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KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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Climate Model Simulations of Stratospheric Geoengineering in the Arctic Spring 1 2 3
Ben Kravitz, Alan Robock, and Allison Marquardt 4 5 6
Department of Environmental Sciences, Rutgers University, New Brunswick, New Jersey 7 8 9 10 11 12 13 14 15 16 17 18
Submitted to Journal of Geophysical Research - Atmospheres 19 20
September, 2010 21 22 23 24 25 26 27 28 29 30 Ben Kravitz, Department of Environmental Sciences, Rutgers University, 14 College Farm 31
Road, New Brunswick, NJ 08901, USA. ([email protected]) (Corresponding 32 Author) 33
34 Alan Robock, Department of Environmental Sciences, Rutgers University, 14 College Farm 35
Road, New Brunswick, NJ 08901, USA. ([email protected]) 36 37 Allison Marquardt, Department of Environmental Sciences, Rutgers University, 14 College Farm 38
Road, New Brunswick, NJ 08901, USA. ([email protected]) 39 40 41
42
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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Abstract 43 We use a general circulation model of Earth’s climate to conduct simulations of 44
stratospheric sulfate aerosol geoengineering in the Arctic spring to determine whether 45
geoengineering during the months of maximum insolation is as effective as geoengineering year-46
round. As control cases, we simulate a global warming ensemble and an ensemble of global 47
warming combined with daily stratospheric injections of SO2 at high latitude, totaling 3 Tg SO2 48
per year. We compare these to two ensembles, each with global warming forcing and high 49
latitude stratospheric injections of 0.75 Tg SO2 per year: daily injections throughout April, May, 50
and June; and daily injections throughout April. These spring injection experiments show 51
smaller aerosol optical depth than the year-round injections, especially in the winter, during 52
which all of the sulfate aerosols from the spring injection experiments are removed each year. 53
They also show summer cooling over the Northern Hemisphere continents, as is seen in large 54
volcanic eruptions, although not as much as in the year-round injections. No significant 55
monsoonal precipitation perturbation is detected, in contrast to previous simulations with this 56
same model. Year-round injection results in an increase in Arctic sea ice from a control 57
scenario, and the spring injection experiments show reduced sea ice loss from the global 58
warming simulations. Further simulations are required, but these results suggest that while SO2 59
injections only in the spring are not as effective as year-round injections, a strategy of injections 60
in spring and summer combined would maximize the cooling of the aerosol cloud, requiring 61
slightly smaller total annual injections than a year-round strategy. 62
63
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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1. Introduction 64
Geoengineering with stratospheric sulfate aerosols has been proposed [e.g., Crutzen, 65
2006] as a cheap [e.g., Robock et al., 2009], effective [e.g., Rasch et al., 2008], and temporary 66
[e.g., Wigley, 2006] means of reducing global average surface air temperature to alleviate 67
negative climate impacts from increasing greenhouse gas concentrations. In an effort to tailor 68
geoengineering and reduce the degree to which humans directly interfere with and modify the 69
climate, some have suggested geoengineering only in the Arctic [Caldeira and Wood, 2008]. 70
They propose that this would have the effect of cooling the Northern Hemisphere continents and 71
potentially “saving” the Arctic sea ice, as is seen temporarily in the case of large volcanic 72
eruptions, but would not impact temperatures in the tropics or the Southern Hemisphere. Due to 73
the reduced area needing to be shaded by sulfate aerosols, geoengineering in the Arctic would in 74
theory require smaller injections of SO2 than geoengineering in the tropics. 75
Robock et al. [2008] performed simulations of both tropical and Arctic geoengineering. 76
They found features similar to those of large volcanic eruptions: summer cooling over the 77
Northern Hemisphere continents and weakening of the Indian/African summer monsoon, which 78
was more pronounced for the case of the Arctic injection. 79
These simulations by Robock et al. involved year-round injections of SO2. However, in 80
the Arctic, year-round injections would not be necessary, as there is no sunlight for the aerosols 81
to backscatter during the winter. This motivated our study to investigate Arctic geoengineering 82
that is tailored to backscatter solar radiation only during the summer, which is the period of 83
maximum insolation. 84
In addition to replicating the Arctic injection experiment from Robock et al. [2008], we 85
designed two additional experiments of geoengineering only in the Arctic spring. Assuming the 86
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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same daily rate of injection as the year-round experiment, this would reduce the amount of SO2 87
that is injected into the stratosphere, lowering the cost and the degree to which humans directly 88
interfere with the climate system. However, we hypothesize that since the aerosols would be 89
present during summer, the radiative effects of the aerosols would be similar to those of a year-90
round injection. We also wished to investigate whether the Asian/African monsoon system is 91
negatively impacted under these scenarios and whether they can prevent the loss of Arctic sea 92
ice. 93
2. Experiment 94
We conducted simulations with the coupled atmosphere-ocean general circulation model 95
ModelE, which was developed by the National Aeronautics and Space Administration Goddard 96
Institute for Space Studies [Schmidt et al., 2006]. We used the stratospheric version with 4° 97
latitude by 5° longitude horizontal resolution and 23 vertical levels up to 80 km. It is fully 98
coupled to a 4° latitude by 5° longitude dynamic ocean with 13 vertical levels [Russell et al., 99
1995]. 100
The aerosol module [Koch et al., 2006] accounts for SO2 conversion to sulfate aerosols, 101
as well as transport and removal of the aerosols. The chemical model calculates the sulfur cycle 102
in the stratosphere, where the conversion rate of SO2 to sulfate is based on the respective 103
concentrations of SO2 and the hydroxyl radical, the latter of which is prescribed [Oman et al., 104
2006a]. We specified the dry aerosol effective radius to be 0.25 µm, which is the value used for 105
simulation of past volcanic eruptions and geoengineering. The model hydrates the aerosols 106
based on ambient humidity values according to formulas prescribed by Tang [1996], resulting in 107
a distribution of hydrated aerosols with an effective radius of approximately 0.30-0.35 µm, 108
which is consistent with the findings of Stothers [1997]. Radiative forcing from the aerosols is 109
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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fully interactive with the atmospheric circulation and in our paper is the conventional one as 110
defined by the Intergovernmental Panel on Climate Change [IPCC, 2001], also called “adjusted 111
forcing” (Fa) by Hansen et al. [2005]. For more details, we refer the reader to Kravitz et al. 112
[2010a], which used the same model version and setup. 113
This version of ModelE has been successfully used in the past to simulate both volcanic 114
eruptions and geoengineering. Simulations have been conducted for the eruptions of Laki in 115
1783-1784 [Oman et al., 2006a, 2006b], Katmai in 1912 [Oman et al., 2005], Pinatubo in 1991 116
[Robock et al., 2007], and the recent eruption of Kasatochi in 2008 [Kravitz et al., 2010a]. In all 117
of these cases, ModelE was shown to be reliable in recreating the climate impacts of the 118
eruption. Moreover, Robock et al. [2008] used this model to simulate geoengineering, and 119
results from this study agreed with similar experiments performed by the Hadley Centre [Jones 120
et al., 2010]. Therefore, we are confident in this model’s ability to simulate geoengineering to a 121
degree of accuracy that is scientifically useful. 122
We used the same version of ModelE that was used by Robock et al. [2008], using the 123
same specifications except for two tuning parameters and the atmospheric and oceanic initial 124
conditions. Robock et al. used atmospheric and oceanic conditions from the year 1999, whereas 125
we used those conditions from the year 2007. The current version of ModelE was tuned by 126
modifying two parameters that change planetary albedo, and hence, the net radiation of the 127
planet. This model was tuned because Robock et al. detected a significant temperature trend 128
during the period over which they conducted their simulations, due to insufficient time allowed 129
for model spin-up. These tuning parameters modify the critical humidities for ice cloud and 130
water cloud condensation. Specifically, Robock et al. used tuning parameters U00ice=0.590 and 131
U00wtrx=1.33. The version used for the simulations in our study used parameters U00ice=0.595 132
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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and U00wtrx=1.40. Before simulations were performed with these new parameters, the model 133
was spun-up for an additional 100 years. This resulted in a much smaller trend over the model 134
period 2007-2026, and, after conducting further simulation with a control run, the temperature 135
trend under these new tuning parameters is largely negligible. In Section 5, we discuss the 136
effects this tuning had on our results. 137
We began with a 6-member control ensemble of 20-year runs (2007-2026), during which 138
global greenhouse gas concentrations, as well as aerosol concentrations, remained fixed at 139
constant 2007 conditions. We then simulated a 6-member ensemble of 20-year runs covering the 140
same period, in which global greenhouse gas concentrations increased according to the IPCC’s 141
A1B scenario [IPCC, 2007]. The greenhouse gas concentrations at the beginning of the 142
simulation were prescribed to be January 1, 2007 levels, and they increased to the A1B 143
scenario’s estimation of December 31, 2026 levels by the end of the simulation. 144
We conducted three 6-member ensembles of 20-year runs to simulate geoengineering, all 145
of which had the A1B greenhouse gas concentrations as a background. One ensemble involved 146
daily injections of 0.0082 Tg of SO2 into one grid box centered at 66°N, 122.5°E in the Arctic, 147
distributed equally in the three model layers that cover an altitude of 10-16 km. This 148
corresponds to an annual injection rate of 3 Tg of SO2, so we refer to this ensemble in the rest of 149
the paper as 3 Tg. This ensemble is conducted in the same manner as the Arctic geoengineering 150
scenario described by Robock et al. [2008], albeit with different initial conditions and tuning 151
parameters, which we describe below. Another ensemble, referred to as AMJ, involved daily 152
injections of the same amount at the same spatial points, but only in the months April, May, and 153
June. This results in a total injection rate of 0.75 Tg of SO2 per year. The third ensemble, 154
referred to as Apr, also results in a total injection rate of 0.75 Tg of SO2 per year, but the daily 155
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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injections occurred only in the month of April. This corresponds to a daily rate of injection of 156
0.0246 Tg, or three times the rate of the previous ensembles. The specifications for the different 157
ensembles are summarized in Table 1. 158
3. Climate Forcing 159
Figures 1 and 2 show the seasonal cycle, averaged over the second decade of simulation, 160
of sulfate aerosol optical depth and radiative forcing once the aerosol layer has achieved a steady 161
state. As with high latitude volcanic eruptions [Kravitz and Robock, 2010] and prior simulation 162
of Arctic geoengineering [Robock et al., 2008], the bulk of the aerosol cloud is mostly confined 163
to latitudes north of 30°N. 164
There is a large seasonal dependence of aerosol optical depth and, consequently, aerosol 165
radiative forcing. All geoengineering simulations show a strong summer peak in optical depth 166
during the summer, which is due to the chemical conversion time of SO2 to sulfate of 30-40 days 167
[McKeen et al., 1984]. In contrast to 3 Tg, AMJ and Apr show all sulfate aerosols are deposited 168
out of the atmosphere throughout the winter and are replenished each spring. In 3 Tg, aerosols 169
are created year-round in the mid-latitudes and throughout the summer, autumn, and early spring 170
in the high latitudes, resulting in a higher peak of aerosol optical depth and even some 171
maintenance of aerosol optical depth in the winter. AMJ and Apr do not have these sources of 172
year-round replenishment, so the aerosols are subjected to unmitigated large-scale removal, and 173
the magnitude of injection is insufficient to allow the aerosols to persist into the following spring 174
[Kravitz and Robock, 2010]. 175
Even though the amount of SO2 injected is the same in both AMJ and Apr, the AMJ 176
ensemble reaches higher peaks of aerosol optical depth and radiative forcing. The AMJ peak 177
occurs in July, which is consistent with the one-month chemical lifetime of stratospheric SO2. 178
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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This means that the daily injection rate and subsequent SO4 formation rate in the AMJ 179
experiment is greater than the late spring/early summer aerosol deposition rate at high latitudes. 180
The optical depths at mid-latitudes are nearly identical between the two spring injection 181
experiments, suggesting the difference between these two scenarios mostly occurs at high 182
latitudes. The optical depth from Apr is higher throughout the spring than AMJ, since the total 183
amount of SO2 injected under Apr is larger than AMJ until the end of June. 184
4. Climate Response 185
With both simulations of volcanic eruptions [Kravitz and Robock, 2010] and 186
geoengineering [e.g., Robock et al., 2008], an introduction of sulfate aerosols into the 187
stratosphere would cause an observable climate response if the injection is large enough, at the 188
proper time of year [Kravitz and Robock, 2010], or if the injection is continued for a long enough 189
period of time [Robock et al., 2010]. The main purpose of geoengineering would be to cool the 190
planet, so one of the primary climate responses should be a reduction in surface air temperature, 191
as is seen from large volcanic eruptions. This modifies dynamics in the Asian/African monsoon 192
region by reducing the land-ocean temperature gradient. Since this gradient is the primary driver 193
of monsoonal precipitation, geoengineering should weaken the monsoon. We discuss this 194
mechanism of precipitation reduction in more detail in the following section. 195
Figure 3 shows globally averaged temperature and precipitation over the simulated period 196
for all experiments. All three geoengineering scenarios show some cooling below A1B 197
temperatures, the strongest and most discernible cooling being from 3 Tg. The results from this 198
experiment are very similar to those in Robock et al. [2008]. Conversely, and contrarily to the 199
findings of Robock et al., none of the simulations, including the 3 Tg experiment, show a 200
reduction in globally averaged precipitation from a global warming situation, although all of the 201
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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ensembles do show statistically significant anomalies when compared to the control ensemble. 202
Trenberth and Dai [2007] argue that geoengineering would indeed cause a reduction in 203
precipitation, as is seen in the case of large volcanic eruptions, which is in concordance with the 204
results of Robock et al. 205
The classical climate responses to large high latitude volcanic eruptions have spatial 206
patterns as well as global average anomalies. Specifically, these patterns are summer cooling 207
over the Northern Hemisphere continents and a weakening of the Indian/African monsoon 208
system, both of which were found by Robock et al. [2008] in their simulations of Arctic 209
geoengineering. Similarly, in our 3 Tg experiment, we found summer cooling over the Northern 210
Hemisphere continents (Figure 4). We found a weak reduction of the summer monsoon system 211
(Figure 5). Robock et al. found a much stronger reduction, but we suspect the differences 212
between their results and ours are due to model tuning, which took place between the simulations 213
performed by Robock et al. and this current set of simulations. We discuss this in greater detail 214
in Section 5. However, the Hadley Centre climate model, when performing tropical injection 215
simulations, did not show a significant precipitation response [Jones et al., 2010], in contrast to 216
Robock et al. and the arguments of Trenberth and Dai [2007]. Therefore, we are unable to 217
ascertain whether monsoonal disruption is a robust feature of geoengineering. 218
Both AMJ and Apr appear to show winter cooling over the Northern Hemisphere 219
continents. However, winter temperatures have a much higher natural variability than summer 220
temperatures, so these anomalies are not necessarily indicative of any climate response to spring 221
geoengineering. Our analysis (not pictured) of dynamics and snow and ice coverage did not 222
reveal any potential forcing that would cause such temperature anomalies. Moreover, 223
temperature anomalies of the magnitude seen in the DJF panels of Figure 4 are similar in 224
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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magnitude to Antarctic temperature anomalies in the JJA panels, which would not be affected by 225
Arctic geoengineering. This leads us to conclude the Northern Hemisphere winter temperature 226
anomalies are due to internal variability. 227
5. The Effects of Model Tuning 228
As we discussed in Section 2, the model was tuned between the time Robock et al. [2008] 229
conducted their simulations and we conducted ours. This tuning was performed to resolve a 230
large temperature trend in the control run of Robock et al., but it also resulted in different 231
precipitation results between their Arctic geoengineering experiment and our 3 Tg experiment, 232
which replicates their experiment. To better determine how model tuning had an impact on our 233
precipitation results, we compared our control run ensemble with that of Robock et al. [2008]. 234
Because the tuning parameters modify the critical humidities for ice cloud and water cloud 235
condensation, we chose to analyze total cloud cover differences between the two control runs. 236
Figure 6 shows the resulting effects of model tuning, which results in a large reduction of 237
summer monsoonal precipitation over Southeast Asia, India, and the Sahel. It also results in an 238
increase in cloud cover during the boreal summer over nearly all of the oceans, in some locations 239
by up to 10%. This resulted in a weaker monsoon than in the simulations of Robock et al. 240
[2008], meaning any further weakening due to a geoengineering aerosol layer would not show as 241
prominently in the results. 242
Because of these changes, we investigated how tuning the model impacted the model’s 243
accuracy when compared to climate data. To perform this analysis, we obtained two control runs 244
from Robock et al. [2008] of 40 years each. We also obtained monthly precipitation data from 245
the Global Precipitation Climatology Centre (GPCC), covering the years 1999-2007 [Schneider 246
et al., 2008; Rudolf and Schneider, 2005]. These years were chosen because this is the most 247
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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recent precipitation data available from GPCC that would not have been directly affected by the 248
large El Niño of 1997-1998. To analyze total cloud cover, we obtained data from the 249
International Satellite Cloud Climatology Project (ISCCP) covering the same time period 250
[Rossow et al., 1996]. 251
Figure 7 shows the comparison between both sets of simulations (pre- and post-tuning) 252
with the GPCC precipitation data. This figure only shows data over land areas, as precipitation 253
data was not available over the oceans. Figure 8 shows the same comparison but with the ISCCP 254
cloud coverage data. Qualitatively, both sets of simulations appear to show similar results. 255
Comparing with Figure 6, the anomalies in Figures 7 and 8 are much larger, indicating the 256
differences between the versions of the model are a great deal smaller in magnitude than the 257
differences between the model and observations. 258
To assess which version of the model more accurately represents these fields, we 259
calculated the difference between the model and the observations at each grid point to form root 260
mean square errors for each month of the climatologies. The results of these calculations are 261
reported in Table 2. The climatology of the model post-tuning shows lower errors than pre-262
tuning for nearly all months and on average for both precipitation and cloud cover. Dividing by 263
the total number of grid boxes (land only for precipitation and globally for cloud cover), we can 264
obtain an average error for a given grid box, which is reported in the last line of Table 2. Using 265
reference values of 1 m of precipitation per year and 60% cloud cover in a given location, these 266
reported errors are quite large. An error of 2.65 mm day-1
of precipitation corresponds to 267
approximately 967 mm a-1
, or approximately 97% of the reference value. An error of 15.98% 268
cloudiness is approximately 26% of the reference value. However, Figures 7 and 8 show these 269
values are heavily influenced by localized regions with very large error. Calculating from the 270
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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totals, the difference between the model versions is approximately 3% for both precipitation and 271
cloud cover. 272
6. Saving the Arctic Sea Ice 273
Because of the negative radiative forcing in the Arctic due to the sulfate aerosols, the loss 274
of Arctic sea ice should slow under geoengineering, with a possible re-growth of sea ice. Table 275
3 shows average values and standard deviations of Northern Hemisphere September sea ice area 276
coverage for all of the experiments. Under A1B, Arctic sea ice coverage (averaged over the 277
second decade of the simulation) is reduced from the control case by over 105 km
2 according to 278
the model results. Under 3 Tg, sea ice actually increases in areal extent. In AMJ and Apr, sea 279
ice coverage is still reduced from the control case, but not as much as in A1B. Apr appears to be 280
slightly more effective at preserving sea ice than AMJ, suggesting having a higher radiative 281
forcing in the early spring is more important to maintaining September sea ice coverage than 282
having a lower radiative forcing for a longer period of time. Of all of these values, only AMJ 283
and 3 Tg are separable by more than one standard deviation. 284
Vinnikov et al. [1999] performed long control run simulations using the GFDL model 285
[Manabe et al., 1991, 1992; Manabe and Stouffer, 1997; Haywood et al., 1997] to determine 286
whether a given Northern Hemisphere sea ice trend over a certain time interval would occur by 287
chance. Using the second decade of our simulations, with 6 ensemble members each, we can 288
compare the trends in Table 3 with their results for a trend over a 60 year period. The units 289
given in Vinnikov et al. for a trend are million square km per 10 years, so our results are directly 290
comparable by multiplying their values by 10. Converting the results of Vinnikov et al., over a 291
60 year time interval, a trend of approximately 0.5 105 km2 per 10 years will occur by chance 292
less than 10% of the time, and a trend of approximately 1.5 105 km2 per 10 years will occur by 293
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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chance less than 0.1% of the time. With the exception of the Apr ensemble, all of our 294
simulations fall within this envelope of values. Specifically, the trend of 1.32 105 km2 over 295
simulation years 11-20, which is the result of the A1B ensemble, will occur by chance less than 296
0.5% of the time. For the geoengineering experiments, the trend of 0.77 105 km2 over 297
simulation years 11-20, which is the result of the 3 Tg ensemble, will occur by chance less than 298
3% of the time, and the trend of 0.63 105 km2 over simulation years 11-20, which is the result 299
of the AMJ ensemble, will occur by chance less than 5% of the time. 300
Comparing differences between the ensembles makes the results more stark. The trend 301
difference between the 3 Tg and A1B ensembles of 2.02 105 km2 over the second decade is 302
highly unlikely to have occurred by natural climate variability. Similarly, the difference between 303
the AMJ and A1B ensembles of 0.69 105 km2 over the second decade would occur less than 304
5% of the time by natural variability, and the difference between Apr and A1B of 1.02 105 km2 305
over the second decade would occur less than 1% of the time by natural variability. Due to the 306
large standard deviations of these trends, as reported in Table 3, we are hesitant to make firm 307
conclusions from this data. However, our results would suggest that a comparison between our 308
simulations and the results of Vinnikov et al. [1999] implies geoengineering in the Arctic spring 309
will prevent some loss of sea ice compared to an A1B scenario. 310
The version of ModelE used to generate this output has a problem with the sea ice 311
module which results in a reduced climate sensitivity in the Arctic, meaning the loss of sea ice 312
under anthropogenic warming is slower than should be experienced [G. Schmidt, personal 313
communication]. The effect of this model issue on our results would be a smaller response of 314
sea ice coverage to radiative forcing than should be experienced, meaning were the issue not 315
present, sea ice loss under the A1B scenario would be greater, and the recovery of the sea ice 316
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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under geoengineering would be more pronounced. This could also have the effect of making the 317
sea ice recovery from geoengineering more statistically significant, although we are unable to 318
quantify this potential change in our results. 319
7. Discussion and Conclusions 320
We conclude that geoengineering with stratospheric aerosols in the Arctic spring, despite 321
taking advantage of the months of maximum insolation, is not as effective as year-round SO2 322
injections of the same daily rate at cooling the planet or preventing the loss of Arctic sea ice. 323
However, each of our spring injection experiments shows cooler temperatures and more Arctic 324
sea ice than the A1B ensemble, which are two of the desired purposes of geoengineering. Only 325
our 3 Tg experiment shows even a slight monsoonal reduction. We argue this difference 326
between our results and those of Robock et al. [2008] is due to model tuning, but we are unable 327
to conclude whether geoengineering would indeed cause a monsoonal disruption. According to 328
our model results, if the primary goal of a geoengineering policy were to save the Arctic sea ice, 329
the spring injection scenarios presented here appear to be well suited to satisfying this policy. 330
The effectiveness of AMJ and Apr could be increased by magnifying the summer 331
radiative forcing to levels seen in 3 Tg. This could be accomplished by increasing the daily rate 332
of injection or, in analogy to Kravitz and Robock [2010], extending the time of year over which 333
the injections occur. This latter means is potentially the more effective one, as it will counteract 334
some of the aerosol deposition that occurs throughout the year, and it will allow for aerosol 335
creation during months that have sunlight but do not experience maximum insolation. In either 336
case, this would involve an increase in the annual amount of SO2 injected, although the total 337
amount would still be less than year-round injection. Based on our findings here and the results 338
of Kravitz and Robock, we postulate a near optimal injection scenario would be at the same daily 339
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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rate as the 3 Tg experiment, but with daily injections over the 6 month period of mid-March 340
through mid-September. However, further experimentation is needed to assess this hypothesis. 341
This study has a strong need for follow-up and improvement upon the results presented 342
here. First, to make the results comparable to those of Robock et al. [2008], the spring injection 343
experiments could be run with the version of the model prior to tuning to analyze precipitation 344
effects of geoengineering. However, the model was tuned to resolve a strong temperature trend, 345
and as we show, this tuning affected the model results. The model’s accuracy in resolving 346
precipitation and cloud coverage appears to agree better with observations as a result of the 347
tuning, but these fields are too different from the observations to conclusively assert that tuning 348
improved the model output. Moreover, geoengineering simulations of a similar nature need to be 349
conducted by multiple modeling groups to conclusively determine whether monsoonal disruption 350
is a robust feature of geoengineering. The Geoengineering Model Intercomparison Project 351
(GeoMIP) [Kravitz et al., 2010b] has placed this as one of its priorities, so this question may 352
soon be resolved. Second, this same study could be repeated on a different version of the model 353
in which the sea ice issue has been resolved in order to verify our hypotheses about how it 354
affected our results. We could additionally extend our runs past 2026 to obtain better statistical 355
significance. 356
Finally, Heckendorn et al. [2009] argue the aerosol size we used in our study is likely 357
much smaller than the aerosol size that would actually result from stratospheric geoengineering. 358
Using a more representative size would likely require larger injection amounts, as the scattering 359
efficiency and aerosol lifetime would both be reduced. However, since the aerosol layer in the 360
spring injection runs is replenished every year (Figure 2), perhaps the results of Heckendorn et 361
al. would not apply to these experiments, but would only apply to year-round injection scenarios. 362
KRAVITZ ET AL.: ARCTIC SPRING GEOENGINEERING
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Although this study is part of an investigation into the optimal means by which the 363
climate could be geoengineered, we can still find many reasons why geoengineering may not be 364
a good idea [e.g., Robock, 2008]. Also, compared to some large volcanic eruptions, for which 365
the current observation system already has large gaps [Kravitz et al., 2010a], the amount of 366
climate interference suggested in this paper is small and would be difficult to observe and 367
analyze [Robock et al., 2010]. 368
369
Acknowledgments. We thank Georgiy Stenchikov for his help in the initial stages of this study 370
and Gavin Schmidt for detailed description of the Arctic climate sensitivity issue in ModelE. 371
Model development and computer time at Goddard Institute for Space Studies are supported by 372
National Aeronautics and Space Administration climate modeling grants. This work is 373
supported by NSF grant ATM-0730452. 374
375
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Table 1. Specifications for the ensembles used in this experiment. All ensembles have 6 475
members and span the years 2007-2026 (20 year simulations). Each ensemble is described by its 476
background greenhouse gas (GHG) scenario, the daily rate of injection of SO2 into the lower 477
stratosphere of one grid box centered at 66°N, 122.5°E, the days of the year over which the SO2 478
was injected, and the total rate of injection per year. 479
480
Ensemble GHG
background
Daily injection
rate (Tg day-1
)
Days of injection
in a given year
Total annual
injection rate (Tg a-1
)
Control
constant 2007
conditions
0 N/A 0
A1B A1B 0 N/A 0
3 Tg A1B 0.0082 Jan 1 - Dec 31 3.0
AMJ A1B 0.0082 Apr 1 - Jun 30 0.75
Apr A1B 0.0246 Apr 1 - Apr 30 0.75
481
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Table 2. Errors for control run climatologies compared with data. Pre-tuning results are 482
simulations performed by Robock et al. [2008] and are calculated from two 40-year simulations. 483
Post-tuning results are simulations from this experiment, i.e., six 20-year simulations. GPCC 484
data [Schneider et al., 2008; Rudolf and Schneider, 2005] are a climatology calculated from the 485
years 1999-2007. These data were regridded by box averaging to the model’s resolution (4° 486
latitude by 5° longitude) and are only available over land areas. ISCCP data [Rossow et al., 487
1996] are a climatology calculated over the same period. They were also regridded by box 488
averaging to the model’s resolution, and data are available globally. Error values were 489
calculated by taking the root mean square of the differences in climatologies for each grid, with 490
averaging done over the total number of grid boxes at this resolution (3312 for the cloud cover 491
variable to represent global coverage, and 1108 for precipitation to represent land areas only). 492
Values are rounded to two decimal places. 493
494
Month
Precipitation
(mm/day)
Pre-tuning
minus GPCC
Precipitation
(mm/day)
Post-tuning
minus GPCC
Cloud Cover (%)
Pre-tuning
minus ISCCP
Cloud Cover (%)
Post-tuning
minus ISCCP
January 2.61 2.54 17.51 17.73
February 2.61 2.53 16.41 16.41
March 2.52 2.44 15.79 14.53
April 2.70 2.62 15.40 14.29
May 2.81 2.75 15.99 15.02
June 2.80 2.72 16.56 15.71
July 3.30 3.12 17.32 16.57
August 3.27 3.12 16.77 16.03
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September 2.95 2.84 15.91 14.86
October 2.51 2.46 15.17 14.67
November 2.46 2.34 17.15 17.46
December 2.43 2.32 18.19 18.43
Annual
Average 2.75 2.65 16.52 15.98
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Table 3. Northern Hemisphere September sea ice coverage anomalies for all detrended 495
ensembles. All values have units 105 km
2 and are rounded to two decimal places. Standard 496
deviation (σ) is calculated from the difference between September for each year and the average 497
September value, giving 120 degrees of freedom for the years 1-20 average (6 ensemble 498
members) and 60 degrees for the years 11-20 average. 499
500
Ensemble
Average
(Years 1-20)
σ
(Years 1-20)
Average
(Years 11-20)
σ
(Years 11-20)
A1B - Control -1.19 9.71 -1.32 3.37
3 Tg - Control 0.30 2.08 0.77 0.54
AMJ - Control -0.42 2.20 -0.63 0.83
Apr - Control -0.30 3.02 -0.30 1.03
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501
502
Figure 1. Seasonal cycle of sulfate aerosol optical depth (λ=550 nm) and surface shortwave 503
radiative forcing at the surface due to sulfate aerosols. Seasonal cycle is calculated from an 504
average of years 11-20 of the simulation. Shaded areas are ±1 standard deviation, calculated 505
from the different ensemble members (6 degrees of freedom). All values shown are Northern 506
Hemisphere averages.507
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508
509
Figure 2. Seasonal cycle of sulfate aerosol optical depth and surface shortwave radiative forcing 510
at the surface due to sulfate aerosols. Seasonal cycle is calculated from an average of years 11-511
20 of the simulation. All plots shown are zonal averages. The Southern Hemisphere is not 512
shown, as all values are zero at those latitudes. 513
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514
Figure 3. Globally averaged surface air temperature and precipitation due to geoengineering. 515
All values shown are running 12 month means, averaged from the current month through the 516
following 11 months (t+0 through t+11). Yellow shading shows the range of the individual 517
ensemble members of A1B minus control. Orange lines indicate ±1.96σ of a 12 month running 518
mean of the variability of the control ensemble.519
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520
521
Figure 4. Spatial plots of surface air temperature, showing differences between the 522
geoengineering ensembles and the A1B ensemble. All panels show averages of years 11-20 of 523
the simulations. The left column shows summer averages (JJA), and the right column shows 524
winter averages (DJF). Grey hatching is meant to obscure values that are not statistically 525
significant at the 95% confidence level, calculated using a Student's t test.526
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527
Figure 5. Spatial plots of precipitation, showing differences between the ensembles. All panels 528
show summer (JJA) averages, temporally averaged over years 11-20 of the simulations. Grey 529
hatching is meant to obscure values that are not statistically significant at the 95% confidence 530
level, calculated using a Student's t test.531
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532
Figure 6. Comparison of control run ensembles from Robock et al. [2008], which were 533
performed before model tuning, and this series of experiments, which were done after model 534
tuning. The ensemble of Robock et al. has two members of 40 years each. This series has six 535
members of 20 years each. Both panels show summer (JJA) averages. Grey hatching denotes 536
values that are not significant at the 95% confidence interval, as determined by a Student's t-test.537
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538
539
Figure 7. Spatial patterns of precipitation climatologies from Robock et al. [2008] and this set of 540
experiments compared with gridded precipitation data from GPCC, which was regridded to 541
match the model's resolution of 4° latitude by 5°longitude. Only values over land are shown, as 542
GPCC does not have precipitation data available for the oceans. Climatologies were calculated 543
from two 40-year control ensembles in Robock et al. and six 20-year ensembles for this 544
experiment. GPCC climatology was calculated from the years 1999-2007.545
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546
547
Figure 8. Spatial patterns of total cloud cover climatologies from Robock et al. [2008] (pre-548
tuning) and this set of experiments (post-tuning) compared with gridded precipitation data from 549
ISCCP, which was regridded to match the model's resolution of 4° latitude by 5° longitude. 550
Climatologies were calculated from two 40-year control ensembles in Robock et al. and six 20-551
year ensembles for this experiment. ISCCP climatology was calculated from the years 1999-552
2007. 553