CCCMA


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AcronymDefinition
CCCMACanadian Centre for Climate Modelling and Analysis
CCCMAColorado City County Management Association (Golden, CO)
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Figure 8 shows the ratio of 3 x C[O.sub.2] to 1 x C[O.sub.2] mean seasonal severity rating as predicted by the CCCma and Hadley models.
Figure 9 shows changes in fire season length from 1 x C[O.sub.2] to 3 x C[O.sub.2] over our region of interest, as predicted by the CCCma and Hadley GCMs.
Institution (country) Model Retrospective Forecast period months Meteorological Research JMA/MRI-CGCMI 1979-2010 7 Institute (MRI)-JMA (Japan) JMA/MRI-CGCM2 1981-2011 7 Met Office L38GloSea4 1989-2003 5 (United Kingdom) L85GloSea4 1989-2010 5 GloSea5 (a) 1996-2009 3 CCCma (Canada) CMAM 1979-2009 4 CMAMIo 1979-2009 4 CCCma-CanCM3 1979-2010 12 CCCma-CanCM4 1979-2010 12 NOA A (United States) CFS 1981-2007 9 MeteoFrance (France) ARPEGE 1979-2008 4 CAWCR (Australia) POAMA 1980-2009 9 CCSR-University of MIROC5 1979-2011 12 Tokyo (Japan) ECMWF (international) ECMWF-S4 1981-2010 7 MPI (Germany) MPI-ESM-LR 1982-2012 12 MPI-ESM-MR 1981-2012 7 Institution (country) Model No.
Model Resolution Atmosphere Ocean CCCMA CGM 3.1 (T47) T47 (3.75 x 3.75), L31 1.85 x 1.85, L29 CNRM CM3 T63 (2.8 x 2.8), L45 1.875 x (0.5-2), L31 CSIRO MK 3.0 T63, L18 1.875 x 0.84, L31 GFDL CM 2.1 2.5 x 2.0, L24 1.0 x (1/3-1), L50 GISS MODEL E_R 5 x 4, L20 5 x 4, L13 IPSL CM 3.0 2.5 x 3.75, L19 2 x (1-2), L30 MIROC 3.2 MEDRES T42, L20 1.4 x (0.5-1.4), L43 MPI ECHAM5 T63, L32 1 x 1, L41 NCAR CCSM 3.0 T85L261.4 x 1.4 1 x (0.27-1), L40 UKMO HADCM3 3.75 x 2.5, L19 1.25 x 1.25, L20 Model References CCCMA CGM 3.1 (T47) [40] CNRM CM3 [41] CSIRO MK 3.0 [42] GFDL CM 2.1 [43] GISS MODEL E_R [44] IPSL CM 3.0 [45] MIROC 3.2 MEDRES [46] MPI ECHAM5 [47] NCAR CCSM 3.0 [48] UKMO HADCM3 [49]
Regional model capability has been developed for the CCCma model (Caya et al., 1995; Laprise et al., 1998; Caya and Laprise, 1999).
(2009) assessed the biogeochemical behavior of the CCCma earth system model (CanESM1) against observations for 1850-2000 and then compared simulated atmospheric C[O.sub.2] concentration with available observations and observation-based estimates; the simulation results showed that forests' different photosynthesis ability and C[O.sub.2] emissions from LUC resulted in different density of C[O.sub.2] in atmosphere, which indicated that the tropics were large carbon sinks [45].
Christian et al., "The Effect of terrestrial photosynthesis down regulation on the twentieth-century carbon budget simulated with the CCCma earth system model," Journal of Climate, vol.
Climate models used in projections of future temperature Climate model Originating group cccma.cgcm3.1 Canadian Centre for Climate Modeling and Analysis cnrm.cm3 Meteo-France/Centre National de Recherches Meteorologiques csiro.mk3.0 CSIRO Atmospheric Research (Australia) gfdl.cmZO Geophysical Fluid Dynamics Laboratory/NOAA (USA) miroc3.2.medres Center for Climate System Research/JAMSTEC (Japan) mpi.echam5 Max Planck Institute for Meteorology (Germany) mri.cgcm2.3.2a Meteorological Research Institute (Japan) The SRES consist of divergent storylines that describe the demographic, economic, and technological changes in the future world.
SRES scenario 1981-2000 (a) 2081-2100 Climate model B1 A1B A2 B1 A1B A2 cccma.cgcm3.1 0.30 1.20 0.30 0.65 1.40 1.05 cnrm.cm3 0.30 3.00 0.30 1.80 3.30 4.00 csiro.mk3.0 0.20 1.00 0.20 0.60 1.45 1.05 gfdl.cm2.0 0.45 1.30 0.45 1.15 2.10 1.70 miroc3.2.medres 0.15 1.00 0.15 3.20 5.40 4.75 mpi.echam5 0.40 1.10 0.40 2.65 5.20 3.95 mri.cgcm2.3.2a 0.20 1.00 0.20 1.70 2.55 2.95 The SRES A1B family or scenarios assumes rapid economic growth, an increase in world population until mid-century followed by a decrease, and the introduction of more efficient energy sources and conversion technologies where the mix of energy sources is balanced across fossil fuel and alternative sources.
These results are in broad agreement with indirect estimates of the predictability of the relative contributions of internally generated and forced components of temperature as characterized by variance ratios in CMIP3 experiments (Boer 2011) and in measures of predictability and prediction skill for hindcasts made with the CCCma model (Boer et al.