Supplementary Material

Cloud cover climatologies in the Mediterranean obtained from

satellites, surface observations, reanalyses, and CMIP5 simulations:

validation and future scenarios

Aaron Enriquez-Alonso1, Arturo Sanchez-Lorenzo2, Josep Calbó1, Josep-Abel

González1, Joel R. Norris3

1Department of Physics, University of Girona, Girona, Spain

2 Instituto Pirenaico de Ecología, Consejo Superior de Investigaciones Científicas

(IPE-CSIC), Zaragoza, Spain

3Scripps Institution of Oceanography, University of California, San Diego, La Jolla, CA,

USA

Climate Dynamics

Corresponding author: aaron.enriquez@udg.edu

Tables

Table S1. Description of Physical Parameterization Schemes in the GCMs (updated from

Zhang et al., 2005). Models highlighted with (*) provide results with the ISCCP simulator.

Models highlighted with (--) do not provide future projections for the scenarios considered at

the end of our paper.

GCM Stratiform Clouds Convective Clouds Convection Cloud Microphysics

ACCESS 1.0 Uses HadGEM2 atmospheric physics.

ACCESS 1.3 Uses atmospheric physics similar to that of the Met Office GA1.0 model configuration (Hewitt et al. 2011), including the PC2 prognostic cloud scheme (Wilson et al. 2008)

BCC-CSM 1.1 Atmospheric module is BCC-AGCM2.1. Convection: Mass flux (Wu 2012)

BCC-CSM 1.1(m) Atmospheric module is BCC-AGCM2.2. Convection: Mass flux (Wu 2012)

BNU-ESM

The atmospheric module is CAM 3.5:Diagnostic (Klein and

Hartmann 1993; Kiehl et al. 1996; Collins et al. 2006)

(Xu and Krueger 1991; Hack 1994)

(Zhang and McFarlane 1995) and convective momentum

transport (Richter and Rasch 2008)

(Rasch and Kristjánsson 1998; Zhang et al. 2003)

CanESM2 (*) Fractional cloud cover is evaluated from the prognostic moisture and temperature fields through relative humidity (McFarlane et al. 1992)

CCSM4

The atmospheric module is CAM 4:As CAM 3.5, modified for polar clouds (Vavrus and

Waliser 2008)As CAM 3.5 As CAM 3.5 As CAM 3.5

CESM1-BGCThe atmospheric module is CAM 5:

CESM1-CAM5

CESM1-FASTCHEM (--)As CAM 4 (Park and Bretherton

2009) As CAM 3.5 (Morrison and Gettelman 2008)

CESM1-WACCM (--)

CMCC-CESM (--)

Uses atmospheric module ECHAM5 (as MPI models)CMCC-CM

CMCC-CMS

CNRM-CM5 Diagnostic (Ricard and Royer 1993) (Bougeault 1985)

Sub-grid condensation parameterization from

(Bougeault 1981; Bougeault 1982)

CSIRO-Mk 3.6.0. Uses atmospheric physics very similar to HadCM/HadGEM models

FGOALS-g2.0Diagnostic (Rasch and Kristjánsson

1998)

Mass flux (Zhang and McFarlane 1995)

(Rasch and Kristjánsson 1998)

FGOALS-s2.0 (--) Diagnostic RH based (Liu and Wu 1997) Mass flux (Tiedtke 1989)

FIO-ESM The atmospheric module is CAM 3.5 (see BNU-ESM)

GFDL-CM3 Prognostic (Tiedtke 1993; Geophysical Fluid

Dynamics Laboratory Global Atmospheric Model Development Team (GFDL

GAMDT) 2004)

Prognostic; (Tiedtke 1993; GFDL

GAMDT 2004)

RAS (Moorthi and Suarez 1992)

(Rotstayn 1997; GFDL GAMDT 2004)

GFDL-ESM2G

GFDL-ESM2M

GISS-E2-H

RH based, Sundqvist type (Del Genio et al. 2005)

Diagnostic (Del Genio et al. 2005)

Mass flux (Del Genio and Yao 1993) (Del Genio et al. 2005)

GISS-E2-H-CC

GISS-E2-R

GISS-E2-R-CC

HadCM3 (--) Statistical (Smith 1990) Diagnostic (Gregory and Rowntree 1990)

Mass flux (Gregory and Rowntree 1990; Gregory and

Allen 1991)(Smith 1990)

HadGEM2-AOStatistical (Smith 1990)

with modifications (Cusack et al. 1999; Webb et al.

2001)

Diagnostic (Gregory and Rowntree 1990) with modifications

(Gregory 1999)

Mass flux (Gregory and Rowntree 1990; Gregory and

Allen 1991)

(Wilson and Ballard 1999)HadGEM2-CC

HadGEM2-ES (*)

INM-CM4Diagnostic based on RH, temperature and vertical

temperature gradient(Betts 1986) (Betts 1986)

IPSL-CM5A-LR (*)

Statistical (Le Trent and Li 1991)

Statistical (Bony and Emanuel 2001) (Emanuel 1991) (Le Trent and Li 1991)IPSL-CM5A-MR (*)

IPSL-CM5B-LR

MIROC-ESM (*)

(Le Trent and Li 1991)MIROC-ESM-CHEM (*)

MIROC4h (--)

MIROC5 (*) (Watanabe et al. 2009). Convective clouds (Chikira and Sugiyama 2010)

MPI-ESM-LR (*)

Prognostic (Tompkins 2002)

Diagnostic; (Roeckner et al.

1996)

Mass flux (Tiedtke 1989; Nordeng 1994)

(Lohmann and Roeckner 1996)MPI-ESM-MR

MPI-ESM-P (--)

MRI-CGCM3 (*) New two-moment bulk cloud scheme (Tiedtke 1993; Jakob 2000). Convective clouds: mass-flux (Tiedtke 1989; Yoshimura et al. 2014)

NorESM1-M The atmospheric module is CAM 4 (see CCSM4)

NorESM1-ME

Table S2. Values of the different metrics used to compare the TCC from the CMIP5 models

and multi-model mean (MMM) against PATMOS-x. The metrics are the Mean Difference

(MD), the Mean Absolute Difference (MAD), the Skill Score (SS), the Annual Range (AR)

difference, this latter being defined as the AR (i.e. mean winter TCC minus mean summer

TCC) for a particular GCM minus the AR for the PATMOS-x, and the coefficient of spatial

and temporal correlation (R). Units are % of sky cover for MD, MAD and AR difference,

while SS and R are dimensionless values between 0 and 1. In parentheses, the ordinal

position of each model for each metric.PATMOS-x

Models MD MAD SS AR difference RACCESS1.0 -8.9 (24) 14.3 (18) 0.69 (17) 2.1 (11) 0.72 (10)ACCESS1.3 -0.2 (2) 13.8 (15) 0.72 (13) 1.4 (8) 0.68 (26)

BCC-CSM1.1 -4.8 (14) 13.2 (8) 0.72 (13) -2.0 (10) 0.69 (23)BCC-CSM-1.1(m) -12.1 (36) 17.8 (40) 0.56 (40) -5.4 (23) 0.60 (35)

BNU-ESM -10.0 (29) 14.8 (21) 0.65 (25) -0.8 (5) 0.72 (10)CanESM2 -8.9 (24) 15.2 (25) 0.69 (17) 0.0 (1) 0.68 (26)CCSM4 -18.5 (42) 21.7 (42) 0.44 (42) -17.4 (39) 0.55 (39)

CESM1-BGC -18.5 (42) 21.8 (43) 0.43 (44) -18.5 (43) 0.55 (39)CESM1-CAM5 -5.1 (16) 13.0 (4) 0.78 (1) 2.9 (16) 0.74 (2)

CESM1-FASTCHEM -19.0 (44) 21.9 (44) 0.44 (42) -17.7 (41) 0.56 (38)CESM1-WACCM -14.4 (41) 18.2 (41) 0.53 (41) -13.1 (34) 0.64 (34)

CMCC-CESM -2.3 (4) 13.7 (13) 0.75 (8) 4.5 (20) 0.70 (22)CMCC-CM -3.3 (9) 12.5 (1) 0.76 (5) -3.9 (17) 0.71 (16)

CMCC-CMS 0.1 (1) 12.5 (1) 0.78 (1) -1.1 (7) 0.71 (16)CNRM-CM5 -11.8 (33) 16.4 (34) 0.64 (30) -6.2 (24) 0.66 (31)

CSIRO-Mk3.6.0. -0.2 (2) 13.0 (4) 0.71 (15) 16.9 (37) 0.77 (1)FGOALS-g20 3.5 (11) 13.1 (7) 0.65 (25) -10.9 (33) 0.66 (31)FGOALS-s20 -14.1 (40) 17.4 (39) 0.66 (23) 9.2 (30) 0.73 (6)

FIO-ESM -9.1 (26) 14.6 (20) 0.64 (30) -9.1 (29) 0.68 (26)GFDL-CM3 7.2 (21) 13.7 (13) 0.73 (12) -0.1 (2) 0.74 (2)

GFDL-ESM2G 2.3 (4) 12.9 (3) 0.77 (3) 6.5 (25) 0.74 (2)GFDL-ESM2M 3.1 (7) 13.3 (10) 0.76 (5) 5.1 (22) 0.72 (10)

GISS-E2-H 5.5 (18) 15.7 (29) 0.60 (34) -17.5 (40) 0.50 (43)GISS-E2-H-CC 4.5 (13) 15.3 (26) 0.61 (33) -18.2 (42) 0.52 (41)

GISS-E2-R 4.9 (15) 15.7 (29) 0.60 (34) -18.6 (44) 0.49 (44)GISS-E2-R-CC 4.9 (15) 15.5 (27) 0.60 (34) -17.0 (38) 0.51 (42)

HadCM3 -7.6 (22) 15.5 (27) 0.65 (25) -13.7 (35) 0.59 (37)HadGEM2-AO -9.7 (28) 14.9 (22) 0.67 (21) 2.4 (14) 0.71 (16)HadGEM2-CC -8.4 (23) 14.2 (17) 0.69 (17) 2.2 (12) 0.72 (10)HadGEM2-ES -9.1 (26) 14.4 (19) 0.68 (20) 2.2 (12) 0.72 (10)

INM-CM4 -5.9 (19) 13.4 (11) 0.75 (8) 10.4 (32) 0.74 (2)IPSL-CM5A-LR -12.1 (36) 16.4 (34) 0.66 (23) 9.5 (31) 0.72 (10)IPSL-CM5A-MR -11.3 (30) 16.6 (36) 0.67 (21) 8.1 (27) 0.69 (23)IPSL-CM5B-LR -3.2 (8) 13.9 (16) 0.75 (8) 2.6 (15) 0.68 (26)

MIROC4h -13.8 (39) 16.8 (38) 0.60 (34) -4.5 (20) 0.71 (16)MIROC5 -11.7 (32) 15.1 (24) 0.64 (30) -1.0 (6) 0.73 (6)

MIROC-ESM -12.0 (35) 16.1 (32) 0.65 (25) 4.3 (19) 0.73 (6)MIROC-ESM-CHEM -11.6 (31) 15.8 (31) 0.65 (25) 3.9 (17) 0.73 (6)

MPI-ESM-LR -3.0 (6) 13.0 (4) 0.77 (3) 0.1 (2) 0.71 (16)MPI-ESM-MR -3.3 (9) 13.4 (11) 0.75 (8) -1.9 (9) 0.69 (23)

MPI-ESM-P -3.6 (12) 13.2 (8) 0.76 (5) 0.3 (4) 0.71 (16)MRI-CGCM3 -6.9 (20) 15.0 (23) 0.70 (16) -13.8 (36) 0.60 (35)NorESM1-M -12.1 (36) 16.7 (37) 0.59 (39) -8.4 (28) 0.65 (33)

NorESM1-ME -11.9 (34) 16.3 (33) 0.60 (34) -7.2 (26) 0.67 (30)Multimodel Annual -6.4 11.2 0.56 -3.0 0.81

Multimodel DJF -6.6 12.4 - - 0.67Multimodel MAM -9.4 12.3 - - 0.71

Multimodel JJA -3.6 9.3 - - 0.81Multimodel SON -6.0 10.7 - - 0.78

Figures

Figure S1. Annual Mean Absolute Difference (MAD, %) of the CMIP5 models and multi-

model mean (bottom row, fifth column) with respect to ISCCP.

Figure S2. Annual Mean Difference (MD, %) of the CMIP5 models and multi-model mean

(bottom row, fifth column) with respect to PATMOS-x.

Figure S3. Annual Mean Absolute Difference (MAD, %) of the CMIP5 models and multi-

model mean (bottom row, fifth column) with respect to PATMOS-x.

Figure S4. Annual Skill Score (SS) of the CMIP5 models and multi-model mean (bottom

row, fifth column) with respect PATMOS-x. The best SS correspond to whitish colors. By

contrast, warmer colors show the worst agreements according to the SS.

Figure S5. Annual and seasonal (left) MD (%) and (right) MAD (%) between of the multi-

model mean with respect to PATMOS-x.

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