Monthly climate data

Monthly climate data used in Chapter 3 comprise historical climate data from the CRU TS 2.10 (Mitchell and Jones, 2005) dataset and 72 global climate change patterns derived from eighteen global climate models (GCMs) combined with four RCPs generated using CIAS (Warren et al., 2008): a modular integrated assessment model (IAM) linking an emission scenarios module (ESM), a simple global climate module (SCM), MAGICC 6 (Meinshausen et al., 2011), and a climate scenario downscaling module (DSM), ClimGEN (Osborn, 2009). Designed for modelling climate change policy and effectiveness, CIAS is a unique multi-institutional modular and flexible integrated assessment system offering a single framework to create multiple IAMs by interchanging the coupling of the different modules (Warren et al., 2008). CIAS is supported by a software framework called SoftIAM, which allows various combinations of modules to be connected together into alternative IAMs and provides a graphical interface to let users interact with the system, as well as configure and perform various kinds of simulations to answer different scientific and policy questions. In Chapter 3, CIAS modules are configured to emulate the behaviour of eighteen GCMs used in the Fourth Assessment Report of the IPCC (IPCC AR4) (Solomon et al., 2007) coupled to four RCPs used in the Fifth Assessment Report of the IPCC (IPCC AR5) (Stocker et al., 2013; van Vuuren et al., 2011) (the eighteen GCMs are listed in Table 2.3).

The ESM provides atmospheric concentration data of greenhouse gas (GHG) emissions for various scenarios database such as the IPCC SRES (Nakicenovic et al., 2000) and RCPs, the latter being used in Chapter 3. Alternatively, GHG concentrations can be estimated from emission scenarios generated from an economic module linked to an emission converter as presented in (Warren et al., 2008). GHG concentration data are then input to MAGICC 6.

The MAGICC model (Wigley, 2001) has been developed and updated over two decades and widely used in integrated modelling studies (Rotmans et al., 1994; van Vuuren

Table 2.3: Model identification and originating group from the CMIP3 archive. IPCC ID Centre and location

CGCM3.1(T47) Canadian Centre for Climate Modelling and Analysis (Canada) CSIRO-Mk3.0 CSIRO Atmospheric Research (Australia)

CNRM-CM3 M´et´eo-France, Centre National de Recherches M´et´eorologiques (France) GFDL-CM2.0 US Dept. of Commerce, NOAA

GFDL-CM2.1 Geophysical Fluid Dynamics Laboratory (United States) GISS-EH

NASA/Goddard Institute for Space Studies (United States) GISS-ER

FGOALS-g1.0 LASG/Institute of Atmospheric Physics (China) INM-CM3.0 Institute for Numerical Mathematics (Russia) IPSL-CM4 Institut Pierre Simon Laplace (France)

MIROC3.2(medres) Center for Climate System Research (The University of Tokyo), MIROC3.2(hires) National Institute for Environmental Studies, and

Frontier Research Center for Global Change (JAMSTEC) (Japan) MRI-CGCM2.3.2a Meteorological Research Institute (Japan)

ECHAM5/MPI-OM Max Planck Institute for Meteorology (Germany) NCAR-CCSM3.0

National Center for Atmospheric Research (United States) NCAR-PCM1

UKMO-HadCM3

Hadley Centre for Climate Prediction and Research, Met Office (UK) UKMO-HadGEM1

et al., 2008). MAGICC is a single piece of software comprising a set of linked internal components to simulate GHGs cycles, radiative forcing, and ice melt. Radiative forcing drives an upwelling diffusion energy balance model to estimate future climate changes. MAGICC 6 (Meinshausen et al., 2011) is an updated version of the original MAGICC, with an improved representation of the carbon cycle. Climate feedback on the carbon cycle is included; the resulting [CO2] depends on the forcing, the climate sensitivity and the ocean heat uptake efficiency. Sulphate aerosol forcing is scaled directly with the emissions because of the short residence time in the atmosphere. Thus the model allows the user to emulate GCM output, specifically changes in [CO2], global-mean surface air temperature and sea level between the years 2000 and 2100 resulting from anthropogenic emissions of CO2, methane, N2O, chlorofluorocarbons, hydrofluorocarbons, perfluorocarbons, as well as sulfur dioxide. In Chapter 3, MAGICC 6 is tuned to emulate eighteen state-of-the-art GCMs listed in Table 2.3 to create global temperature projections for the four RCPs (van Vuuren et al., 2011).

The DSM generates spatially explicit climate data at various temporal scales from the single global-mean surface air temperature calculated by the SCM. The current DSM is CLIMGEN, which produces monthly, seasonal and annual mean climate data at a spatial resolution of 0.5◦× 0.5◦ grid-cell covering both the terrestrial land surface excluding

Chapter 2. Global crop modelling 28

Antarctica (Mitchell and Jones, 2005). CLIMGEN follows a pattern-scaling methodology currently based on GCM patterns from the third Coupled Model Intercomparison Project (CMIP3) archive (Meehl et al., 2007): any given change in annual mean temperature as simulated by MAGICC 6 can be linearly rescaled to represent spatial and temporal patterns of change in each climate variable. ClimGEN combines these patterns of change with the observed climatology, currently provided by the CRU TS 2.10 dataset, to produce patterns of mean absolute climate, and then combines them with observed time series of deviations from climatology to produce realisations of climate change over 2001 to 2100 with realistic yearly variability superimposed. CLIMGEN can generate monthly climate data for eight variables including mean, maximum and minimum temperatures, precipitation, vapour pressure, cloud cover and wet-day frequency. In the case of precipitation, change in GCM precipitation patterns is expressed as fractional change from present-day precipitation that is applied to the observed climatology by multiplication. To simulate a future change in both precipitation variability and mean precipitation, ClimGEN includes a gamma shape method where a gamma shape parameter represents the temporal distribution of precipitation (Aksoy, 2000). Change in the gamma shape parameter output by the GCMs is scaled by the required global-mean temperature change (Osborn, 2009). Future changes in the frequency of temperature extremes are not, however, as yet incorporated (Osborn, 2009; Warren et al., 2012).

Figure 2.4 presents the spread among the 72 climate change scenarios used in Chapter 3 in terms of global average temperature increase and total annual precipitation change for medium (2050s) (Figure 2.4(a)) and long (2080s) (Figure 2.4(b)) time horizons relative to the 1910s. Note the CRU TS 2.1 dataset begins in 1901 so that comparison to pre-industrial climate conditions, as typically done by the IPCC, was not possible here. Nonetheless, comparison to the 1910s time horizon gives a valuable indiction of the spread in the climate change scenarios ensemble. We calculated 30-year climatologies for each time-period. Most GCMs agree in a general increase in annual total precipitation globally except GFDL-CM21 that predicts a small decrease. Relative change in global

average temperature varies widely among GCMs and RCPs. GCM differences are

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Figure 2.4: Scatter-plots showing distribution of relative change between the 2050s and the 1910s (left) and between the 2080s and the 1910s (right) in global mean temperature and precipitation among the 4 RCPs × 18 GCMs. Each circle represents a combination of one RCP–GCM. Data for each RCP are presented in a different colour.