watersheds) for each model.
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The formula used for choosing the temperature projection GCM percentiles is the same as the ones
used for precipitation projection. For example, since 44 GCMs are used for the ensemble of
temperature projections, the 10th percentile model is determined by (44-1)* 10% +1 = 5.3. For
simplicity, the 5th place GCM value is chosen as the desired low percentile temperature change value.
For temperature projection, the GCMs selected for the three percentiles are:
10th% = EC_EARTH
50th% = GFDL_CM3
90th% = MIROC5
Parallel to the precipitation scenarios, projections of future monthly temperature change are developed
from the three models listed above and provided for four time periods (2030, 2050, 2070, 2100) and
under three emission scenarios (RCP 4.5, 6.0, and 8.5) at 0.5 degree x 0.5 degree resolution.
Monthly temperature change for each time period and climate change scenario are then applied to
historical daily climate station data for the study area by respective month over the entire period of
record at that station. This study utilizes historical climate station data from NOAA's Global Historical
Climate Network (GHCN), as provided by SimCLIM software. After this application, the final resolution of
the high temperature maps is approximately 0.3 mile (0.5 kilometer).
For example, if the station data cover the period 1900 - 1985, then each July day in the historical record
(July 1-31, 1900, July 1-31, 1901, ...) is applied to the July change in temperature for the respective
future time period and climate change scenario (e.g., the July Tmax change for 2030 under RCP 4.5 for
the 90th percentile model, etc.). The average number of days > 95°F for July is then calculated from the
full series of July data.
Similarly, for the maximum number of consecutive days > 95°F per year, the monthly temperature
change is first applied to the full series of historic daily temperature data, and then the number of
consecutive days above 95°F for each year is calculated. The number from the highest value year from
the whole set is chosen as the projected value for the particular scenario, with the assumption of no
inter-annual variance in the observations.
To ensure data quality, the historical climate station data from the Global Historical Climate Network is
filtered to include only stations that met the following criteria:
1) Stations must have at least 80% (292 days) of the days for each year;
2) Stations must have at least 80% (24 days) per month;
3) Stations must have at least 20 years of data meeting above criteria
4) Stations limited to data from year 1900 and beyond.
Additional criteria are also used to ensure adequate coverage of stations across the Commonwealth of
Massachusetts for interpolation purposes.
References:
CLIMSystems, SimCLIM 2013 Data Manual. November 2013.
LIMSystems, SimCLIM 2013 FAQ (Version 2.1). July 2014.
Warrick, R.; Ye, W.; Li, Y.; Dooley, M.; Urich, P, 2009: SimCLIM: A Software System for Modelling the Impacts of
Climate Variability and Change. CLIMsystems Ltd. Hamilton, New Zealand.
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