North American Regional Climate Change Assessment Program
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Caveats for Users
 

Regarding Uncertainty

As described in the section on Quantification of Uncertainty, it is important to note that only a limited part of the total uncertainty associated with future climate change is covered by the NARCCAP simulations. Only one emissions scenario and four AOGCMs are used. A total of 23 different AOGCMs participated in the Fourth Assessment Report of the IPCC and are available at the PCMDI web site. However, based on earlier work (e.g., Tebaldi et al., 2005) the climate changes simulated by the four AOGCMs can be placed in the context of probabilities of future climate change generated using the full suite of climate models. This will allow NARCCAP results to be placed in the context of the large number of AOGCM results. The uncertainty related to spatial scale is relatively well-covered since six different regional models are used. As always, however, uncertainties that cannot be readily quantified through use of even the full suite of global climate models still must be recognized. These include such uncertainties as processes that are missing from the climate models (e.g., a fully coupled carbon cycle) processes that are not understood well enough to model successfully (e.g., certain aspects of ice sheet dynamics), and processes that we are still unaware of.


Regarding the Data

The data from the climate model simulations are provided as-is. We have implemented quality control procedures to correct any errors occurring in the creation of the data sets, but these procedures are not perfect. In addition, data corruption can occur during storage and download. When errors are found in published data, we correct them and attempt to notify those users who have downloaded the affected files.

Note also that the NCEP-driven simulations and current climate simulations using boundary conditions from the AOGCMs do contain biases compared to observed data for the same time periods. Climate model simulations are not perfect. We will, however, provide some insights into the credibility of the future climate simulations through detailed analysis of the model results, including an assessment of the quality of the current model simulations and their representation of processes which are responsible for the projected climate changes.


Regarding Time

Published NARCCAP data includes data from the initial equilibration or 'spin-up' period of the run. For general use, we strongly recommend that spin-up data be excluded for analysis, and that only data from 1980-2004 (NCEP), 1971-2000 (GCM current), or 2041-2070 (future) be used. For more details, see the Time Periods Covered page.


Regarding WRF Simulations nested in the CCSM: Treatment of the Great Lakes and other smaller lakes resolved by WRF at 50 km grid resolution.

  1. For the current climate simulations, lake temperatures were assigned in the standard fashion by spatially interpolating the CCSM sea surface temperatures (SSTs) from coast to coast (Atlantic/Pacific) to the locations of the lakes. This is a fairly standard practice used by many of the regional models. Lake ice forms when the assigned lake temperature is below 271.4K (-1.6° C) or when sea ice is identified by the sea ice mask from CCSM.
  2. For the future simulation, lake temperatures were inadvertently assigned based on the CCSM skin (i.e. surface) temperature at the locations of the lakes. Lake ice forms when the assigned lake temperature is below 271.4K (-1.6° C).
  3. However, the surface package of the CCSM (the Community Land Model, CLM) includes a lake thermodynamics model to simulate subgrid lakes. For larger lakes such as Lake Superior, the CCSM grid cell mean surface temperatures are dominated by the surface temperatures simulated for the lake surface type. Therefore, the lake temperature assigned based on the CCSM skin temperature is reflective of the lake surface type and close to the temperature of the lakes. However, this is less true for the smaller lakes (Ontario and Erie) for which the subgrid percent lake is lower than 50%; for Ontario in particular it is only between 20 and 30%.
  4. Since lake temperatures in the future simulation were not correctly assigned, we recommend that near surface variables such as surface temperature and surface heat fluxes, over the lake points for the two smaller lakes of the Great Lakes and other small lakes such as those in the Canadian prairie, in the future simulation not be used in analysis of the model outputs.

Rerun of the RegCM3 using GFDL driving data: Improvement in the treatment of the Great Lakes (but sea ice problem remains)

  1. For the original GFDL-driven current and future simulations, initial lake temperatures were assigned using downward-extrapolated air temperature for the GFDL land points corresponding to the lake point locations in the RegCM3. After the initial time step, the BATS land surface model in RegCM3 calculates the temperatures at lake grid points. Grid points designated as lakes use the BATS parameterization for inland water (land use category 14). This land type does not correspond well to the characteristics of a lake, and so the lake temperatures, particularly in summer, do not correspond well to what one would expect for lake temperatures compared to surrounding land. For example, in summer the lake temperatures should be cooler than that of surrounding land. This is not the case in the original RegCM3 simulations.

    To correct for this situation, the RegCM3 model was rerun using the GFDL model boundary conditions, but using a different means of representing the lakes. As is done in a number of other regional climate models, the lake temperatures were determined by interpolating for the same latitudes the sea surface temperatures of the adjacent oceans to the locations of the Great Lakes grids (this is a weighted interpolation using both Atlantic and Pacific Ocean SSTs). The lake surface temperatures in the simulations are now more realistic. The adjacent figures show the surface temperatures in the Great Lakes regions for winter and summer from the simulations of the current and future periods for a) the old runs and b) the new set of runs. Another limitation is that lake ice does not form in either run. So both sets of simulations have limitations in how accurately they portray the winter and early spring seasons over the lakes themselves. For further reference, the RegCM3 source code and documentation are available here: http://users.ictp.it/RegCNET/model.html.

    CurrentFuture
    a) Original GFDL run
    DJF
    JJA
    b) GFDL rerun
    DJF
    JJA


  2. In the GFDL future and historical simulations, sea ice on ocean points (BATS land use category 15) does not form, due to problems with RegCM3's sea ice code. The temperatures of those grid points are set to the temperatures from the GCM SSTs.
  3. Note that there are no problems in the original simulations for all other parts of the NARCCAP domain and they can be used. There is little difference in the two runs for these other parts of North America.

Caveat regarding RegCM3 simulations using CGCM3 driving data: Treatment of the Great Lakes.

  1. For the CGCM3 future and historical simulations, initial lake temperatures were assigned using downward-extrapolated air temperature. After the initial time step, the BATS land surface model calculates the temperatures at lake grid points. Grid points designated as lakes use the BATS parameterization for inland water (land use category 14). See the description of this land use category below.
  2. CGCM3 surface temperature data includes two (?) lake points, from the lake model within CGCM3, and these temperatures are incorporated into RegCM3.
  3. Users should carefully evaluate the model output for the Great Lakes region to determine if it is suitable for their uses. Surface variables should be considered suspect.
  4. In the CGCM future and historical simulations, sea ice on ocean points (BATS land use category 15) does not form, due to problems with RegCM3's sea ice code. The temperatures of those grid points are set to the temperatures from the GCM SSTs.


About BATS land use category 14 (inland water)

In the BATS land surface model, used by RegCM3, lake grid cells are defined as land use category 14. This land use type is treated in the same way as any other land use type by the land surface model (i.e. fluxes of heat and moisture are calculated, along with other variables). The response of land use category 14 is governed by the parameters in the tables below. The land surface model also keeps category 14 saturated at all time steps, by setting negative values for runoff. This is the best approximation for a water covered grid cell given BATS limitations. For further reference, the RegCM3 source code and documentation are available here.

BATS land use category 14 (inland water) parameters. Obtained from BLOCKDATA001.F in RegCM3 source code.

Parameter name and description Parameter value
vegc - maximum fractional cover of vegetation:0
seasf - difference between vegc and fractional cover at 269K:0
rough - aerodynamic roughness length (m):0
displa - displacement height (m):0
albvgs - vegetation albedo for wavelength lt 0.7 microns:0.07
albvgl - vegetation albedo for wavelengths ge 0.7 microns:0.20
rsmin - min stomatal resistance (s/m):200
xla - max leaf area index:0
xlai0 - min leaf area index:0
sai - stem area index:2
sqrtdi - inverse square root of leaf dimension:5.0
fc - light dependence of stomatal resistance:.02
depuv - depth of upper soil layer (mm):100.
deprv - depth of root zone (mm):1000.
deptv - depth of total soil (mm):3000.
iexsol - soil texture type:6
kolsol - soil color type:5

Parameters specific to soil texture type 6, from BLOCKDATA001.F in RegCM3 source code (note: in the case of BATS land use category 14, only the saturated albedos are applicable since these grid cells are saturated at all times).

Parameter name and description Parameter value
xmopor - fraction of soil that is voids (porosity):0.48
xmosuc - min soil suction (mm):200
xmohyd - saturated hydraulic conductivity (mm s-1):6.3 x 10-3
xmowil - moisture content relative to saturation at which transpirations ceases:.332
xmofc -fraction of water content at which wilting occurs:.688
bee - clapp and hornberger (1978) "B" exponent:6.0
skrat - ratio of saturated thermal conductivity to that of loam:1.0
dry soil albedo for wavelengths lt 0.7 microns:0.16
Dry soil albedo for wavelengths ge 0.7 microns:0.32
saturated soil albedo for wavelengths lt 0.7 microns:0.08
saturated soil albedo for wavelengths ge 0.7 microns: 0.16

Caveat regarding HRM3 Treatment of Great Lakes Temperatures in the GFDL-Driven Runs.

There was no GFDL SST or sea ice data available for the Great Lakes. For the current-period experiment, which was simulated first, monthly climatology values were used. However, when it came to starting the future period (scenario) experiment, climatological values were not suitable.

For the scenario run, the modelers used the SST ancillaries from the HadCM3Q0 scenario simulation to calculate the monthly climatology difference in SSTs between each grid box in the great lakes and a nearby ocean grid box from James Bay. This difference was then applied to the GFDL data to create SSTs over the great lakes. The sea ice fraction for the Great Lakes was set to zero.

The lake temperatures for the two runs thus come from two independent sources, and this inconsistency allows the future period summer lake temperatures to be cooler than the current period summer lake temperatures.


References

Tebaldi, C., R. Smith, D. Nychka, and L. O. Mearns, 2005: Quantifying uncertainty in projections of regional climate change: A Bayesian approach to the analysis of multi-model ensembles. J. Climate 18:1524-1540.

 
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