Drylands face potential threat under 2 C global warming target

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1 In the format provided by the authors and unedited. SUPPLEMENTARY INFORMATION DOI: /NCLIMATE3275 Drylands face potential threat under 2 C global warming target Jianping Huang 1 *, Haipeng Yu 1, Aiguo Dai 2,3, Yun Wei 1 and Litai Kang 1 which cover 71% of Earth s surface. The Paris Agreement aims to limit global mean surface warm- warming occurred over drylands. This large asymmetry between the Warming over land is also not evenly distributed, and large 1 Key Laboratory for Semi-Arid Climate Change of the Ministry of Education, College of Atmospheric Sciences, Lanzhou University, Lanzhou , China. 2 Department of Atmospheric & Environmental Sciences, State University of New York at Albany, New York 12222, USA. 3 National Center for Atmospheric Research, PO Box 3000, Boulder, Colorado , USA. * hjp@lzu.edu.cn NATURE CLIMATE CHANGE Macmillan Publishers Limited, part of Springer Nature. All rights reserved.

2 Table S1. CMIP5 models used in this study Model name Modelling centre BCC-CSM1.1 Beijing Climate Center, China BCC-CSM1.1-M Beijing Climate Center, China CCSM4 National Center for Atmospheric Research, USA CESM1-CM5 National Center for Atmospheric Research, USA CSIRO-Mk3.6.0 Commonwealth Scientific and Industrial Research, Australia FIO-ESM First Institute of Oceanography, China GFDL-ESM2G Geophysical Fluid Dynamics Laboratory, USA GISS-E2-H NASA Goddard Institute for Space Studies, USA GISS-E2-R NASA Goddard Institute for Space Studies, USA HadGEM2-AO Met Office Hadley Centre, UK IPSL-CM5A-LR Institute Pierre-Simon Laplace, France IPSL-CM5A-MR Institute Pierre-Simon Laplace, France MIROC-ESM Japan Agency for Marine-Earth Science and Technology, Japan MIROC-ESM-CH Japan Agency for Marine-Earth Science and Technology, Japan MIROC5 Atmosphere and Ocean Research Institute, Japan MRI-CGCM3 Meteorological Research Institute, Japan NorESM1-M Norwegian Climate Centre, Norway NorESM1-ME Norwegian Climate Centre, Norway 2

3 Table S2. The respective 11-year time slices for CMIP5 model used in these projections when the warming reaches 1.5 C and 2 C. Model 1.5 C 2 C Ensemble GFDL-ESM2M [ ] [ ] r1i1p1 HadGEM2-ES [ ] [ ] r1-4i1p1 MIROC-ESM-CHEM [ ] [ ] r1i1p1 NorESM1-M [ ] [ ] r1i1p1 3

4 Table S3. The model configuration of climate impact assessment. Section Variable Impact model Forcing Social CO 2 Irrigation GCM model Ensemble Members Agriculture Maize yield gepic ssp2 CO 2 firr 4 4 Hydrology Total runoff sdgvm jedi NA CO 2 NA 4 8 Drought P/PET Climatic Health suitability for Malaria transmission miasma ssp2 NA NA 4 4 4

5 a CRUTEM4 GISS CO2 CMIP5 MLOST b CRUTEM4 GISS MLOST CMIP5 CO2 Fig. S1 As in Fig. 1a but for (a) the CPC PREC/L precipitation dataset (b) for

6 a b Fig. S2 As in Fig. 1b but for (a) the CRUTEM4 and (b) GISS datasets. 6

7 a b c Fig. S3 As in Fig. 1c but for (a) , (b) and (c)

Fig. S4 Time series of global-mean surface air temperatures anomalies for the CMIP5 (relative to the 1861-1900 mean) and three observational datasets (relative to the 1880-1899 mean).

8 Fig. S4 Time series of global-mean surface air temperatures anomalies for the CMIP5 (relative to the mean) and three observational datasets (relative to the mean). The GISS, CRUTEM4 and MLOST are denoted by black, blue and yellow lines, respectively. Red (green) lines denote ensemble mean of the CMIP5 from historical simulations and RCP8.5 (RCP4.5) projections. The solid (dashed) lines are the mean of global (drylands) grids. Shading denotes the 95% confidence intervals of the 18 models, and gray dashed lines denote the 2.0 C and 1.5 C warming limitation conditions. 8

9 Fig. S5 The mean surface air temperature anomalies relative to mean for each CMIP5 model and the CMIP5 ensemble mean when the GMSW reaches (a) 2.0 C and (b) 1.5 C under the RCP8.5 scenario. Red squares denote the mean temperature changes over drylands, blue triangles denote the mean temperature changes over humid lands, and black dots denote the mean temperature changes over all land surfaces (60 S-65 N). The 1.5 C (2.0 C) warming condition is defined by projections in which the 11-year smoothed GMSW reaches 1.5 C (2.0 C) warming relative to the condition in each climate model. 9

10 a b Fig. S6 As in Fig. 2, but for all land surfaces (a) and oceans (b). 10

11 a b c d Fig. S7 As in as Fig. 2 for drylands (a), humid lands (b), all land surfaces (c) and global oceans (d), but with unforced internal components of observations (CRUTEM4, MLOST and GISS) and solid lines representing linear regressions. 11

12 Fig. S8 Left: Patterns of regression coefficients between global averaged CMIP5 ensemble mean temperature anomalies and local temperature anomalies from the CRUTEM4, MLOST and GISS. Right: Patterns of regression coefficients between unforced globally averaged CMIP5 ensemble mean temperature anomalies and local detrended temperature anomalies from the CRUTEM4, MLOST and GISS. 12

13 a b c Fig. S9 The estimated amplification factors for the externally forced components (orange) and for the raw data (i.e., including both forced and unforced components, green) for drylands, humid lands, and all land and ocean surfaces from the GISS, CRUTEM4 and MLOST datasets. 13

14 Fig. S10 Time series of global mean surface air temperature anomalies (relative to mean) for the CMIP5 18-model ensemble mean (red), GISS (black), CRUTEM4 (blue) and MLOST (yellow), with the red shading denoting the 95% confidence intervals for the 18 models. 14

15 a b c d Fig. S11 The same as Fig. 2, but for the ensemble mean of 64 all-forcing runs from 35 CMIP5 models 15

16 a b c d Fig. S12 The amplification factors of the forced changes over different regions estimated using data over different time periods from to based on the MLOST (yellow), CRUTEM4 (blue) and GISS (black) data sets and the CMIP5 18-model ensemble mean (red). 16

Fig. S13 Variations in the regionally averaged AOD (2001-2015) with respect to the climatological mean precipitation level (1948-2005) for warm

17 Fig. S13 Variations in the regionally averaged AOD ( ) with respect to the climatological mean precipitation level ( ) for warm seasons (May-September), cold seasons (November-March) and the annual mean. The shading denotes the standard deviation of temporal change for 2001 to

18 a b Fig. 14 Time series of the global mean surface temperature (a) and precipitation (b) anomalies relative to for GFDL-ESM2M (red line), HadGEM2-ES (blue line), MIROC-ESM-CHEM (green line), NorESM1-M (orange line) and 16 other CMIP5 models (grey lines). CMIP5 all-forcing historical simulations ( ) and the RCP8.5 projections (for 2005-) were used. 18

19 Fig. S15 The spatial distribution of global precipitation anomalies (mm/year) when GMSW reaches 1.5 C relative to the climatological mean of of each model under RCP8.5. The 11-year time slices when GMSW reaches 1.5 C are denoted after each model name. The four models used in the impact assessment are listed on the first row. 19

20 Fig. S16 The spatial distribution of global surface air temperature anomalies ( C) when GMSW reaches 1.5 C relative to the climatological mean of of each model under RCP8.5. The 11-year time slices when GMSW reaches 1.5 C are denoted after each model name. The four models used in the impact assessment are listed on the first row. 20

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Supporting Tables and Figures Desert Amplification in a Warming Climate Liming Zhou Department of Atmospheric and Environmental Sciences, SUNY at Albany, Albany, NY 12222, USA List of supporting tables