Speed of environmental change frames relative ecological risk in climate change and climate intervention scenarios

Domain: earth_science
Published: 2024-04-18
Editorial outcome: minor_revision

Abstract
Stratospheric aerosol injection is a potential method of climate intervention to reduce climate risk as decarbonization efforts continue. However, possible ecosystem impacts from the strategic design of hypothetical intervention scenarios are poorly understood. Two recent Earth system model simulations depict policy-relevant stratospheric aerosol injection scenarios with similar global temperature targets, but a 10-year delay in intervention deployment. Here we show this delay leads to distinct ecological risk profiles through climate speeds, which describe the rate of movement of thermal conditions. On a planetary scale, climate speeds in the simulation where the intervention maintains temperature are not statistically distinguishable from preindustrial conditions. In contrast, rapid temperature reduction following delayed deployment produces climate speeds over land beyond either a preindustrial baseline or no-intervention climate change with present policy. The area exposed to threshold climate speeds places different scenarios in context to their relative ecological risks. Our results support discussion of tradeoffs and timescales in future scenario design and decision-making.

Introduction
The imprint of anthropogenic climate change is clear in ecosystems worldwide, with worsening impacts expected under all future emissions pathways1–7. High-impact risks such as these motivate the study of potential climate intervention methods to reduce climate impacts as efforts to decarbonize continue8,9. Stratospheric aerosol injection (SAI) is a hypothetical method to limit warming or cool the planet by adding reflective particles to the stratosphere8. Many different potential SAI deployment scenarios could complement emissions reductions. For example, SAI could be used to maintain global temperatures at or below some critical threshold or to rapidly reduce temperatures8–12. In contrast to carbon dioxide removal interventions, which operate on slower timescales13, solar radiation management methods such as SAI currently represent the only known method to quickly alter global mean temperatures with near-future technology8,9.

Species habituated to environmental niches must shift their range, adapt, or be extirpated as ambient conditions shift geographically in a changing climate14,15. The climate velocity of 2-meter temperature expresses the movement of thermal conditions and can be used to address the question: How fast, and in what direction, must an organism move over a period to stay in the same temperature conditions in which it started?15,16. Species have varying ability to shift their range in response to climate change; on average, marine organisms can move more quickly than terrestrial species, and trees have among the slowest responses of all forms of life7,17,18. Climate impacts to ecology emerge from many sources beyond temperature, including changes in precipitation14, biogeochemistry2,15, or interactions among species19,20. Species with very short life histories (e.g., bacteria) can adapt to a changing climate through evolution, while more complex organisms may be able to employ behavioral adjustments1,14,21,22. Populations unable to adapt or shift their range at sufficient rates may be at risk of extirpation–which often takes place abruptly following subsequent extreme events rather than as a slow, linear process accompanying the climatic change1,23. The climate velocity provides a general metric for perturbations to large-scale ecology by the movement of thermal niches, rather than a tool to describe all types of impacts15,16,19.

Future values of the scalar magnitude of climate velocity (which we refer to as the climate speed) under scenarios consistent with present policy exceed mean dispersal rates of known terrestrial (⪅2 km/yr7,16) and marine species (≈7 km/yr7) and are expected to redistribute and endanger ecosystems globally4,6,15. The use of SAI has the potential to contribute an additional dimension of particularly rapid temperature change at the start or end of an intervention. Abrupt warming and cataclysmic climate speeds ("termination shock") are possible if an SAI intervention were to be terminated from masking a higher radiative equilibrium, or potentially dangerous cooling may occur at the start of an intervention intended to rapidly reduce global temperatures18,24. While the specific choices involved in generating a termination shock are clear18,24,25, the strategic design decisions that could result in dangerous cooling rates are currently unknown.

We analyze climate speeds in the 20-year period following SAI deployment in simulations performed in the Community Earth System Model Version 2 with Whole Atmosphere Community Climate Model (CESM2 [WACCM6]) under the Assessing Responses and Impacts of Solar climate intervention on the Earth system (ARISE) protocol11,26,27. ARISE simulations were constructed to allow outcomes to be directly connected to specific strategic design choices in each scenario. The ARISE-1.5 scenario portrays the deployment of SAI in 2035 to maintain the Paris Agreement global temperature target of 1.5°C above preindustrial against moderate-mitigation climate change (Shared Socioeconomic Pathway [SSP] 2-4.5)11. ARISE-DelayedStart has a similar target of ≈1.37°C but SAI deployment in 2045, yielding a rapid temperature reduction due to warming over the intervening decade28. A 10-year period represents a plausible delay that could come about from global governance and decision-making processes12,29. We compare these scenarios against baselines of preindustrial climate variability over the millennium prior to 1850 (Last Millennium, 850–1849) and no-SAI climate change consistent with present policy (SSP2-4.5)26,30–33.

Results
Distinct responses linked to strategic choices
Global maps of climate speeds (Fig. 1) reveal highly distinct outcomes in the pattern of ecosystem risk in each of the four scenarios, reflecting their individual temperature trends over time (Fig. 2).

Substantial climate speeds forced by warming occur nearly globally under no-SAI SSP2-4.5 (Fig. 1a, b). The majority of land area (61%) is exposed to potentially dangerous climate speeds beyond 2 km/yr (Supplementary Fig. 1a). Very large climate speeds are projected to cause extreme ecosystem stress in tropical regions where spatial gradients are weak (Fig. 1a, b)6,34. For example, ensemble mean climate speeds averaged over the Amazon region (as defined by the IPCC Working Group 1 Fifth Assessment Report35) are 12 km/yr, suggesting that tropical terrestrial species would need to move poleward or up topography by 240 km in order to remain in their starting conditions over this 20-year period. Sharp topographic gradients buffer climate speeds and allow relict populations to shelter in microclimates, but these communities often have low connectivity5,36 and persistent warming may render these niches inaccessible5,16,36,37. Poor connectivity can occur elsewhere due to causes including fragmentation by human land use such as urbanization38,39, or natural barriers as in semi-enclosed marine basins like the Mediterranean Sea40. This fragmentation impedes the ability of many ecological communities to shift in response to climate changes and may increase population vulnerability36,38,41.

In the ocean, while depth gradients in temperature allow some species to escape climate change, non-thermal constraints prevent many from shifting vertically15,34,42. Climate speeds are large in the Arctic, where the warming rate is high due to Arctic amplification43. Transport barriers imposed by the edge of continents and the North Pole mark poleward limits on terrestrial and marine species4,44. Negative climate speeds occur in the North Atlantic warming hole (Fig. 1b) where a decreasing temperature trend is driven by the weakening Atlantic Meridional Overturning Circulation in these simulations45,46. Analogous to historical ecosystem responses to persistent internally-driven temperature anomalies1,47, it is possible the North Atlantic may experience competing ecosystem responses between negative climate speeds associated with the warming hole and positive climate speeds elsewhere in the basin.

Climate speeds averaged over ten 20-year periods from the Last Millennium (Fig. 1c, d) are small, reflecting the relatively smaller magnitudes and slower evolution of climate forcings over this epoch. Volcanoes exert the largest external forcing on surface temperatures over the Last Millennium, but their influence is highly nonlinear and only persists for a few years48,49. As the climate velocity does not provide meaningful insight on these timescales15, we omit periods within 5 years of a large volcanic eruption (defined as 10 teragrams of stratospheric sulfate injection31). Internally-driven climate variability50,51, natural phenomena such as solar cycles52, small volcanic eruptions53, or anthropogenic land-use changes54 can still cause nonzero regional-scale climate speeds (e.g., in Eastern Europe on Fig. 1c). Climate speeds are larger over the ocean (Fig. 1d) than land (Fig. 1c), reflecting the smaller spatial temperature gradients in marine environments34. Where temperature gradients are shallowest over the tropical oceans, even small perturbations to temperature can drive nonzero climate speeds34. The small magnitude of these climate speeds indicate internal variability and natural forcings over this period could lead to distributional shifts among species, but would not likely exceed their dispersal capabilities. A purely unforced simulation with boundary conditions fixed at 1850 (Supplementary Fig. 2) produces qualitatively similar results.

The 20-year climate speeds following deployment of ARISE-1.5 in the year 2035 (Fig. 1e, f) to maintain global mean temperature at 1.5°C above preindustrial are relatively small compared to no-SAI SSP2-4.5 (Fig. 1a, b). These climate speeds reflect the nearly flat temperature trends implied by the use of SAI to maintain temperature (Fig. 2). Over land, climate speeds in ARISE-1.5 (Fig. 1e) are similar in magnitude to those in the Last Millennium simulation (Fig. 1c). Climate speeds over the ocean (Fig. 1f) are largely negative in sign. Since global temperatures are slightly above the 1.5°C target when the intervention is deployed in 2035, ARISE-1.5 forces a small negative trend in temperature (Fig. 2). On regional scales, internal climate variability can overwhelm the forced response to the SAI intervention (e.g., Fig. 1f in the eastern Pacific)55,56. Negative climate speeds occur in the North Atlantic warming hole similar to no-SAI SSP2-4.5, as the weakening of the Atlantic Meridional Overturning Circulation is partially offset–but not halted–by the SAI intervention in ARISE-1.511,46,57. Much more ocean area is exposed to climate speeds of 2 km/yr (48%) and 5 km/yr (17%) in ARISE-1.5 than the Last Millennium (23% and 8%, respectively). These values are within the observed mean dispersal rates of marine species (≈7 km/yr3,7), and little area is exposed to climate speeds that exceed these values in ARISE-1.5 (Supplementary Fig. 1b).

The SAI strategy in ARISE-DelayedStart produces large 20-year climate speeds (Fig. 1g, h) due to the negative temperature trend necessary to quickly reach the temperature target following deployment in 2045 (Fig. 2). A greater amount of land and ocean area is exposed to dangerous climate speeds in ARISE-DelayedStart (Fig. 1g, h) as opposed to no-SAI SSP2-4.5 (Fig. 1a, b; Supplementary Fig. 1). Two-thirds of land area (66%) is exposed to climate speeds beyond 2 km/yr; 13% of total land area (comparable to the size of South America) and more than a third of the world ocean (35%) are exposed to climate speeds greater than 10 km/yr in ARISE-DelayedStart (Supplementary Fig. 1). Five percent of the ocean is exposed to climate speeds beyond 50 km/yr (Fig. 1h, Supplementary Fig. 1b), which surpasses even the capability for extreme range shifts observed in many invasive species58. During the 20-year period following deployment, ARISE-DelayedStart depicts a forcing from climate speeds to global and regional ecosystems (Fig. 1g, h) that exceeds the corresponding time period in no-SAI SSP2-4.5 (Fig. 1a, b), and draws a striking contrast to the small values under ARISE-1.5 (Supplementary Fig. 3). This phenomenon of large climate speeds forced by rapid global temperature reduction could be viewed as a “deployment shock," similar to the termination shock previously identified if an intervention ceases at a high radiative equilibrium18,24.

Internal climate variability modulates conditions
Land and ocean median climate speeds in the Last Millennium simulation (Fig. 3a) illustrate the range of values experienced in 20-year periods in the preindustrial climate. The small magnitude of these climate speeds are within the range of dispersal rates for terrestrial and marine species7,16. When considering the full distribution of 20-year periods during the Last Millennium, climate speeds infrequently exceed mean dispersal rates of terrestrial species (≈2 km/yr) and never exceed those of marine species (≈7 km/yr) (Supplementary Fig. 9a). Median climate speeds over both the land and ocean from the ARISE-1.5 scenario where SAI is used to maintain global mean temperature fall within the distribution of Last Millennium variability (Fig. 3a). The global land and ocean median climate speeds in ARISE-1.5 are statistically indistinguishable from the Last Millennium simulation under a robustness test56. Climate speeds under no-SAI SSP2-4.5 robustly exceed both the Last Millennium and ARISE-1.5 over the land and ocean. In ARISE-DelayedStart, climate speeds surpass all other scenarios: the distribution is entirely separated from ARISE-1.5 or the Last Millennium, and robustly larger in magnitude than no-SAI SSP2-4.5 over land.

Contributions from internal variability are large on decadal to interdecadal timescales, such as the 20-year periods examined here, even in the presence of an external climate forcing such as an SAI intervention50,55,56. While analyzing the ensemble mean (Fig. 1) allows for investigation of the response to a climate forcing, each individual ensemble member (Fig. 3b, c, d) illustrates a plausible representation of the conditions that could be experienced under a single realization of internal variability. We describe the evolution of members across the ensemble of ARISE-DelayedStart to provide an example of the role of internal variability in the presence of a forced response to an SAI scenario of rapid temperature reduction. Climate speeds are large across the ensemble of ARISE-DelayedStart, exceeding dispersal rates in marine (≈7 km/yr) and terrestrial species (⪅2 km/yr) in every ensemble member over land and six members over the ocean (Fig. 3a). Still, on regional scales in individual members, internal variability from sources such as the El Niño-Southern Oscillation or Pacific Decadal Variability can moderate trends or even flip their sign (Fig. 3b)50. Other members display a spatial pattern more similar to the ensemble mean (Fig. 3c). When average global trends from internal variability are in phase with the forced response, individual realizations can experience negative climate speeds of greater magnitude everywhere around the globe (Fig. 3d). In one realization of ARISE-DelayedStart, this amplification from internal variability produces extreme median climate speeds over the global ocean exceeding 10 km/yr.

Previous analysis shows that the noise introduced by internal variability may impede detection of the surface climate response and lead to the perceived failure of an intervention8,55,59. The planetary-scale cooling in ARISE-DelayedStart is strong enough to entirely separate its distribution from no-SAI SSP2-4.5 when the sign of the trend is considered (Supplementary Fig. 9b); even in the member with the smallest global median climate speed, few regions see a sign opposite to the forced response (Fig. 3b). These results suggest that perceived failure at a regional or planetary scale would be much less likely under scenarios with rapid temperature reduction.

Relative ecological risk from climate speeds
Climate speeds of 10 km/yr provide a threshold of extreme risk by exceeding the dispersal rates of both adaptable families (such as mammals) and terrestrial and marine species on average7,16,18. We plot the global area exposed to these climate speeds against the annual rate of global temperature change for a wider range of datasets to efficiently summarize ecological risk (Fig. 4; see Table 1 for detailed data descriptions) and subsequently describe the implications for each product in the context of this figure.

The mean of all non-overlapping 20-year Last Millennium time periods is located at the origin, reflecting conditions that ecosystems experienced over the millennium before the Industrial Revolution began. The maximum area exposed (10% of global area) to the threshold climate speed over Last Millennium variability is denoted by the horizontal dotted line (Fig. 4). Greater distance from the origin beyond this dotted line denotes a relatively higher profile of ecological risk.

The European Reanalysis Version 5 (ERA5)60 provides an observationally constrained global estimate of climate speeds during the recent past (1996–2015)60,61. Over this same period, a historical simulation (CESM2-Historical27) displays a larger area exposed to the threshold climate speed and a higher rate of temperature change than in ERA5. The physical reasons underlying the overly rapid warming rate in CESM2-Historical during this period are an ongoing area of research, and likely include errors in prescribed biomass burning emissions62. More generally, discrepancies between ERA5 and historical simulations may partly be due to structural differences between the single realization of real-world climate variability and the ensemble mean forced response63. Overall, both ERA5 and CESM2-Historical display climate speeds beyond Last Millennium conditions and within the range of mid-century SSP2-4.5, consistent with known historical and expected future ecosystem stress from warming1,3,6,7,37,42.

No-SAI future scenarios expose substantial global area to large climate speeds from warming and cluster on the right half of Fig. 4. Scenarios with higher mitigation (SSP1-2.6), moderate mitigation (SSP2-4.5), and no mitigation (RCP8.5) all exceed the Last Millennium baseline, with increased emissions causing greater ecological risk. The relatively higher risk portrayed in the SSP2-4.5 simulation in the United Kingdom Earth System Model version 1 (UKESM1-SSP2-4.5) (see “Methods” section for full description) as opposed to CESM2-SSP2-4.5 primarily stems from the more rapid warming rates due to the higher climate sensitivity in UKESM164. The above results are in keeping with previous findings of widespread ecosystem stress under all future emissions pathways1–7,37,42, with the most extreme risks from climate change under scenarios with no mitigation34,65.

Scenarios where SAI is used for rapid temperature reduction cluster on the left side of the figure above the Last Millennium baseline and expose large amounts of global area to high climate speeds from rapid cooling. The individual scenarios (see Table 1 for details) each depict a unique potential design choice that could produce a deployment shock. One pathway would be through a delayed start with deployment after a temperature target has been surpassed, either through the choice to deliberately postpone deployment (ARISE-DelayedStart) or if the temperature threshold is breached early due to high climate sensitivity (UKESM1-ARISE-1.557). Alternatively, the intervention could be deployed with the explicit goal of obtaining a low-temperature target below the starting global mean value (CESM2-ARISE-1.011,12, or the simulations of ref. 66 [not shown]). Regardless of the underlying strategic logic, the salient point is that every SAI scenario with rapid temperature reduction exposes more global area to the threshold climate speed than its corresponding no-SAI climate change reference scenario: CESM2-ARISE-1.0 and CESM2-ARISE-DelayedStart compared to CESM2-SSP2-4.5, and UKESM1-ARISE-1.5 compared to UKESM1-SSP2-4.5.

SAI scenarios where the intervention is used to maintain global mean temperature (CESM2-ARISE-1.5, CESM1-GLENS-SAI) remain near the origin, with ensemble means within the bounds of Last Millennium conditions. These SAI scenarios essentially eliminate the most extreme risks to ecosystems from climate speeds occurring in the no-SAI climate change scenarios. However, a 10-year delay in deployment is the predominant difference between ARISE-1.5 and ARISE-DelayedStart. This short delay is sufficient to produce a highly distinct profile of extreme ecological risk.

Methods
Primary simulations
Our work draws on data from three simulations (SSP2-4.5, ARISE-1.5, and ARISE-DelayedStart) using the Community Earth System Model Version 2 with Whole Atmosphere Community Climate Model version 6 (CESM2 [WACCM6])26,27. CESM2 (WACCM6) is a fully interactive Earth system model with a high-fidelity depiction of the climate, including the stratospheric processes thought to be most relevant to SAI26,27,71. For all simulations here, CESM2 (WACCM6) was run with 70 vertical levels (model top ≈140 km) and 1.25° longitude × 0.9° latitude horizontal resolution11. This spatial scale (Table 1) is considered adequate to analyze global ecosystem risk in the broader ecology literature4,15,18.

The CESM2 (WACCM6ma) Last Millennium dataset is a simulation of the 1000-year interval 850 through 1849, immediately preceding the Industrial Revolution which is defined to begin in 1850 by convention in the climate modeling community26,30. Relatively abundant paleoclimate data allows for a well-constrained long-record depiction of this period including natural variability, realistic natural forcings including volcanoes and solar cycles, and anthropogenic land-use changes31. We use the Last Millennium to provide an ecologically relevant baseline of climate variability and change before anthropogenic climate change through greenhouse gas emissions and industrialization became large. CESM2 (WACCM6ma) is a middle-atmosphere configuration of CESM2 (WACCM6) and includes a simplified chemistry scheme to reduce computational complexity. The climate of CESM2 (WACCM6ma) is very similar to CESM2 (WACCM6) apart from the tropospheric chemistry72.

The SSP2-4.5 simulations depict a no-SAI future with moderate mitigation of climate change and the slow deployment of negative emissions technologies32. Five ensemble members were created for the Coupled Model Intercomparison Project Phase 651. An additional five ensemble members were created to augment the sample size for the ARISE project11. All 10 realizations are available from 2015–2069. SSP2-4.5 is consistent with present-day policy pledges by the global community, though it still results in warming beyond Paris Agreement targets in CESM2 and other climate models33,73.

We use the ARISE-1.5 and ARISE-DelayedStart datasets to explore two policy-relevant SAI scenarios11,28. These simulations are often referred to as ARISE-SAI-1.5 and ARISE-SAI-1.37-DelayedStart. We use the names ARISE-1.5 and ARISE-DelayedStart for brevity, or CESM2-ARISE-1.5 and CESM2-ARISE-DelayedStart when necessary to distinguish from scenarios run in other models. SAI in ARISE-1.5 is deployed in 2035 to maintain global mean temperature at the 2020–2039 average in CESM2 (WACCM6) (≈1.5°C above the IPCC AR6 preindustrial value)11,74. In ARISE-DelayedStart, SAI is deployed 10 years later in 2045 with a similar global mean temperature target of the 2020–2039 average from CESM1 (WACCM5) (≈1.37°C above the IPCC AR6 preindustrial value) to depict the impacts of a policy-relevant delay in deployment12. ARISE-DelayedStart requires a larger stratospheric sulfate burden than ARISE-1.5 due both to the delayed start and the slightly lower temperature target12,28.

Other design choices are constant between ARISE-1.5 and ARISE-DelayedStart: sulfur dioxide is injected at the same height (≈21 km), SSP2-4.5 greenhouse forcing is used in both, and each ensemble has ten members. Injections occur continuously from four locations (30° and 15° N/S, all at 180° E) with a proportional-integral feedback-control algorithm to maintain the pole-to-pole and pole-to-equator temperature gradients alongside the global temperature target11,75. Controlling for these goals with off-equatorial injections is intended to reduce side effects by compensating for the planetary-scale spatial patterns of greenhouse warming: the increase in global mean temperature, hemispheric asymmetry, and polar amplification76.

ARISE-1.5 and ARISE-DelayedStart are identical to SSP2-4.5 in every way except for the SAI intervention. Therefore, consistent differences between the simulations are likely due to the SAI strategies. The effect sizes of the SAI interventions in ARISE-1.5 and ARISE-DelayedStart relative to SSP2-4.5 are large enough that the global-scale results are clearly due to the SAI intervention (i.e., ensemble mean global temperature trend changing sign worldwide). Where useful, we additionally use the robustness test as a non-parametric method to identify where the forced response to the SAI intervention is large56. We refer to results as robust when they pass this test (corresponding to p < 0.1 under a binomial test).

The CESM2 (WACCM6) Preindustrial control (Unforced) provides a single 500-year integration of the Earth system with perpetual 1850 greenhouse gas forcing26. This simulation illustrates the range of internal climate variability over an extended period of time without external forcings51. The small climate speeds in the Unforced simulation (Supplementary Fig. 2) raise confidence that the model is adequate for our analysis: while internal climate variability can produce pronounced ecosystem impacts in individual regions, planetary-scale risk to ecosystems would be implausible under unforced variability alone1,47. We use Unforced as a reference to perfectly-unforced conditions under internal variability alone, although land-use changes during and before 1850 imply it does not perfectly represent true equilibrium conditions54.

Climate velocity
The climate velocity of a geophysical quantity describes the movement of the isopleths of that variable in a changing climate16. Formally, the climate velocity is defined as the ratio of the temporal gradient of a variable A (dA/dt, units time−1) to the spatial gradient of that same variable (∇A, units space−1)16. The resultant variable (CA) has units of space per time–that is, a velocity (Equation (1))16.

dA/dt / ∇A = CA (1)

Climate velocity can be calculated for any variable but is most frequently applied to temperature15. Temperature exhibits a clear large-scale response to both climate change and SAI and has relatively well-understood spatiotemporal behavior in both observations and model output15. We use 2m temperature rather than sea surface temperature over the ocean due to data availability limitations in ARISE-DelayedStart at the time of writing. On climatological spatiotemporal scales, 2m temperature is similar to sea surface temperature and is often used for aquatic ecosystem analysis77,78.

The climate velocity is a vector quantity, with both a magnitude and a direction. The scalar magnitude alone (climate speed) can be used separately from the vector quantity to quantify the high-level degree of disturbance to ecosystems18,34,79,80. This degree of disturbance is the quantity of interest for our research questions, and we use the climate speed exclusively in our analysis. We provide climate velocity vector maps for additional context (Supplementary Fig. 11), however, we caution readers that local analysis of these vectors would require a much finer-resolution dataset to better capture spatial gradients15,16,40,80.

Following standard methods, we calculate the temporal gradient of temperature using linear regression and the spatial gradient of temperature using the 3×3 neighborhood slope algorithm15,16,18,34. In the accompanying Python code (fun_calc_var.py), we implement the Sobel operator (mathematically equivalent to the 3×3 neighborhood slope algorithm) to obtain the spatial gradient of temperature. We calculate both our temporal gradient and spatial gradient directly from each dataset. At each point for each ensemble member, we divide the local 20-year temporal gradient (10-year for UKESM1 only, see below) by the spatial gradient and take the vector magnitude to obtain the climate speed. We impose a sign on the climate speed to denote whether it is associated with a warming or cooling trend. For all figures, we take the ensemble mean after the calculation of the climate speed. Climate speeds may be overestimated around complex topography in datasets with coarse spatial resolution15. Our results are robust to the choice of spatial resolution at the scale of all datasets used in this work (demonstrated for ERA5 in Supplementary Fig. 10).

By convention, climate velocities and climate speeds are assessed over time periods of 10 years or longer15,16,18. We calculate the climate speed over time periods chosen to be relevant to each scenario. For the scenarios with SAI in CESM2, this is the 20-year period immediately following deployment: 2035–2054 in ARISE-1.5, and 2045–2064 in ARISE-DelayedStart. 20-year timespans encompass the entire period when global mean temperature is decreasing in ARISE-DelayedStart. For no-SAI SSP2-4.5, we use the period 2045–2064 to compare to results from ARISE-DelayedStart. The time period 2035–2054 (corresponding to ARISE-1.5) is very similar in CESM2 simulations of SSP2-4.5, as the rate of change in global mean temperature does not alter substantially between 2035 and 2064. The time period spanning the interval when global mean temperature is decreasing in an SAI scenario is model-dependent and needs to be adjusted to correspond to the model that generated a given dataset. In UKESM1, a 10-year period fully encompasses the cooling after deployment due to its high aerosol sensitivity57,81. Thus, we calculate climate speeds over 10-year periods for output from UKESM1 as opposed to 20-year periods for all other products on Fig. 4. Using 20-year periods for UKESM1 would artificially reduce the climate speeds during its deployment shock by spreading the cooling out past the time horizon when global mean temperature has stabilized. In contrast, using the shorter 10-year periods for CESM2 would overlook the substantial cooling that continues past this horizon (Fig. 2).

The 10-member ensemble size of ARISE-1.5, ARISE-DelayedStart, and SSP2-4.5 enlarges the number of years available for analysis over each 20-year period to an effective size of 200 years50. Since the Last Millennium has only one ensemble member but a 1000-year simulation period, we choose ten 20-year time periods to avoid large volcanic eruptions (10 teragrams of stratospheric sulfate injection31): years 851–870, 871–890, 891–910, 911–930, 945–964, 971–990, 991–1010, 1011–1030, 1031–1050, 1051–1070. Avoiding such eruptions is necessary due to their large, but brief, impacts on global climate, which violate the linear trend assumptions underlying the definition of the climate velocity15. We additionally calculate the full distribution of non-overlapping 20-year climate speeds (again omitting large volcanic eruptions) in the Last Millennium for use in Fig. 4. Similarly, we choose ten 20-year time periods (years 5–24, 43–62, 95–114, 124–143, 164–183, 259–278, 280–299, 336–355, 379–398, 465–484) from the Unforced simulation to obtain a comparable sample size of 200 years, where relevant.

Peer review rounds
[{"round": 1, "reviewer_comments": {"reviewer_1": "This is an interesfing and important paper. The result that delaying implementafion of SAI has great\nassociated ecological risks is striking. The work is done very well, to the extent that I am able to assess.\nThe quesfion is of very broad general interest and is of value for ecologists, policy makers, environmental\nand conservafion scienfists, and to the general public.\nAbstract:\nClimate scienfists call model runs with different parameters “experiments.” Ecologists, biologists more\ngenerally, and the public think of “experiments” as actual manipulafions. This arficle will be likely to be\nread by a broader audience than just climate scienfists. Given the fraught and contenfious nature of the\ntopic of climate intervenfion, it might be prudent to use a different word in the Abstract, and clarify how\nthe term is being used in the body of the arficle, so that no one mistakes the work reported here as\nactually implemenfing SAI in the stratosphere.\nThe sentence that “climate speeds…are indisfinguishable” depends on the scenario, but also depends on\nthe resolufion of the model. It may be true at some spafial scales for some scenarios (and models) for\ntemperature, but it is definitely not true for precipitafion and other aspects of climate, so while this\nstatement is technically correct as stated, it is potenfially misleading. One of the challenging things about\nboth climate change itself and climate intervenfion for ecological systems are the creafion of novel\nclimate environments, where the combinafions of climate components, yearly temporality, seasonality,\nand other aspects of climate are put together in novel ways. For example, apparently the SAI scenarios\nyou are looking at might push global climates into a permanent and extreme La Nina state (from recent\nwork presented at the 2023 AGU meefing). Who knows if that is correct, but stafing that the earlier SAI\nintervenfion is indisfinguishable from preindustrial climates really applies only to one scenario at one\n(prefty large) spafial scale and only for average annual temperature, and for ecological systems, that\nstatement is misleading. Clearly the authors recognize the complexity of the changes that would occur\nwith SAI and acknowledge it in the first couple of paragraphs of the introducfion, but I think the\nencouraging result that annual average temperatures are restored if SAI is implemented sooner can be\nstated without being unintenfionally implying something overly opfimisfic.\nBecause many readers are likely to summarily dismiss SAI, I have a few suggesfions about things to add\nto highlight why SAI is really important to understand and consider. You might want to add a sentence or\ntwo to remind readers that planfing large numbers of trees to sequester carbon is at best controversial,\nand at worst is not going to stave off climate change, while leading to many other (ecological and other)\nproblems. I would also add a sentence to the effect that while scienfists are working on carbon dioxide\nremoval, and while it may one day prove to be an effecfive strategy for carbon reducfion and climate\ncooling, we are very far from that as a viable solufion now. That is, SAI has become a potenfial reality, so\nwe had befter take it seriously and understand its potenfial effects.\nText:\nIt is my understanding that delaying implementafion also requires a larger SO2 injecfion for the same\ncooling, and thus also has other addifional risks to perturbing the climate system, e.g., ozone/UV\nchanges (the more SO2, the more these other perturbafions). Not sure if that is what happens in the\ndelayed scenario.\nLine 74: Also, as for likely fime needed before implementafion, we need fime for invesfigafing the\necological and other consequences of SAI (including unintended consequences and risks), parficularly\nwith downscaling, and that means having fime for funding agencies to start to recognize the imperafive\nfor funding research in this field. This has not yet happened at anything like scale (just a personal grumpy\ncomment, not needed for the text or to be addressed by the authors).\nFigure 1: Could these be complemented by addifional maps showing vectors rather than colors? The\ncolors are appropriately alarming for speed, but they of course do not indicate direcfion.\nIt would also be informafive in the supplemental informafion to have maps with differences between the\ntwo ARISE implementafions (2035 and delayed start).\nLine 86-88: “…where spafial gradients are weak” is a really important point, and I get it, but it might be\nmissed by general readers who have not been exposed to the climate velocity literature. Please spell this\nout more in an addifional 1-2 sentences, with examples (e.g. a tropical tree in the Amazon Basin would\nhave to move 15 km every year towards the equator to remain in the same temperature range). It is very\nsurprising, and odd, that these extreme values are not the case for the boreal coniferous forests, which\nfor large parts are really flat. Same, apparently, for the N.A. Great Plains and Eurasian steppe. The tundra\nis hit with warming, but not as much with cooling, except that liftle strip over the far northeastern\nSiberia. Why? I am puzzled and intrigued.\nAlso lines 86-88: Low connecfivity, to say the least! To me, this is too understated and compresses the\ninformafion so much that it is likely to be overlooked and below general readers’ recognifion. You might\npoint out that global megacifies, roads, and agriculture, not to menfion the Gulf of Mexico and the\nCaribbean for NA, and the Mediterranean for Europe, are some of the tangible examples of why low\nconnecfivity is an issue.\nIn Figure 3, the colors did not have any obvious meaning, and the yellow was very hard to see.\nOtherwise, it is a useful figure, although it would be less puzzling if it appeared after the explanafion of\nthe abbreviafions in the text rather than before those explanafions.\nThere are several ways for organisms and ecological systems (communifies, forest types, etc.) to\nsuccessfully respond to rapid climate change. Organisms can move, they can evolve, or they can employ\nplasfic responses. It would be valuable to menfion this in a short paragraph, because this is a paper\nfocused on ecology, while having liftle actual ecology in it other than climate. Bacteria, viruses, and other\norganisms with very rapid life histories (including pathogens) can certainly evolve in response to climate\nchange occurring over one or more decades. Insects can also probably evolve at those fime scales, and\nsome insects can move long distances quickly. Birds can move, at least some of them can, if the right\nhabitats are somewhere they can reach and are available. Mammals, not so much. Some weed species\ncan move quickly, but trees cannot (you might want to cite E.C. Pielou’s classic work documenfing tree\nmovement following the North American glacial retreat after the last Ice Age—it took a long fime, and\nwas very different for different species). Many plants and some mammals have considerable plasficity,\nbut perhaps not to sudden cold. I have no idea what happens to marine invertebrates, fish,\nphytoplankton, mammals, and others (there is a menfion of that in the text, but not organism specific).\nBecause this paper is clearly aimed at ecologists and at a broader public, it would be valuable to give this\nbroader context to those who have not been thinking about such scenarios.\nI wish to sign my report.\nJessica Gurevitch, Purdue University", "reviewer_2": "This arficle discusses and expands upon the quesfion of climate velocifies in the context of climate\nintervenfion, comparing a set of datasets with different starfing dates and cooling profiles, and\ncontrasfing the resulfing regional climate velocifies with those from climate change in the SSP2-4.5\nscenario and in the recent past. I’ll start by saying that the arficle is excellent: beaufifully wriften, of high\nquality, and presenfing novel and interesfing analyses for one of the foremost issues in climate science in\nthis decade. So I wholeheartedly endorse publicafion in Nature Communicafion. However, I think the\nanalyses need to be complemented and expanded as I detail below before publicafion. I understand this\nwill add work for the authors, and I am usually weary of asking more during review, but this paper has\nthe potenfial to be a highly cited, essenfial part of the literature around SAI, and therefore I think the\nanalyses need to be impeccable and include more than what has been included right now (especially for\na NatComm study!).\nOne stylisfic comment: Figure 1 is very hard to read, and should be greatly expanded. I can barely read\nthe numbers on the colorbar even with zooming. Same for the supplementary figures with maps. Almost\nimpossible to read…\nFor a reader not familiar with ARISE and SAI, imagining the difference between the various\nscenarios is hard. Yes, there’s Figure 1 in the supplementary, but who reads that? My suggesfion is to incorporate Fig\n1 in the supplementary as the first panel of Fig. 1 in the main text (or on the left of it), so as to give even\na cursory reader the proper context.\nOne minor comment: at line 262-263, the authors discuss the aility of return to lower temperature\ntargets. I think the authors should cite this recent study (Visioni et al., 2023)\nhftps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003851 that shows results for exactly lower\ntemperature targets. Considering these simulafions also are performed with CESM2, they might even\nconsider adding them to their Fig. 3, but it’s not super necessary.\nNow to the main issue:\nI have an issue with using “preindustrial climate” in this context, which is highlighted by phrases such as\n“Preindustrial climate speeds (Fig. 1cd) are small, reflecfing the absence of external forcings in this\nsimulafion.”. This is true, but a reader should be disabused of the nofion that this is what any “real”\nclimate looks like, and then hence the climate velocifies there are in any way what a species might\nexpect from before 1850. PI is, indeed, a climate with no forcings at all. But regionally, in the last 2000\nyears, there have been other changes due to a variety of factors (some anthropics, some natural, such as\none or more large volcanic erupfions) that have always to a point affected climate. Hence at the very\nbeginning I would at least be more explicit that the PI climate considered here is nothing but the best\nguess of CESM2 of what the internal variability of the climate looks like with no forcing. Another thing I\nthink the authors should do is to integrate another dataset here, such as those provided by the pages 2k\nconsorfium (hftps://www.nature.com/arficles/sdata201788) and repeat the same analyses they have\nperformed over PI simulafions also for temperature reconstrucfions of the last 2000 years. Only then will\nphrases like “Much more ocean area is exposed to climate speeds of 2 km/yr (48%) and 5 km/yr (17%) in\nARISE-1.5 than Preindustrial (16% and 4%, respecfively). These values are within the observed mean\ndispersal rates of marine species (≈7 km/yr [3, 7])” make sense, scienfifically. This would greatly\ncomplement the analyses presented here and in Figure 3: their current dashed line in Figure 3 states that\n“Greater distance from the origin beyond this dofted line denotes a higher profile of ecological risk.”\nWhich again, it is based on the assumpfion that the PI period is what species were led to (or befter,\nevolved/adapted to) expect, and that suddenly in 1850 there was an abrupt shift. In order to make\nmeaningful assumpfions over things like that, one needs to also look at longer reconstrucfions (and not\nonly things like ERA5 as done there, but something like the dataset I provided above)."}, "author_response": "Response to reviewers for Hueholt et al. “Climate speeds help frame relative ecological risk in\nfuture climate change and stratospheric aerosol injection scenarios\" [Paper\nNCOMMS-23-51671-T]\nGeneral response\nWe are grateful to the reviewers for their thoughtful and constructive feedback, and were happy to\nsee that both saw the relevance of our work to a broad audience. We have thoroughly revised the\nmanuscript to address all comments. Changes in response to major comments include\nincorporating: a more appropriate preindustrial baseline in the CESM2 Last Millennium simulation\n(Reviewer 2), revising terminology for clarity to a broader audience (Reviewer 1), and adding\nfurther background information and analysis (Reviewers 1 & 2). Our detailed response to each\nreviewer comment is below. Changes to the manuscript can be viewed in the “tracked changes”\nversion included with the revised submission. We additionally include passages from the text in\nindividual comments as relevant.\nPoint-by-point response to Reviewer 1\nThis is an interesting and important paper. The result that delaying implementation of SAI has\ngreat associated ecological risks is striking. The work is done very well, to the extent that I am\nable to assess. The question is of very broad general interest and is of value for ecologists,\npolicy makers, environmental and conservation scientists, and to the general public.\nWe thank the reviewer for these kind remarks, and are glad to see they find our work to be broadly\nmeaningful.\nAbstract:\nClimate scientists call model runs with different parameters “experiments.” Ecologists,\nbiologists more generally, and the public think of “experiments” as actual manipulations. This\narticle will be likely to be read by a broader audience than just climate scientists. Given the\nfraught and contentious nature of the topic of climate intervention, it might be prudent to use a\ndifferent word in the Abstract, and clarify how the term is being used in the body of the article,\nso that no one mistakes the work reported here as actually implementing SAI in the\nstratosphere.\nWe have replaced “experiment” throughout the manuscript with terms such as “simulation” to\nemphasize these datasets are produced by numerical model runs with no real-world\nimplementation. For example, the second sentence of the abstract now reads: “Two recent Earth\nsystem model simulations depict policy-relevant SAI scenarios with similar temperature targets\n(near 1.5 ˚C) but with deployment delayed by 10 years between each.”\nThe sentence that “climate speeds…are indistinguishable” depends on the scenario, but also\ndepends on the resolution of the model. It may be true at some spatial scales for some\nscenarios (and models) for temperature, but it is definitely not true for precipitation and other\naspects of climate, so while this statement is technically correct as stated, it is potentially\nmisleading. One of the challenging things about both climate change itself and climate\nintervention for ecological systems are the creation of novel climate environments, where the\ncombinations of climate components, yearly temporality, seasonality, and other aspects of\nclimate are put together in novel ways. For example, apparently the SAI scenarios you are\nlooking at might push global climates into a permanent and extreme La Nina state (from recent\nwork presented at the 2023 AGU meeting). Who knows if that is correct, but stating that the\nPage 1 of 12\nearlier SAI intervention is indistinguishable from preindustrial climates really applies only to one\nscenario at one (pretty large) spatial scale and only for average annual temperature, and for\necological systems, that statement is misleading. Clearly the authors recognize the complexity\nof the changes that would occur with SAI and acknowledge it in the first couple of paragraphs of\nthe introduction, but I think the encouraging result that annual average temperatures are\nrestored if SAI is implemented sooner can be stated without being unintentionally implying\nsomething overly optimistic.\nWe agree that scenario- and model-dependence of results in SAI scenarios must be clear at all\ntimes. We have revised the abstract to clarify that the results in question are the planetary-scale\nclimate speeds, which are inherited from the differing forced global temperature trends in the\nmodel simulations. The new text reads: “On a planetary scale, climate speeds in the simulation\nwhere global temperature is maintained with SAI are not statistically distinguishable from those\nexperienced under preindustrial conditions.” To avoid adding jargon to the abstract, we refrain\nfrom stating the full name of each simulation and instead describe the different forced\ntemperature trends. This is why we state “the simulation where global temperature is maintained\nwith SAI” and “scenario of rapid temperature reduction” rather than writing “ARISE-1.5” and “ARISE-\nDelayedStart.”\nFor context, we note that the ENSO-locking results shown at AGU appear to be from simulations\nof marine cloud brightening (Xing et al. 2023). This is a different form of hypothetical climate\nintervention which is not discussed in our work and involves distinct processes from SAI.\nBecause many readers are likely to summarily dismiss SAI, I have a few suggestions about\nthings to add to highlight why SAI is really important to understand and consider. You might\nwant to add a sentence or two to remind readers that planting large numbers of trees to\nsequester carbon is at best controversial, and at worst is not going to stave off climate change,\nwhile leading to many other (ecological and other) problems. I would also add a sentence to the\neffect that while scientists are working on carbon dioxide removal, and while it may one day\nprove to be an effective strategy for carbon reduction and climate cooling, we are very far from\nthat as a viable solution now. That is, SAI has become a potential reality, so we had better take it\nseriously and understand its potential effects.\nWe have added text in Section 1 contrasting the difference in timescale between the potentially\nrapid changes in temperature possible with SAI and the slow socio-physical processes of carbon\nremoval. The passage now reads:\n“For example, SAI could be used to maintain global temperatures at or below some critical\nthreshold, or to rapidly reduce temperatures [8-12]. In contrast to carbon dioxide removal\ninterventions, which operate on slower timescales [13], solar radiation management\nmethods such as SAI currently represent the only known method to quickly alter global\nmean temperatures with near-future technology.”\nWhile we largely agree with the reviewer on the broader ecological, ethical, and governance issues\npertaining to carbon removal, we have concerns that including a brief summary of this topic\nwould risk coming across as superficial and may polarize readers. We feel a sufficiently nuanced\nand comprehensive discussion of these issues lies beyond the scope of this work.\nText:\nPage 2 of 12\nIt is my understanding that delaying implementation also requires a larger SO2 injection for the\nsame cooling, and thus also has other additional risks to perturbing the climate system, e.g.,\nozone/UV changes (the more SO2, the more these other perturbations). Not sure if that is what\nhappens in the delayed scenario.\nYes, a delayed start requires a larger sulfate burden than maintaining temperatures (MacMartin et\nal. 2022). In the specific case of ARISE-DelayedStart, this larger burden is due both to the later\ndeployment and the slightly lower temperature target than ARISE-1.5. We have added a sentence\nto the Online Methods to state this explicitly. This text reads: “ARISE-DelayedStart requires a larger\nstratospheric sulfate burden than ARISE-1.5 due both to the delayed start and the slightly lower\ntemperature target [11,12].”\nThe start time and aerosol burden interact within the Earth system in unintuitive ways. For\nexample, delaying deployment results in a smaller impact on stratospheric ozone despite the\nlarger sulfate burden—because the concentration of halogen species necessary for ozone\ndestruction is projected to decrease between 2035 and 2045 (MacMartin et al. 2022). We refer to\nthe papers documenting the ARISE-1.5 simulations (Richter et al. 2022) and ARISE-DelayedStart\nscenarios (Richter et al. 2022, MacMartin et al. 2022) with early simulations (MacMartin et al.\n2022) for more information regarding the physical climate response.\nLine 74: Also, as for likely time needed before implementation, we need time for investigating\nthe ecological and other consequences of SAI (including unintended consequences and risks),\nparticularly with downscaling, and that means having time for funding agencies to start to\nrecognize the imperative for funding research in this field. This has not yet happened at anything\nlike scale (just a personal grumpy comment, not needed for the text or to be addressed by the\nauthors).\nWe thank the reviewer for this commentary!\nFigure 1: Could these be complemented by additional maps showing vectors rather than colors?\nThe colors are appropriately alarming for speed, but they of course do not indicate direction.\nIn the course of developing this study, we did create vector plots of the climate velocity. When we\ndiscussed this work in early forms, however, most audiences found the vector plots confusing and\nchallenging to interpret. This was due to both the sheer quantity of vectors and the difficulty of\nvisually comparing vector features between global-scale figures. We reproduce an example of a\nvector plot at the native grid resolution for the CESM2-SSP2-4.5 simulation here.\nPage 3 of 12\n(2045-2064) SSP2-4.5 at 0.94x1.25˚\n+50\n+30\n+10\n+5\n+2\n-2\n-5\n-10\n-30\n-50\nVector visualizations are clearer if the dataset is coarsened—however, the climate velocity\ndirectionality is only as accurate as the modeled spatial gradient. The nominal 1x1˚ resolution in\nthe CESM2 SSP2-4.5 and ARISE simulations is already quite coarse with respect to ecosystems,\nor other important features such as orography or marine low cloud decks. Coarsening the data\nfurther dilutes these features. At 2.5x2.5˚ resolution the figures remain difficult to interpret, yet\nregions such as the Mediterranean Sea or Indonesia are visibly altered by the coarser grid. By\n5x5˚, the maps are more straightforward but phenomena as large as the North Atlantic warming\nhole have been compromised by the grid scale. We show examples of the CESM2-SSP2-4.5\nsimulation at 2.5x2.5˚ and 5x5˚ resolution below.\nFor completeness, we include vector maps below for SSP2-4.5, ARISE-DelayedStart, and\nARISE-1.5 below at 2.5x2.5˚ resolution.\nPage 4 of 12\n)ry/mk(\nerutarepmet\nm2\nfo\ndeeps\netamilC\n(2045-2064) SSP2-4.5 at 2.5x2.5˚ (2045-2064) SSP2-4.5 at 5x5˚\n-50\n-30 -10 -5 -2 +2 +5 +10 +30\n+50\nClimate speed of 2m temperature (km/yr)\n(2045-2064) SSP2-4.5 at 2.5x2.5˚\n(2045-2064) ARISE-DelayedStart at 2.5x2.5˚ (2035-2054) ARISE-1.5 at 2.5x2.5˚\nVector analyses of climate velocity are frequent in regional-scale research (e.g., Burrows et al.\n2011, 2014; Brito-Morales et al. 2018), and we imagine a future study using these methods in the\ncontext of SAI scenarios would yield interesting results. For the specific finding we wished to\nhighlight here—the relative risk between scenarios arising from the differing global temperature\ntrends—we felt the climate speeds stood better on their own. Thus, we continue to include only\nthe climate speed figures within the manuscript.\nIt would also be informative in the supplemental information to have maps with differences\nbetween the two ARISE implementations (2035 and delayed start).\nWe have added a difference plot of ARISE-DelayedStart and ARISE-1.5 as Supplementary Fig. 3.\nThis figure is now referenced in Section 2.1. The relevant text reads: \"During the 20-year period\nfollowing deployment, ARISE-DelayedStart depicts a forcing from climate speeds to global and\nregional ecosystems (Figure 1gh) that exceeds the corresponding time period in no-SAI SSP2-4.5\n(Figure 1ab), and draws a striking contrast to the small values under ARISE-1.5 (Supplementary\nFig. 3).”\nLine 86-88: “…where spatial gradients are weak” is a really important point, and I get it, but it\nmight be missed by general readers who have not been exposed to the climate velocity\nliterature. Please spell this out more in an additional 1-2 sentences, with examples (e.g. a\ntropical tree in the Amazon Basin would have to move 15 km every year towards the equator to\nremain in the same temperature range).\nWe have added text to Section 2.1 explicitly spelling out the implications of the relationship\nbetween climate speed and the spatial gradient of temperature. The new passage reads:\nPage 5 of 12\n“Very large climate speeds are projected to cause extreme ecosystem stress in tropical\nregions where spatial gradients are weak (Fig. 1ab) [6, 33]. For example, ensemble mean\nclimate speeds averaged over the Amazon region (as defined by IPCC Working Group 1\nFifth Assessment Report [34]) are 12 km/yr, suggesting that tropical terrestrial species\nwould need to move poleward or up topography by 240 km in order to remain in their\nstarting conditions over this 20-year period.”\nIt is very surprising, and odd, that these extreme values are not the case for the boreal\nconiferous forests, which for large parts are really flat. Same, apparently, for the N.A. Great\nPlains and Eurasian steppe. The tundra is hit with warming, but not as much with cooling, except\nthat little strip over the far northeastern Siberia. Why? I am puzzled and intrigued.\nWe agree these regional responses are puzzling and intriguing! Many of these features likely\noccur by chance due to stochastic climate variability opposing the regional forced temperature\ntrend. It seems plausible that certain high-latitude features could be forced by the response to the\npole-to-equator temperature gradient target in ARISE-DelayedStart—but this is purely speculation.\nSince our work is focused on global ecological impacts from the planetary-scale forcing, we do\nnot attempt to explain regional results in depth. We also believe regional analysis would require\nmuch greater ecological and biological expertise than we possess as climate scientists,\nparticularly given the limitations of climate velocity as a tool for projecting species-level impacts\n(e.g., Ettinger & HilleRisLambers 2013, Alabia et al. 2018). Further exploration of regional-scale\neffects of SAI on ecology, whether through climate speeds or other phenomena, is a clear avenue\nfor future work. We hope other researchers will build on our results in this way.\nAlso lines 86-88: Low connectivity, to say the least! To me, this is too understated and\ncompresses the information so much that it is likely to be overlooked and below general\nreaders’ recognition. You might point out that global megacities, roads, and agriculture, not to\nmention the Gulf of Mexico and the Caribbean for NA, and the Mediterranean for Europe, are\nsome of the tangible examples of why low connectivity is an issue.\nYes! Our team had struggled to decide on what level of detail to provide here, and we appreciate\nthe insight that this is a critical point. We have added text and references discussing connectivity\nin more depth. This new passage specifically includes the examples of both anthropogenic\nfragmentation and the Mediterranean Sea. The new text reads as follows:\n“Sharp topographic gradients buffer climate speeds and allow relict populations to shelter\nin microclimates, but these communities often have low connectivity [5, 35] and persistent\nwarming may render these niches inaccessible [5, 16, 35, 36]. Poor connectivity can occur\nelsewhere due to causes including fragmentation by human land use such as urbanization\n[37, 38], or natural barriers as in semi-enclosed marine basins like the Mediterranean Sea\n[39]. This fragmentation impedes the ability of many ecological communities to shift in\nresponse to climate changes and may increase population vulnerability [35, 37, 40].\"\nIn Figure 3, the colors did not have any obvious meaning, and the yellow was very hard to see.\nOtherwise, it is a useful figure, although it would be less puzzling if it appeared after the\nexplanation of the abbreviations in the text rather than before those explanations.\nThe yellow denoting CESM2-SSP1-2.6 on the figure (now Figure 4) has been changed to pink for\nbetter visibility. The colors are intended to help distinguish the dots representing different\ndatasets, particularly where the bars representing ensemble uncertainty overlap (e.g., as for\nPage 6 of 12\nCESM2-ARISE-1.0 and CESM2-ARISE-DelayedStart). We added text to the Figure 4 caption to\nclarify the colors distinguish differing simulations. This text reads: “Dots denote ensemble mean,\nand lines display the width of the ensemble variability. The colors of each dot help visually\ndistinguish datasets from each other.” We evaluated multiple other approaches (e.g., all dots the\nsame color and distinguished by the annotations, or using shades of black/gray rather than\ncolors) and found these to be much more confusing.\nWe have revised the text introducing the figure in Section 2.3 to set expectations for the upcoming\nsection, and provide the table reference for all datasets. The new passage reads:\n“Climate speeds of 10 km/yr provide a threshold of extreme risk by exceeding the\ndispersal rates of both adaptable families (such as mammals) and terrestrial and\nmarine species on average [7, 16, 18]. We plot the global area exposed to these climate\nspeeds against the annual rate of global temperature change for a wide range of datasets\nto efficiently summarize ecological risk (Fig. 4; see Table 1 in Online Methods for\ndetailed data descriptions), and subsequently describe the implications for each product\nin the context of this figure.”\nThere are several ways for organisms and ecological systems (communities, forest types, etc.)\nto successfully respond to rapid climate change. Organisms can move, they can evolve, or they\ncan employ plastic responses. It would be valuable to mention this in a short paragraph,\nbecause this is a paper focused on ecology, while having little actual ecology in it other than\nclimate. Bacteria, viruses, and other organisms with very rapid life histories (including\npathogens) can certainly evolve in response to climate change occurring over one or more\ndecades. Insects can also probably evolve at those time scales, and some insects can move\nlong distances quickly. Birds can move, at least some of them can, if the right habitats are\nsomewhere they can reach and are available. Mammals, not so much. Some weed species\ncan move quickly, but trees cannot (you might want to cite E.C. Pielou’s classic work\ndocumenting tree movement following the North American glacial retreat after the last Ice Age—it\ntook a long time, and was very different for different species). Many plants and some mammals\nhave considerable plasticity, but perhaps not to sudden cold. I have no idea what happens to\nmarine invertebrates, fish, phytoplankton, mammals, and others (there is a mention of that in the\ntext, but not organism specific). Because this paper is clearly aimed at ecologists and at a broader\npublic, it would be valuable to give this broader context to those who have not been thinking\nabout such scenarios.\nWe thank the reviewer for raising these points. We have added text and citations to Section 1\n(including the reference to Pielou 2008) providing more background information on how species\nrespond to rapid climate change. The text reads:\nSpecies have varying ability to shift range in response to climate change; on average,\nmarine organisms can move more quickly than terrestrial species, and trees have among\nthe slowest responses of all forms of life [7, 17, 18]. Climate impacts to ecology emerge\nfrom many sources beyond temperature, including changes in precipitation [14],\nbiogeochemistry [2, 15], or interactions among species [19, 20]. Species with very short\nlife histories (e.g., bacteria) can adapt to a changing climate through evolution, while more\ncomplex organisms may be able to employ behavioral adjustments [1, 14, 21, 22].\nPopulations unable to adapt or shift range at sufficient rates may be at risk of extirpation–\nwhich often takes place abruptly following subsequent extreme events rather than as a\nslow, linear process accompanying the climatic change [1, 23]. The climate velocity\nPage 7 of 12\nprovides a general metric for perturbations to large-scale ecology by the movement of\nthermal niches, rather than a tool to describe all types of impacts [15, 16, 19].\nWe acknowledge that this more granular information about biological responses to climate\nchange falls outside our area of formal training as climate scientists. If elements of our added\ntext are incorrect, we invite the reviewer to provide further context on these topics.\nI wish to sign my report.\nJessica Gurevitch, Purdue University\nWe thank Dr. Gurevitch for her insightful review.\nPage 8 of 12\nPoint-by-point response to Reviewer 2\nThis article discusses and expands upon the question of climate velocities in the context of\nclimate intervention, comparing a set of datasets with different starting dates and cooling\nprofiles, and contrasting the resulting regional climate velocities with those from climate change\nin the SSP2-4.5 scenario and in the recent past. I’ll start by saying that the article is excellent:\nbeautifully written, of high quality, and presenting novel and interesting analyses for one of the\nforemost issues in climate science in this decade. So I wholeheartedly endorse publication in\nNature Communication. However, I think the analyses need to be complemented and expanded\nas I detail below before publication. I understand this will add work for the authors, and I am\nusually weary of asking more during review, but this paper has the potential to be a highly cited,\nessential part of the literature around SAI, and therefore I think the analyses need to be\nimpeccable and include more than what has been included right now (especially for a NatComm\nstudy!).\nWe thank the reviewer for their thoughtful review and constructive comments. In response, we\nhave incorporated further data through the CESM2(WACCM6ma) Last Millennium simulation to\nprovide a more appropriate baseline of preindustrial climate speeds, changed our terminology\naround preindustrial conditions, and added further analysis in response to both their comments\nand those of Reviewer 1.\nOne stylistic comment: Figure 1 is very hard to read, and should be greatly expanded. I can\nbarely read the numbers on the colorbar even with zooming. Same for the supplementary\nfigures with maps. Almost impossible to read…\nWe have redesigned Figure 1 to improve readability, and increased panel and text size in the\nsupplementary figures as much as possible. With twenty individual maps for each scenario in\nSupplementary Figures 4-8, it is difficult to further increase the size without breaking these into an\nimpractical number of individual figures.\nFor a reader not familiar with ARISE and SAI, imagining the difference between the various\nscenarios is hard. Yes, there’s Figure 1 in the supplementary, but who reads that? My suggestion\nis to incorporate Fig 1 in the supplementary as the first panel of Fig. 1 in the main text (or on the\nleft of it), so as to give even a cursory reader the proper context.\nSupplementary Figure 1 has been added to the main text as Figure 2. We attempted to incorporate\nit into Figure 1, but this proved impractical while keeping each subfigure sufficiently large for\nreadability (see response to previous comment).\nOne minor comment: at line 262-263, the authors discuss the aility of return to lower\ntemperature targets. I think the authors should cite this recent study (Visioni et al., 2023)\nhttps://agupubs.onlinelibrary.wiley.com/doi/10.1029/2023EF003851 that shows results for\nexactly lower temperature targets. Considering these simulations also are performed with\nCESM2, they might even consider adding them to their Fig. 3, but it’s not super necessary.\nWe have added a reference to Visioni et al. 2023 to Section 2.3 as part of the discussion of design\nchoices that may produce deployment shock. The text reads: “[…] the intervention could be\ndeployed with the explicit goal of obtaining a low temperature target below the starting global\nmean value (CESM2-ARISE-1.0 [11, 12], or the simulations of [64] [not shown]).”\nPage 9 of 12\nWe are reluctant to add more simulations in Figure 3 given the large number of datasets already\npresent on the figure. Additionally, two of the three temperature targets in Visioni et al. 2023 are\npresently included in Fig. 3: their 1.5˚C target matches ARISE-1.5, and the 1.0˚C target ARISE-1.0.\nNow to the main issue:\nI have an issue with using “preindustrial climate” in this context, which is highlighted by phrases\nsuch as “Preindustrial climate speeds (Fig. 1cd) are small, reflecting the absence of external\nforcings in this simulation.”. This is true, but a reader should be disabused of the notion that this\nis what any “real” climate looks like, and then hence the climate velocities there are in any way\nwhat a species might expect from before 1850. PI is, indeed, a climate with no forcings at all.\nBut regionally, in the last 2000 years, there have been other changes due to a variety of factors\n(some anthropics, some natural, such as one or more large volcanic eruptions) that have always\nto a point affected climate. Hence at the very beginning I would at least be more explicit that the\nPI climate considered here is nothing but the best guess of CESM2 of what the internal\nvariability of the climate looks like with no forcing. Another thing I think the authors should do is\nto integrate another dataset here, such as those provided by the pages 2k consortium (https://\nwww.nature.com/articles/sdata201788) and repeat the same analyses they have performed\nover PI simulations also for temperature reconstructions of the last 2000 years. Only then will\nphrases like “Much more ocean area is exposed to climate speeds of 2 km/yr (48%) and 5 km/yr\n(17%) in ARISE-1.5 than Preindustrial (16% and 4%, respectively). These values are within the\nobserved mean dispersal rates of marine species (≈7 km/yr [3, 7])” make sense, scientifically.\nThis would greatly complement the analyses presented here and in Figure 3: their current\ndashed line in Figure 3 states that “Greater distance from the origin beyond this dotted line\ndenotes a higher profile of ecological risk.” Which again, it is based on the assumption that the\nPI period is what species were led to (or better, evolved/adapted to) expect, and that suddenly in\n1850 there was an abrupt shift. In order to make meaningful assumptions over things like that, one\nneeds to also look at longer reconstructions (and not only things like ERA5 as done there, but\nsomething like the dataset I provided above).\nWe thank the reviewer for raising this critical distinction between preindustrial and unforced\nclimates, and agree the unforced preindustrial control is structurally different than the true climate\nthat ecosystems experienced in the past. Thus, we have replaced the previous unforced\npreindustrial control simulation throughout the manuscript with the CESM2(WACCM6ma) Last\nMillennium dataset (Otto-Bliesner et al. 2023). The Last Millennium simulation uses boundary\nconditions from paleoclimate proxies to represent the conditions of the last 1000 years, an\napproach philosophically similar to conventional simulations of the historical period. This\nmethodology is extensively validated as a Tier 1 experiment (“past1000”) of the Paleoclimate\nModelling Intercomparison Project (Jungclaus et al. 2017).\nCESM2(WACCM6ma) denotes a “middle atmosphere” configuration of CESM2(WACCM6) with\nsimplified chemistry. Aside from tropospheric chemistry, its representation of global climate is\nvery similar to CESM2(WACCM6) (Davis et al. 2023). The Last Millennium simulation was recently\ncompleted and has not yet been documented in an overview publication. However, its immediate\npredecessor—the CESM1(CAM) Last Millennium Ensemble—successfully reproduced known\nregional and global features of the paleoclimate record (Otto-Bliesner et al. 2016). Hence, this\nsimulation provides both an ecologically-relevant baseline of preindustrial climate and an\nappropriate comparison to the CESM2(WACCM6) simulations of future scenarios (SSP2-4.5,\nARISE-1.5, ARISE-DelayedStart). We omitted 5 year periods following strong volcanic eruptions\n(>10 teragrams of stratospheric sulfate injection, Jungclaus et al. 2017). Volcanoes strongly\nPage 10 of 12\naffect global climate during the Last Millennium, but their perturbations are nonlinear and only\nlast for a few years. Climate velocities are not appropriate for analysis on this timescale due to\ntheir definition as a linear regression (e.g., Brito-Morales et al. 2018). In contrast, the SAI\nsimulations we explore feature rapid climate change which is sustained for decades.\nOur results with the Last Millennium simulation were qualitatively similar to the original findings\nwith the unforced preindustrial control. In replacing the simulations, we revised a large amount of\ntext throughout Section 1, 2, and the Online Methods as shown in the Tracked Changes file. We\nsummarize the principle results in the response here. Climate speeds were generally small over\nthe average of ten 20-year periods in the Last Millennium (Section 2.1). The distribution of values\nin ARISE-1.5 and the Last Millennium were not statistically distinguishable (Section 2.2). The Last\nMillennium baseline for ecological risk in Figure 4 is 10% of global area exposed to climate\nspeeds greater than 10 km/yr, similar to the 11% found in the unforced preindustrial control\nsimulation (Section 2.3).\nThe unforced preindustrial control is now mentioned in only a few places in our revised\nmanuscript. We include one sentence in Section 2.1 noting the results are qualitatively similar in\nboth the Last Millennium and the unforced simulation. The sentence reads: “A purely unforced\nsimulation with boundary conditions fixed at 1850 (Supplementary Fig. 2) produces qualitatively\nsimilar results [to the Last Millennium].” The finding that small climate speeds in the unforced\npreindustrial control provides confidence in the model’s adequacy for our analysis is moved to the\nOnline Methods.\nWe extensively assessed the literature to find a long-record dataset with full-globe gridded\ncoverage (necessary for the calculation of climate velocities) that provided both a direct\ncomparison to CESM2 simulations and a meaningful baseline for ecology. Unfortunately, the\nPages2k datasets are sparse in space and time; thus, it is not possible to use them to calculate\nclimate velocities. Global paleo-reanalyses and previous paleoclimate simulations (see recent\nreview by Smerdon et al. 2023) provide an appropriate ecological baseline, but would have\nsubstantial structural differences from CESM2(WACCM6). While the CESM2(WACCM6ma) Last\nMillennium simulation covers the past 1000 years rather than 2000 years as the reviewer\noriginally suggested, we feel this still provides a sufficient baseline for both ecology and climate in\nthe presence of realistic forcings and internal climate variability.\nPage 11 of 12\nWorks Cited\nAlabia, I. D., García Molinos, J., Saitoh, S. I., Hirawake, T., Hirata, T., & Mueter, F. J. (2018).\nDistribution shifts of marine taxa in the Pacific Arctic under contemporary climate changes.\nDiversity and Distributions, 24(11), 1583-1597.\nBrito-Morales, I., Molinos, J. G., Schoeman, D. S., Burrows, M. T., Poloczanska, E. S., Brown, C.\nJ., ... & Richardson, A. J. (2018). Climate velocity can inform conservation in a warming world.\nTrends in ecology & evolution, 33(6), 441-457.\nBurrows, M. T., Schoeman, D. S., Buckley, L. B., Moore, P., Poloczanska, E. S., Brander, K. M., ... &\nRichardson, A. J. (2011). The pace of shifting climate in marine and terrestrial ecosystems.\nScience, 334(6056), 652-655.\nBurrows, M. T., Schoeman, D. S., Richardson, A. J., Molinos, J. G., Hoffmann, A., Buckley, L. B., ...\n& Poloczanska, E. S. (2014). Geographical limits to species-range shifts are suggested by\nclimate velocity. Nature, 507(7493), 492-495.\nDavis, N. A., Visioni, D., Garcia, R. R., Kinnison, D. E., Marsh, D. R., Mills, M., ... & Vitt, F. (2023).\nClimate, variability, and climate sensitivity of “Middle atmosphere” chemistry configurations of\nthe community Earth system model version 2, whole atmosphere community climate model\nversion 6 (CESM2 (WACCM6)). Journal of Advances in Modeling Earth Systems, 15(9),\ne2022MS003579.\nEttinger, A. K., & HilleRisLambers, J. (2013). Climate isn't everything: competitive interactions\nand variation by life stage will also affect range shifts in a warming world. American Journal of\nBotany, 100(7), 1344-1355.\nJungclaus, J. H., Bard, E., Baroni, M., Braconnot, P., Cao, J., Chini, L. P., ... & Zorita, E. (2017). The\nPMIP4 contribution to CMIP6–Part 3: The last millennium, scientific objective, and experimental\ndesign for the PMIP4 past1000 simulations. Geoscientific Model Development, 10(11),\n4005-4033.\nMacMartin, D. G., Visioni, D., Kravitz, B., Richter, J. H., Felgenhauer, T., Lee, W. R., ... & Sugiyama,\nM. (2022). Scenarios for modeling solar radiation modification. Proceedings of the National\nAcademy of Sciences, 119(33), e2202230119.\nOtto-Bliesner, B. et al. CESM2-WACCM6ma Last Millennium (2023). Dataset. doi.org/\n10.26024/5dgt-qf16\nOtto-Bliesner, B. L., Brady, E. C., Fasullo, J., Jahn, A., Landrum, L., Stevenson, S., ... & Strand, G.\n(2016). Climate variability and change since 850 CE: An ensemble approach with the\nCommunity Earth System Model. Bulletin of the American Meteorological Society, 97(5),\n735-754.\nRichter, J. H., Visioni, D., MacMartin, D. G., Bailey, D. A., Rosenbloom, N., Dobbins, B., ... &\nLamarque, J. F. (2022). Assessing Responses and Impacts of Solar climate intervention on the\nEarth system with stratospheric aerosol injection (ARISE-SAI): Protocol and initial results from\nthe first simulations. Geoscientific Model Development, 15(22), 8221-8243.\nSmerdon, J. E., Cook, E. R., & Steiger, N. J. (2023). The Historical Development of Large‐Scale\nPaleoclimate Field Reconstructions Over the Common Era. Reviews of Geophysics, 61(4),\ne2022RG000782.\nXing, C., Stevenson, S., Fasullo, J., Harrison, C. S., Chen, C. C., Wan, J., ... & Pfleger, C. (2023).\nWhy does Marine Cloud Brightening shut down El Niño–Southern Oscillation?. AGU23.\nPage 12 of 12"}, {"round": 2, "reviewer_comments": {"reviewer_1": "The authors have addressed all of my comments safisfactorily and I am pleased to recommend this\noutstanding and important paper. I thought the figures with the actual vectors were very informafive,\nand would like to see at least the ones in the response to reviews incorporated into the supplementary\ninformafion if possible. The comments by the second reviewer (which I was privileged to see) were\ninteresfing; I have never seen an ecological climate change paper that set the baseline at the past\nmillenium--interesfing and makes some sense, perhaps, but not the norm.", "reviewer_2": "The authors have done a great job with the revision. I am grateful they took my suggesfion and included\nthe Last Millennium results, and think this greatly strengthened the manuscript. I also like how the\nfigures have been revised.\nAs I already noted before, the manuscript is excellently wriften and this hasn't changed in the revision,\nso I have no further comments. Congratulafions on a really, really good and useful paper.\nBest,\nDaniele Visioni"}, "author_response": "Response to reviewers for Hueholt et al. “Speed of environmental change frames relative\necological risk in climate change and climate intervention scenarios” [Paper\nNCOMMS-23-51671-T]\n(Previously titled: “Climate speeds help frame relative ecological risk in future climate change\nand stratospheric aerosol injection scenarios”)\nGeneral response\nWe thank both reviewers for their thoughtful comments during the review process and kind\nremarks on the revised manuscript. We briefly address their final points below.\nPoint-by-point response to Reviewer 1\nThe authors have addressed all of my comments satisfactorily and I am pleased to recommend\nthis outstanding and important paper. I thought the figures with the actual vectors were very\ninformative, and would like to see at least the ones in the response to reviews incorporated into\nthe supplementary information if possible. The comments by the second reviewer (which I was\nprivileged to see) were interesting; I have never seen an ecological climate change paper that\nset the baseline at the past millenium--interesting and makes some sense, perhaps, but not the\nnorm.\nWe now include the vector maps as Supplementary Fig. 11. We revised Section 4.2 to introduce\nthis figure: “The climate velocity is a vector quantity, with both a magnitude and a direction. The\nscalar magnitude alone (climate speed) can be used separately from the vector quantity to\nquantify the high-level degree of disturbance to ecosystems [18, 34, 79, 80]. This degree of\ndisturbance is the quantity of interest for our research questions, and we use the climate speed\nexclusively in our analysis. We provide climate velocity vector maps for additional context\n(Supplementary Fig. 11), however, we caution readers that local analysis of these vectors would\nrequire a much finer-resolution dataset to better capture spatial gradients [15, 16, 40, 80].”\nPoint-by-point response to Reviewer 2\nThe authors have done a great job with the revision. I am grateful they took my suggestion and\nincluded the Last Millennium results, and think this greatly strengthened the manuscript. I also\nlike how the figures have been revised.\nAs I already noted before, the manuscript is excellently written and this hasn't changed in the\nrevision, so I have no further comments. Congratulations on a really, really good and useful\npaper.\nBest,\nDaniele Visioni\nWe are happy to see that the reviewer likes our redesigned figures and approves of the inclusion\nof the Last Millennium—we agree this strengthened our analysis.\nPage 1 of 1"}]

Decision letter