Impact of solar panels on global climate · 103 this not only will lead to reduced total production...

Impact of Solar Panels on global climate1

Aixue Hu1*, Samuel Levis1,2, Gerald A. Meehl1, Weiqing Han3, Warren M. Washington1, Keith 2

W. Oleson1, Bas J. van Ruijven1, Mingqiong He4, Warren G. Strand13

1Climate and Global Dynamics Division, National Center for Atmospheric Research, Boulder, 4

CO 80305, USA5

2Now at the Climate Corporation, San Francisco, California6

3Department of Atmospheric and Oceanic Sciences, University of Colorado, Boulder, CO 80301,7

USA8

4Meterological Bureau of Hubei Province, Wuhan, Hubei Province, 430074, China 9

*Correspondence to: [email protected]

Impact of solar panels on global climate

SUPPLEMENTARY INFORMATIONDOI: 10.1038/NCLIMATE2843

NATURE CLIMATE CHANGE | www.nature.com/natureclimatechange 1

© 2015 Macmillan Publishers Limited. All rights reserved

http://dx.doi.org/10.1038/nclimate2843

Supplementary material: 11

CCSM4 and its urban model: 12

The National Center for Atmospheric Research (NCAR) Community Climate System 13

Model version 4 (CCSM4) is a fully coupled climate model which is developed under the 14

collaboration among NCAR scientists, Scientists from US Department of Energy Laboratories, 15

and university scientists. The version of the model used here is the default version which 16

contains the Community Atmospheric Model version 4 (CAM4) with 26 vertical levels and a 17

horizontal resolution of 1 degree, the Parallel Ocean Program (POP) version 2 with 60 levels 18

vertically and nominal 1 degree resolution horizontally, the Community Land Model version 4 19

(CLM4), and the Community Ice Code version 4 (CICE4)1. The equilibrium climate sensitivity 20

to a CO2 doubling is 3.2oC and the transient climate response from a 1% CO2 simulation around 21

the time of CO2 doubling is 1.73oC for CCSM4, lying near the mid-way values among the 22

CMIP5 models2. 23

The land component of CCSM4 is the Community Land Model (CLM4)3,4. Included in 24

the CLM4 is an urban canyon parameterization that is modeled as a separate land unit within 25

each model grid cell [Community Land Model Urban (CLMU)]. A full technical description of 26

CLMU can be found in Oleson et al.5. Here a brief description of the parameterizations for 27

anthropogenic heat flux (urban space heating, air conditioning, and waste heat) is provided. 28

The urban land unit has the following five components denoted as columns in the CLM4 29

subgrid framework: roof, sunlit wall, shaded wall, and pervious (e.g., to represent lawns and 30

parks) and impervious (e.g., to represent roads, parking lots, sidewalks) canyon floor. Each 31

column is divided into 15 below-ground layers for temperature calculations. A one-dimensional 32

heat conduction equation is solved numerically for each column to determine conduction fluxes 33


into and out of each surface. The lower (internal) boundary conditions for roofs and walls are 34

determined by an approximation of internal building temperature held between maximum and 35

minimum temperatures as prescribed by the urban properties dataset6. The amount of energy 36

required to be added to bring the interior building temperature up to the minimum temperature 37

and the amount of energy required to be removed from the building interior to reduce the interior 38

building temperature to the maximum temperature are proxies for the space heating and air 39

conditioning fluxes, respectively. The heat removed by air conditioning is added as waste heat 40

(sensible heat) to the canyon floor, in proportion to pervious and impervious surface fraction. 41

Waste heat from inefficiencies in the heating and air conditioning equipment and from energy 42

lost in the conversion of primary energy sources to end use energy is also added as sensible heat 43

to the canyon floor5. The total amount of anthropogenic heat flux added to the climate system is 44

the sum of the energy due to the nonzero internal boundary condition for roofs and walls, the air 45

conditioning flux, and the waste heat7. This energy is distributed in urban areas and depends on 46

the local urban climate simulated in the model. 47

It is worth noting that the current CCSM urban model takes into account only energy 48

consumed for building space heating and cooling which is about 1/5 of the total current energy 49

consumption. The other two major sectors, transportation and factories, use roughly the other 4/5 50

of the total energy consumption which is not included in the CCSM simulation. 51

In the SPDU+UH simulation, we have set the living standard in the whole world to the 52

same as in the US. For example, air conditioning is used world-wide the same as in the US. The 53

purpose for this assumption is not meant to make the assumption realistic, but to consume as 54

much power as possible for our climate sensitivity analysis. By applying this assumption, heat 55

removed by air conditioning increases from only 0.14±0.01 TW in the Control to 27±1.4 TW in 56


the SPDU+UH run, and the total waste heat increases from about 6±0.3 TW to 104±5 TW. 57

Overall, the world-wide power consumption in our model simulations increases dramatically 58

from 5.4±0.5 TW in the Control to 109±5 TW in the SPDU+UH which is equivalent to a change 59

of the global mean radiative forcing from 0.01±0.001 W/m2 to 0.21±0.01 W/m2. In response to 60

this increased energy consumption, the global mean temperature rises by 0.09±0.12oC with 61

global mean urban temperature increasing by 1.1±0.2oC in comparison to SPDU (Table S1-S6). 62

Overall, one can clearly see that the impact of the energy consumption itself. The release of 63

waste heat into the environment does not affect the global mean temperature much, but the 64

greenhouse gases produced by burning of the fossil fuels can induce much more significant 65

global mean temperature changes. 66

Choice of background climate forcing and their potential impact to our conclusions: 67

In our simulations, we choose the representative concentration pathway (RCP) 2.6 as our 68

background climate forcing. This is a future greenhouse gas emission scenario which limits the 69

global mean surface temperature change by 2100 to be less than 2oC higher than the pre-70

industrial level. In this scenario, it assumes a 70% reduction of the greenhouse gas emission from 71

2010 to 2100. We define this climate scenario as our control climate. All of our sensitivity 72

simulations with solar panels are compared with this control simulation. In other words, we focus 73

on the anomalies in the sensitivity simulations relative to the CONTROL, or the changes, not the 74

absolute values. Therefore, our conclusions shown in this manuscript do not depend on the 75

choices of the climate background because we discuss here the potential impact of the solar 76

panels on regional and global climate against a background climate. If different climate 77

backgrounds were chosen, the absolute changes induced by solar panels on regional and global 78

climate may be different, but the overall impact will remain the same. 79


Limitations and constraints of solar panel installation and production: 80

In our simulations the solar panels cover 100% of the urban and desert regions as shown in Fig. 81

S1. This large coverage of course would not be feasible in the real world, but these are designed 82

as sensitivity experiments to provide a large forcing so that the climate system response can be 83

unambiguously detected. Such experiments are standard practice in the field of climate 84

modeling. The idea behind such sensitivity experiments is that smaller forcing (i.e. smaller areas 85

covered by solar panels) would produce a similar but smaller amplitude climate signal. In reality 86

solar panels can only cover a small portion of the urban area if the panels are only installed on 87

rooftops. Roof area occupies about 42% of total urban areas averaged globally in CCSM4. This 88

will reduce the urban energy production from 48 TW to about 20 TW which will still provide 89

enough energy for the short term. Of course, it is not possible that all roofs are suitable for solar 90

panel installation8. If solar panels are installed only on 50% of the roofs, the energy production in 91

urban areas reduces to about 10 TW, making it necessary to install solar panels outside of the 92

urban regions as well. Solar panel installation in the desert areas cannot be 100% either. Spacing 93

is needed between rows/strings of the panels in order to avoid shading and to maximize the solar 94

panel production. To properly maintain the panels, access roads are also needed. Corridors for 95

wild-life access and habitat preservation also need to be considered for large scale panel 96

installation. Given these limitations, solar panels in desert areas normally would only cover 97

about 40% or less of the land surface9. Therefore, the actual solar power production in our model 98

would be reduced by about 60%, i.e. about 296 TW for desert areas and 10 TW for urban areas 99

in SPDU and SPDU+UH experiments, and only about 24 TW for SPDLess experiment. By 100

comparing the three sensitivity simulations, we can expect that if a more realistic deployment of 101

the solar panels is used, such as reduced percentage coverage of solar panels in a certain area, 102


this not only will lead to reduced total production of solar power, but also result in reduced 103

impacts of the solar panels on local radiation balance, thus producing less of an influence on the 104

regional and global climate. For example, the changes of radiation budget in solar panel areas as 105

shown in Table S1 could reduce by 60%, and the corresponding temperature changes in these 106

areas would be reduced as well. 107

Currently we assume that the future solar panel installation is mainly in desert areas. The lack of 108

vegetation in these areas makes the impact of solar panel installation on evapotranspiration and 109

its feedback on local precipitation be very small. However, when solar panels are installed in 110

urban or other areas with dense vegetation, there is a potential that the ways on how the solar 111

panels are installed could influence the local evapotranspiration and its feedback on precipitation. 112

To assess these potential impacts, a specific model module, which is capable to simulation the 113

detailed processes of the solar panel-environment interactions10 (such as explicitly modeling the 114

mass, momentum, and energy balances of a large solar farm to more realistically represent these 115

processes), is needed which is beyond the scope of current model simulations, but is planned for 116

future model development work. 117

A comparison of the sensitivity simulations with CCSM4 and CMIP5 model ensemble 118

As shown in Table S4-6, the surface temperature changes in CCSM4, in general, agree with the 119

CMIP5 multi-model ensemble mean changes. Therefore, we only compare our sensitivity 120

simulations with the ensemble mean of the RCP scenarios using CCSM4. The global mean 121

temperature changes in the RCP scenarios are higher than that in any of our sensitivity 122

simulations, indicating that the greenhouse gas induced climate change signal is much larger than 123

the global and regional climate change induced by solar panel installation and consumption of 124


the power produced by solar panels. It becomes even clearer if we compare the regional 125

temperature and precipitation change patterns in Figures 1 and 2 with Figure S5. In Figure S5, all 126

RCP scenarios show a larger warming almost everywhere in comparison to the RCP2.6 than the 127

warming shown in Figure 1. Although the magnitude of the precipitation changes in the RCP 128

scenarios is comparable to the sensitivity simulations, the regional patterns are quite different. 129

For the desert regions with solar panels installed, the reduction of precipitation is much larger in 130

the sensitivity experiments than that in the RCP scenarios using CCSM4 (Table S6). In the RCP 131

scenarios, the precipitation changes in the desert regions are mostly insignificant, but they are 132

significant in SPDU and SPDU+UH experiments. 133

Future energy demands: 134

To gain some insight into plausible future ranges of demand for solar energy, we have analyzed 135

the IPCC WG3 AR5 scenarios database11,12. This database includes 1184 scenarios from the 136

peer-reviewed literature, generated by 31 different models. We have used this scenario database 137

because it represents the current state of the science on future scenarios for energy use and 138

emissions. The scenarios in this database comprise a wide range of different futures with respect 139

to population growth, economic growth, energy use, technology development and availability, 140

and climate policies. About 95% of the scenarios in the database were developed as part of nine 141

model comparison exercises: ADAM (Adaptation and Mitigation Strategies— Supporting 142

European Climate Policy)13; AME (Asian Modeling Exercise)14; AMPERE (Assessment of 143

Climate Change Mitigation Pathways and Evaluation of the Robustness of Mitigation Cost 144

Estimates)15,16; EMF 22 (Energy Modeling Forum 22)17; EMF 27 (Energy Modeling Forum 145

27)18-20; LIMITS (Low Climate Impact Scenarios and the Implications of required tight 146

emissions control strategies)21,22; POeM (Policy Options to engage Emerging Asian economies 147


in a post‐Kyoto regime)23,24; RECIPE (Report on Energy and Climate Policy in Europe)25; 148

RoSE (Roadmaps towards Sustainable Energy futures)26-30. Other scenarios in this database 149

originate from the Global Energy Assessment and explore a range of possible future 150

development pathways that meet policy goals on climate change, with energy access and air 151

quality31-33. 152

From this database of scenarios, we extracted two key indicators to assess plausible future ranges 153

of solar energy production (see Table S7): solar electricity production and total primary energy 154

use. We have extracted these values at the global level, for two subsets of scenarios: 1) all 155

scenarios and 2) low emission scenarios (i.e. scenarios that stabilize GHG emissions at 450 or 156

550 ppm CO2-eq, or 2.6 or 3.7 W/m2 increase in radiative forcing). 157

The first indicator (solar electricity production) directly indicates what the global energy models 158

in the database deem a plausible range for solar power in scenarios where it competes with other 159

energy technologies such as wind power, hydropower, nuclear, or carbon capture and storage. 160

The highest value for solar energy production is 525 EJ/yr (or 17TW) by 2100, which comes 161

from the MESSAGE model scenario from the Global Energy Assessment, in which emissions 162

stabilize at 450 ppm while most of the mitigation takes place on the supply side of the energy 163

system and nuclear energy is phased out. This scenario implies a total solar electricity production 164

by the end of the century that is comparable to the scale of the present day total global energy 165

system. The maximum value for 2050 (131 EJ or 4 TW) comes from a comparable scenario (low 166

climate stabilization with limited technology availability) with the GCAM model. 167

The second indicator, total primary energy, provides insight into the total scale of the energy 168

system. The maximum values for this indicator by 2050 and 2100 are from the IMACLIM model, 169


for a baseline scenario where nuclear energy is phased out, and the total energy system increases 170

from 523 EJ (or 17TW) currently to 1980 EJ (or 63TW) by the end of the century. In scenarios 171

with limited greenhouse gas emissions, the scale of the energy system is expected to be 172

considerably smaller (max 1421 EJ, 45TW) due to the implementation of energy efficiency 173

measures that reduce the demand for energy. 174

Besides these indicators that are directly provided by the global energy models, we have 175

calculated a third indicator that quantifies what the demand for solar electricity would be if it 176

were to supply all global final energy use. Because the IPCC database does not provide sectorial 177

detail on final energy use in the scenarios, calculating this indicator is based on several 178

assumptions. This indicator is supposed to give a rough idea of the ultimate maximum scale of 179

demand for solar electricity. 180

First, a large portion of final energy use that is currently provided by fuels (such as natural gas, 181

oil, coal or biofuels) is expected to shift to electricity over the century in many of the scenarios. 182

For this, we rely on the global energy models behind the IPCC database and directly use the 183

variable “final energy – electricity”. 184

Second, in virtually all economic sectors the remaining use of fuels can theoretically be 185

substituted by electricity or hydrogen (which can be produced from solar electricity through 186

electrolysis). However, for most energy services, there are large differences in efficiency 187

between fuels or electricity. Therefore, we make several assumptions for each sector. 188

In the transport sector, the ‘tank-to-wheel’ efficiency of an internal combustion engine to convert 189

fuel into movement is currently around 25% (and could be expected to increase to 35% over 190

time), while the efficiency of battery electric vehicles is around 90% 34,35. Not all energy 191


functions in the transport sector can theoretically be supplied by battery powered vehicles, but 192

hydrogen-powered fuel cells can ultimately supply energy for larger equipment, such as trucks, 193

ships or aircraft36,37. The chain efficiency of such a hydrogen system (i.e. from solar power to 194

hydrogen to movement) is assessed to be around 65% 38-41. Hence, assuming a 50% share for 195

both battery-electric and hydrogen powered vehicles, only 45% of the final energy fuel use 196

would be needed in the transport sector if solar electricity had to provide all primary energy 197

production. 198

In the buildings sector, fuels are mainly used for space heating and cooling. These energy 199

functions have an efficiency of around 90%, which we assumed to be similar whether fuels or 200

electricity is used42,43. Hence, if electricity had to provide all final energy in the buildings sector, 201

100% of the current fuel use would be needed. 202

In the industry sector, we assumed that most fuels are used for high temperature processes in the 203

heavy industry (steel production, cement production) at an efficiency of 90%. These high 204

temperatures could also be delivered by hydrogen produced from solar electricity44. However, 205

since electrolytic hydrogen production has an efficiency of 80% (and we assume that the final 206

application of hydrogen would be as efficient as other fuels), a shift to solar-electricity would 207

lead to an increase of energy use for the industry sector, requiring 112.5% of fuel use if it were to 208

be replaced by solar-electricity based alternatives. 209

Since no sector-level information is provided in the IPCC database, we have to make an 210

assumption about the shares of these sectors in final energy by the end of the century. Currently, 211

the shares of transport, building and industry in final energy use are roughly equal, around 30-35% 212

each, and we have assumed that this remains the case by the end of the century. Averaging the 213


changes in final energy use between these three sectors leads to a total efficiency gain of 14%. 214

Hence, if all remaining final energy use in fuels were to be replaced by solar-electricity based 215

technologies, only 86% of the final energy use of fuels would be needed as solar –electricity 216

demand. This is mostly due to efficiency gains in the transport sector, where electricity and 217

hydrogen are more efficiently applied than fuels. 218

Based on these assumptions, we derived that the scale of electricity demand in a fully solar-219

electricity powered energy system by the end of the 21st century would be 789-1138 EJ/yr (or 220

25-36TW) depending on the level of final energy use. This paper looks into centrally produced 221

solar-power, which has to be transported over long distances. Generally, transmission losses are 222

assumed to be around 10-20% 45,46. Taking the upper end of this range, we conclude that the 223

maximum plausible range of a fully solar-powered energy system by the end of the 21st century 224

would be around 1420 EJ/yr or 45TW. 225

Solar panels: 226

Based on the ways electricity is generated from solar power, there are three major types of solar 227

panels, namely photovoltaic (PV), thermophotovoltaic (TPV), and concentrated solar power 228

(CSP). The PV panels convert sunlight directly into electricity. When sunlight is absorbed by PV 229

panels, the solar energy knocks electrons loose in these PV panels, thus electricity can flow. A 230

TPV system converts radiant heat differentials directly into electricity via photons. This system 231

includes a thermal emitter and a PV diode cell. CSP is a system using mirrors and lenses to focus 232

the sunlight to a small area and converting this focused sunlight to heat, then this heat drives a 233

steam turbine to generate electricity. The concentrated PV (CPV) system uses lenses and curved 234

mirrors to focus sunlight onto small, multi-junction solar cells to improve the efficiency of the 235


PV panels. Except the conventional PV system, CPV, TPV and CSP systems all can reach an 236

efficiency of 40% or above and are suitable for large scale installation47-51. 237

Sensitivity simulations: 238

To investigate the impact of the more centralized solar panel installation in desert areas versus 239

the more distributed solar panel installation in urban areas, two additional simulations are carried 240

out under the same assumption as SPDU. The area for the desert where the solar panels are 241

installed is shown in Figure S10 which is about 100% more area than that in SPDU simulation. 242

The urban area is the same as in bottom panel of Figure S1. The total area with solar panels is the 243

same in these two simulations. In other words, the shaded area of bottom panel in Figure S10 244

plus the shaded area in Figure S1 bottom panel is equal to the shaded area of top panel in Figure 245

S10. These two experiments are named “solar panels in large desert areas” (SPDL) and “solar 246

panels in large desert areas and urban” (SPDUL). Figure S9a shows that since the area where 247

solar panels are installed is larger in SPDL (Figure S10) than SPDU (Figure 1), the regional and 248

global cooling effect is also larger in SPDL (global mean -0.52±0.15oC) than in SPDU (-249

0.34±0.12oC). However, the patterns of the surface temperature change in these two experiments 250

are almost the same. 251

On the other hand, although the total area where solar panels are installed is exactly the same in 252

SPDL and SPDUL, the global mean cooling effect in SPDUL reduces by 0.04±0.13oC (Figure 253

S9a, S9c). There are two reasons for this: 1. The impact of more distributed installation of solar 254

panels would reduce the overall impact on regional and global climate; 2. Many cities are located 255

at higher latitudes where less solar radiation reaches the surface, leading to a reduction of solar 256

energy production by 5%. This also reduces the impact on local and global precipitation (Figure 257

S9b and S9d), thus resulting in an overall reduced climate impact. 258


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396


Table: 397

Table S1 | Global totals for solar panel production, and changes in climate variables in solar 398 panel installed desert regions 399

Control Changes from Control SPDU area SPDLess area SPDU SPDU+UH SPDLess

Energy production (TW)

0 0 739±5 740±5 59±1

Energy production urban only (TW)

0 0 48±1 48±1 0

SPD incident direct solar radiation (TW)

2703±13 216±2 35±19 49±18 1±2

SPD total cloud cover (%)

21.5±1 9.8±1.6 -1.1±1.3 -1.5±1.3 -0.3±2.2

SPD absorbed direct solar radiation (TW)

1955±9 125±5 -374±3 -367±3 5±1

SPD reflected direct solar radiation (TW)

748±5 89±1 -330±3 -330±3 -63±1

SPD T in desert solar panel region (oC)

16.24±0.25 22.24±.40 -2.35±0.20 -2.17±0.15 0.04±0.64

SPD P in desert solar panel region (mm/yr)

291±4.1 29±14 -61±17 -68±18 -7±11

SPD Albedo 0.295±0.003 0.410±0.001 .114±0.003 .113±0.003 -.026±0.001 SPD represents solar panel installed desert area. T denotes temperature and P denotes 400 precipitation. The values for the Control are either area mean or area sum, and changes in SPDU, 401 SPDU+UH and SPDLess are with respect to the same area mean/sum in the Control. 402 Negative/positive values represent a decrease/increase relative to the Control. The numbers after 403 the ± sign represent the uncertainty which is represented by one standard deviation of that 404 variable. TW is Terawatts, 1 TW=1012 Joules/second. mm/yr is millimeter per year 405

406


Table S2 | Percentage changes of the radiation, cloud cover and precipitation in solar panel 407 installed desert area relative to Control run 408

Percentage Changes from Control SPDU SPDU+UH SPDLess

SPD incident direct solar radiation (TW) 1.3±0.7% 1.8±0.7% 0.5±1.0% SPD total cloud cover (%) -5±6% -7±6% -3±200% SPD absorbed direct solar radiation (TW) -19±0.2% -19±0.2% 4±0.8% SPD reflected direct solar radiation (TW) -44±0.4% -44±0.4% -77±1% SPD P in desert solar panel region (mm/yr) -21±6% -23±6% -21±38% Definitions of the variable in this table are the same as in Table S1, but for the percentage change 409 in the SPDU, SPDU+UH, and SPDLess relative Control. 410

411

Table S3 | Temperature, precipitation, and radiation on global and regional scales 412

Control Changes from Control SPDU SPDU+UH SPDLess

Global1 mean temperature (oC) 15.08±0.13 -0.34±0.12 -0.25±0.12 -0.04±0.13 Land mean temperature (oC) 12.11±0.21 -0.58±0.19 -0.41±0.18 -0.04±0.20 Urban mean temperature (oC) 21.10±0.20 -0.26±0.19 0.84±0.21 0.00±0.20 Global mean Precipitation (mm/yr)

1131±11 -6±4 -4±4 1±4

Land mean precipitation (mm/yr) 901±29 -8±4 0±6 1±6 Global incident solar radiation (TW)

97394±151 231±178 177±166 18±164

Land incident solar radiation (TW) 31709±112 111±113 110±125 16±107 Ocean incident solar radiation (TW)

65685±118 120±151 67±144 2±125

Global absorbed solar radiation (TW)

84801±140 -274±135 -284±143 0±142

Land absorbed solar radiation (TW)

23936±92 -320±88 -315±96 13±89

Ocean absorbed solar radiation (TW)

60865±113 46±97 31±109 -13±117

1Includes land and ocean 413 The values for the Control are either area mean or area sum, and changes in SPDU, SPDU+UH 414 and SPDLess are with respect to the same area mean/sum in the Control. Negative/positive 415 values represent a decrease/increase relative to the Control. The numbers in parenthesis are the 416 percentage changes relative to the Control. The numbers after the ± sign represent the 417 uncertainty which is represented by one standard deviation of that variable. TW is Terawatts, 1 418 TW=1012 Joules/second. m/yr represents meter per year. 419

420


Table S4 | Global and regional mean temperature of 1986-2005 for CMIP5 model ensemble 421 and for CCSM4, and the global and regional mean precipitation of 1986-2005 for CCSM4 422 only 423 Temperature (oK) Precipitation (mm/year) CMIP5 global 287.7±0.14 CCSM4 global 287.4±0.20 1085±4

desert 288.4±0.40 284±6 desertLess 294.5±0.31 30±6

424 Table S5 | Global mean temperature change relative to 1985-2005 mean for CMIP5 model 425 ensemble (oC) 426 427

Periods RCP2.6 RCP4.5 RCP6.0 RCP8.5 2041-2060 1.02±0.02 1.28±0.01 1.15±0.02 1.81±0.01 2081-2100 1.08±0.02 1.78±0.01 2.21±0.02 3.74±0.01 2011-2100 0.96±0.11 1.28±0.09 1.33±0.06 2.13±0.09

428 Table S6 | Global and regional mean temperature and precipitation changes averaged over 429 2011-2100 relative to climatological mean of 1985-2005 for CCSM4 and solar panel 430 simulations 431 432 Temperature (oC) Precipitation (mm/year) global Desert desertLess global desert desertLess RCP2.6 0.79±0.09 0.97±0.16 0.90±0.20 18.7±2.4 5.9±10.4 -1.7± 7.0 RCP4.5 1.20±0.10 1.54±0.17 1.38±0.21 24.4±2.1 4.7± 9.4 -0.6± 8.2 RCP6.0 1.30±0.06 1.66±0.13 1.59±0.20 24.5±2.1 4.7± 9.1 -0.7± 7.8 RCP8.5 2.02±0.10 2.58±0.15 2.54±0.20 36.9±2.4 11.3± 7.9 1.8± 8.0 Control 0.79±0.11 0.98±0.13 0.89±0.41 18.4±4.1 6.7± 4.1 -0.6±14.2 SPDU 0.45±0.10 -1.36±0.20 ̶ 12.5±4.1 -54.5±17.2 ̶ SPDU+UH 0.54±0.12 -1.19±0.15 ̶ 13.8±4.4 -61.0±17.5 ̶ SPDLess 0.75±0.12 ̶ 0.93±0.45 17.5±4.1 ̶ -6.83±11.4 433


434

Table S7 | Maximum values for solar electricity production and total primary energy use 435

from the IPCC AR5 scenarios database and derived maximum values for solar electricity 436

demand 437

TW EJ

2010 2050 2100 2010 2050 2100

All scenarios Electricity production, solar 0.015 4 17 0.462 131 525

Total primary energy 17 41 63 523 1281 1980

If all final energy were solar-electricity 15 30 45

472 957 1422

Low scenarios

Electricity production, solar 0 4 17 0 131 525

Total primary energy 17 33 45 523 1046 1421

If all final energy were solar-electricity 14 25 31

450 797 986

438


Figures: 439

440

Figure S1 | Areas where solar panels are installed. a desert areas and b urban areas. Green 441

stippling in panel a indicates where solar panels are installed for experiment SPDLess. Panel b 442

shows the percentage of the urban area in each model grid cell. 443

444


445

Figure S2 | Global mean temperature time series. Upper panel is the absolute values of the 446 global mean temperature for the four simulations, and lower panel is the global mean 447 temperature anomaly for the three sensitivity simulations relative to the Control. 448


449

Figure S3 | Global mean temperature evolution for coupled model intercomparison project 450 phase 5 (CMIP5). 451


452

Figure S4 | Time evolving global mean temperature and precipitation for RCP2.6, RCP4.5, 453 RCP6.0, RCP8.5 from CCSM4 and for solar panel sensitivity simulations. Left panels are 454 the temperature and right panels are the precipitation. Top panels are the global means, mid-455 panels are the mean of solar panel installed desert areas in Control, SPDU and SPDU+UH 456 experiments, and bottom panels are the mean of solar panel installed small desert areas in 457 Control and SPDLess experiments. 458


459

Figure S5 | Ensemble mean temperature and precipitation anomaly relative to the 460 ensemble mean of RCP2.6 for RCP4.5, RCP6.0 and RCP8.5 averaged over 2011-2100. 461 Contour interval for temperature is 0.2oC and for precipitation is 0.025m/yr. Stippling indicates 462 changes are significant at the 95% level using a double sided student t-test. 463


464

Figure S6 | Surface property anomalies relative to Control in SPDU. a the latent heat flux; b 465 sensible heat flux; c total leaf area index; d sea level pressure and surface wind. The units for 466 latent heat, sensible heat, and sea level pressure are given at the top-right corner of each panel. 467 The unit for surface wind is m/s and the leaf area index is m2 of leaf area per m2 of ground area. 468 Contour interval for latent and sensible heat flux is 2 W/m2, for total leaf area index is 0.2, and 469 for sea level pressure is 0.2 hPa. Stippling indicates changes are significant at the 95% level 470 using a double sided student t-test. 471

472


473

Figure S7 | 500hPa geopotential height (shading) and wind (vector). a Control; b 474 geopotential height and wind anomaly relative to the Control in SPDU; c geopotential height and 475 wind anomaly relative SPDU in SPDU+UH; d the same as b but in SPDLess. The unit for 476 geopotential height is meters and for wind is m/s. Contour interval is 100 hPa for a, 4 hPa for b, 477 and 2 hPa for c and d. Stippling indicates changes are significant at the 95% level using a double 478 sided student t-test. 479


480 Figure S8 | Mean zonal wind. a Control; b zonal wind anomaly relative to the Control in SPDU; 481 c zonal wind anomaly relative to SPDU in SPDU+UH; d the same as b but for SPDLess. The 482 unit is m/s. Contour interval is 5 m/s for Control, but 0.2 m/s for panels b-d. The stippling 483 indicates changes are significant at the 95% level using a double sided student t-test. 484

485


486

Figure S9 | Surface air temperature (left panels) and precipitation (right panels) changes in 487 the solar panel production sensitivity experiments. a/b temperature/precipitation difference 488 between experiments SPDL and control; c/d temperature/precipitation difference between 489 experiments SPDUL and SPDL. The numbers at upper right corner of each panel represents the 490 global average difference. The unit is oK for temperature and meter/year (m/yr) for precipitation. 491 Contour interval for temperature is 0.1oC, and for precipitation is 0.05 m/yr. Stippling indicates 492 changes are significant at the 95% level using a double sided student t-test. These simulations are 493 discussed in the main text and the supplementary material. 494


495

Figure S10 | A sensitivity experiment which is similar to SPDU, but with solar panel 496 installation expanded to including the entire Sahara Desert and deserts in the Middle East, 497 China and Mongolia. Panel a shows the regions where solar panels are installed in desert 498 regions only, and Panel b shows a reduced desert area where the solar panels are installed. The 499 area reduction in the bottom panel is equivalent to the total urban area in the model. Thus the 500 total areas where solar panels are installed are exactly the same in experiments SPDL and 501 SPDUL. 502

503


Impact of solar panels on global climate · 103 this not only will lead to reduced total production...

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