United States 

Department of 

Agriculture

Forest Service

Pacific Southwest 

Research Station

Research Paper

PSW-RP-262

July 2011

A Climate Change Primer for 

Land Managers: An Example 

From the Sierra Nevada

Toni Lyn Morelli, Maureen C. McGlinchy,

and Ronald P. Neilson
The Forest Service of the U.S. Department of Agriculture is dedicated to the principle of 

multiple use management of the Nation’s forest resources for sustained yields of wood, 

water, forage, wildlife, and recreation. Through forestry research, cooperation with the 

States and private forest owners, and management of the National Forests and National 

Grasslands, it strives—as directed by Congress—to provide increasingly greater service to a 

growing Nation.

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Authors

Toni Lyn Morelli is a research ecologist, U.S. Department of Agriculture,

Forest Service, Pacific Southwest Research Station, 800 Buchanan St., Albany, CA 

94710; Maureen C. McGlinchy is a master’s candidate, Oregon State University, 

Corvallis, OR 97331; Ronald P. Neilson is a bioclimatologist (retired), U.S. Department of Agriculture, Forest Service, Pacific Northwest Research Station, 3200 SW 

Jefferson Way, Corvallis, OR 97331. Morelli currently is located at the University of 

California at Berkeley, Berkeley, CA 94720.

Cover photo by Toni Lyn Morelli
Abstract

Morelli, Toni Lyn; McGlinchy, Maureen C.; Neilson, Ronald P. 2011. A climate 

change primer for land managers: an example from the Sierra Nevada. Res. Pap. 

PSW-RP-262. Albany, CA: U.S. Department of Agriculture, Forest Service, 

Pacific Southwest Research Station. 44 p.

Interest in location-specific climate projections is growing. To facilitate the communication of these data, we provide here an example for how to present climate 

information relevant at the scale of a national forest. We summarize some of the 

latest data on climate change projections and impacts relevant to eastern California, 

from the global scale to the state level, then focus on the Sierra Nevada, and finally 

the Inyo National Forest. Most climate models project increased temperatures and 

reduced snow cover across most of the Sierra Nevada, potentially causing water 

shortages and cascading ecological impacts. For the Inyo National Forest, we 

provide MC1 Dynamic General Vegetation Model outputs projecting a reduction in 

the extent of alpine/subalpine ecosystems, an increase of woodlands and grasslands, 

and an emergence of novel desert habitat in eastern Sierra Nevada by the end of the 

21st century. Finally, we offer resources and possible alternatives to land managers 

for climate change adaptation. Thus, our study provides climate change information 

for a specific management unit in the West as well as an example for other regions. 

We used this information as background for a climate-adaptation workshop held 

in Bishop, California, in 2009. This workshop was part of a Westwide Climate 

Initiative project to work with land managers to develop climate change adaptation 

options.

Keywords: Adaptation, California, Inyo National Forest, land management, 

management tool. 
Summary 

The objective of this paper is to present a template for conducting a review of the 

latest climate research for a particular management unit. As an example, background material is provided from a recent climate change adaptation workshop 

held in Bishop, California, including some of the latest climate change projections 

relevant to the eastern Sierra Nevada, as well as a glossary of terms. This work was 

part of the Westwide Climate Initiative (WWCI)’s Toolkit for Adapting to Climate 

Change on Western National Forests.

Globally, the average, minimum, and maximum temperatures are rising as 

a result of human-induced increases in greenhouse gases. Other global physical 

effects of anthropogenic climate warming include the melting of arctic sea ice and 

land-based ice bodies, thawing of the permafrost, global sea level rise, earlier spring 

runoff, and more frequent and more extreme weather events. Biologically, species 

ranges are shifting poleward and upward, and phenology is changing. In California, 

rising sea levels, increasing average and extreme temperatures, changing precipitation regimes, and more frequent and severe wildfires are also projected. Scientists 

have already observed an increase in rain versus snow, earlier snowfed streamflow, 

and earlier budbreak. In the Sierra Nevada, winter temperatures are projected to 

increase the most. Changes in snowfall and snowmelt in the Sierra Nevada could 

cause water shortages throughout the state, as the Sierra Nevada is the source of 

most of California’s water supply in the dry season. Climate change could have 

cascading effects across the Sierra Nevada, starting with physical changes and 

working its way through food webs, as in Lake Tahoe. Climate change effects on 

wildlife and vegetation will be complex and are not yet understood.

We also present specific modeled projections for the Inyo National Forest. We 

selected three global climate models (GCMs; HADLEY CM3, MIROC3.2-medres, 

and CSIRO-Mk3.0) in combination with three carbon emissions scenarios (A2, 

A1B, and B2) to run the dynamic general vegetation model MC1 over the next 

century for the Inyo National Forest. This model projected between 2.5 and 10 

°C temperature warming by 2100. Precipitation was not consistent among models 

except for an overall decrease in annual snowpack and an increase in precipitation, primarily falling as rain, projected along the Sierra Crest just west of Mono 

Lake. The models also indicate a longer and more severe fire season for the Inyo 

National Forest. Overall, there is considerable agreement between the two GCMs 

(MIROC3.2-medres and CSIRO-Mk3.0) used for the MC1 vegetation projections. 
They both project an increase in grassland and woodland, a decrease in shrubland, 

a reduction of subalpine forest, a severe loss of tundra habitat, and the emergence 

of a novel habitat, desert vegetation, for the Inyo National Forest. Although there 

is uncertainty in the MC1 model projections, our results resemble similar analyses 

of the Sierra Nevada and provide a general framework upon which to base management decisions. Finally, we review adaptation options for decisionmakers and 

provide a list of key electronic climate change adaptation resources.
Contents

1 Introduction

2 Global Trends

4 California’s Changing Climate

8 Climate Trends in the Sierra Nevada

11 Ecological Effects of Changing Climate in the Sierra Nevada

13 Climate Projections for Inyo National Forest

19 Adaptation

23 Acknowledgments

24 English Equivalents

24 References

34 Glossary

39 Appendix
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A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

Introduction

A climate change adaptation workshop, “Evaluating Change in the Eastern Sierra,” 

was held on September 22–23, 2009, in Bishop, California, (www.fs.fed.us/psw/

topics/climate_change/wwci_toolkit/bishop2009/). The audience was a mix of 

federal, university, and other scientists; resource specialists; and concerned citizens. 

Although some knowledge of climate change was expected, we developed a summary, presented here, to provide further understanding.

For the background material, we synthesized some of the latest climate change 

projections relevant to the eastern Sierra Nevada, and specifically the Inyo National 

Forest. In reproducing it here, we hope to provide not only these specific data but an

example for other researchers and land managers to use when communicating summaries of climate change information. For other areas in the Sierra Nevada, the last 

two sections could be replaced by more locally appropriate results. Likewise, for 

areas in other parts of the country, a similar format could be followed while inserting more relevant information after the “Global Trends” section. As newer information becomes available (e.g., the Fifth Assessment Report of the Intergovernmental 

Panel on Climate Change [IPCC]), it could be used to modify the “Global Trends” 

section.

The Inyo National Forest section delineates climate projections and impacts for 

one particular management unit. These data were produced by Maureen McGlinchy 

and Ron Neilson from the MAPSS (Mapped Atmosphere-Plant-Soil System) 

research group, based at the U.S. Forest Service Pacific Northwest Research Station 

Forestry Sciences Laboratory in Corvallis, Oregon, and associated with the Department of Forest Ecosystems and Society at Oregon State University. The MAPSS 

research group is available to produce similar projections for other locations and 

would be grateful for feedback about needs and use of their projections.

The Bishop workshop was part of the Westwide Climate Initiative’s (WWCI) 

Toolkit for Adapting to Climate Change on Western National Forests. The WWCI 

(http://www.fs.fed.us/psw/topics/climate_change/wwci_toolkit/) is composed of 

researchers from the three western USDA Forest Service research stations—Pacific 

Southwest, Pacific Northwest, and Rocky Mountain—as well as collaborators from 

Forest Service western regional offices, other regions, and other federal agencies. 

The WWCI’s purpose is to act as a bridge between scientists and land managers 

when dealing with climate change issues.
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RESEARCH PAPER PSW-RP-262

Global Trends

“Observed increases in global average air and ocean temperatures, widespread 

melting of snow and ice, and rising global sea level provide unequivocal evidence 

that the earth’s climate system is warming” (Mazur and Milanes 2009: 1). Since 

1906, the average global temperature increased nearly 0.8 °C, rising much faster

in the second half of the century than in the first (Parry 2007b). There has been

on average a rapid increase in daily minimum and maximum temperatures and a 

sharp decline in the number of frost days globally (Bonfils et al. 2008). Furthermore, 1995 through 2006 contained 11 of the 12 hottest years on record globally 

(Trenberth et al. 2007). Concurrently, atmospheric greenhouse gas (GHG) concentrations, including carbon dioxide (CO2

), have risen steeply, and the rate of increase 

is projected to continue rising under most emissions scenarios (see figs. 1 and 2).

Figure 1—Summary of characteristics of the four Special Report on Emission Scenarios storylines 

(From Parry et al. 2007b, fig. TS.2).
3

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

Figure 2—Scenarios for greenhouse gas (GHG) emissions from 2000 to 2100 in the absence of 

additional climate policies. Global GHG emissions (in Gt CO2-eq/year) in the absence of additional 

climate policies: six illustrative Special Report on Emission Scenarios (SRES) marker scenarios 

(colored lines) and 80th percentile range of recent scenarios published since SRES (post-SRES) (gray 

shaded area). Dashed lines show the full range of post-SRES scenarios. The emissions include carbon 

dioxide (CO2

), methane (CH4), nitrous oxide (N2O) and F-gases (from Pachauri and Reisinger 2007, 

fig. 3.1). See figure 1 for SRES scenario definitions.

There is now very high confidence that these emissions were the result of human 

fossil fuel use, land use changes, and agriculture, and that “there is very high confidence that the net effect of human activities since 1750 has been one of warming” 

(Pachauri and Reisinger 2007: 5) (see fig. 3).

Some of the physical changes that have occurred owing to anthropogenic 

climate warming include the melting of arctic sea ice and land-based ice bodies, 

thawing of the permafrost, earlier spring runoff, and more frequent and more extreme weather events (Pachauri and Reisinger 2007). Biologically, species ranges 

are shifting poleward and upward in elevation, and phenology is changing (Colwell 

et al. 2008, Parmesan 2007, Root et al. 2003). Both negative and positive effects can 

be seen in plant populations, with productivity increases for some plant species,

while drought stress and increased insect outbreaks resulting from warmer temperatures causes dieback in other populations (e.g., Raffa et al. 2008). There are 

also social changes and economic impacts related to energy, forestry, health, and 

other areas (McMichael and Haines 1997, Mendelsohn and Neumann 1999, Patz 

and Olson 2006, Perez-Garcia et al. 2002, Tapsell et al. 2002). For example, global 

sea level rose, with biological and social implications, an average rate of 0.18 cm per 

year from 1961 to 2003, and the rate of increase is accelerating (Parry et al. 2007a).
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RESEARCH PAPER PSW-RP-262

California’s Changing Climate

The West has shown the strongest impacts from GHG emissions in the United 

States with an average temperature increase 70 percent greater than the world

average from 2003 through 2007 (Moser et al. 2009). The temperature increase

is expected to continue throughout the century, with a range of projected warming from 1.7 to 5.8 °C (Cayan et al. 2008, Moser et al. 2009), depending on the 

emissions scenario (see fig. 4). Precipitation projections are not as consistent, but 

Figure 3—Temperature changes relative to the corresponding average for 1901–1950 (°C) from decade to decade from 1906 to 

2005 over the Earth’s continents, as well as the entire globe, global land area and the global ocean (lower graphs). The black line 

indicates observed temperature change, whereas the colored bands show the combined range covered by 90 percent of recent 

model simulations. Pink indicates simulations that include natural and human factors, whereas blue indicates simulations that 

include only natural factors. Dashed black lines indicate decades and continental regions for which there are substantially fewer 

observations (from Soloman et al. 2007b, FAQ 9.2, fig. 1).
5

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

Figure 4—Historical and projected annual average temperatures for California. The lighter colored 

lines represent annual average temperatures, and the darker lines are the smoothed time series of 

annual average temperatures from different climate models all using the same emissions scenario, 

using 6-year running averages to more clearly display overall trends. The projections for the A2 and 

B1 global emission scenarios are represented with solid and dashed lines, respectively (from Moser

et al. 2009, fig. 16). 

overall, drier summers are expected in most of the Western United States, and more 

severe winter flooding is expected in some areas (Dettinger 2005, Knowles and 

Cayan 2004, Mastrandrea et al. 2009). 

Like many regions worldwide, California is being affected by rising sea levels, 

increasing average and extreme temperatures, changing precipitation regimes, and 

more frequent and severe wildfires (Mazur and Milanes 2009). Scientists have 

already observed an increase in rain versus snow, earlier snowfed streamflow, and 

earlier budbreak (Cayan et al, in press; Mastrandrea et al. 2009). Researchers found 

that anthropogenic GHGs, aerosols, ozone, and land use have contributed much 

to the observed decline in Western U.S. snowpack, the earlier snowmelt, and the 

reduction in snow water content (Hidalgo et al. 2009, Pierce et al. 2008).

Studies have used a mix of models and emissions scenarios to demonstrate

that extreme temperatures will likely be more common in the future. Mastrandrea 

et al. (2009), using updated versions of six global climate models (GCMs) used in 

the IPCC 2007 assessment (NCAR 1, GFDL CM2.1, CNRM CM3, Max Planck 

Institute ECHAM5, NCAR CCSM3 and MIROC 3.2) and two different emissions 

scenarios (SRES A2 and B1; see glossary for explanation of scenarios and GCMs), 

Like many regions 

worldwide, California is 

being affected by rising 

sea levels, increasing 

average and extreme 

temperatures, changing 

precipitation regimes, 

and more frequent and 

severe wildfires.
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RESEARCH PAPER PSW-RP-262

found that both minimum and maximum temperature extremes will become more 

common in many areas of California. Under the A2 higher emissions scenario, 

extreme temperatures that historically have occurred once every 100 years could 

happen nearly annually. Two statistical downscaling techniques both projected 

significant increasing trends across the state from 1950 to 2100 in warm nights, 

warm summer nights, and warmest three-night episodes. Both models projected 

increases in the duration of heat waves and hot spells (year-round), with severity 

increasing from coastal to interior California; results were more significant under 

the A2 scenario. Further, the two models projected significant decreases in growing 

season length and the number of frost days, more severe in the west of California; 

the A2 scenario again showed a stronger trend than B1. Precipitation projections, 

on the other hand, did not show significant trends under either of the models, and 

precipitation intensity showed opposite trends between models. Another study by 

Cayan et al. (2008) found similar results using two GCMs: (1) Parallel Climate 

Model (PCM) and (2) Geophysical Fluids Dynamics Laboratory (GFDL), chosen 

to address different levels of sensitivity to GHGs. Under the A2 and B1 emission 

scenarios, the extremely cool summer temperatures that have occurred in the past 

will be almost completely eliminated within decades (fig. 5).

Warming across California depends on geography, topography, season, and 

time of day. Although minimum temperatures are increasing almost uniformly, 

some maximum daily temperatures are actually decreasing (e.g., in the Central

Valley), owing in part, to irrigation and other local cooling effects (Bonfils et al. 

2008, Moser et al. 2009).

Precipitation patterns form an even more complex picture than temperature. As 

climates warm, there will be proportionally more rain and less snow, snow will melt 

earlier, and thus the growing season will start sooner. However, the actual levels of 

rainfall, the spatial and temporal variability of precipitation, the rate of evapotranspiration and the amount of soil moisture, and the interactive effects with plants are 

still unknown (Lundquist 2008).

Fire is also increasing in frequency and severity as the climate warms. Early 

snowmelt means larger fires and longer fire seasons. Moreover, climate change can 

increase wildfire risks by raising temperatures and by either increasing the vegetative fuel load (in wetter years) or drying out vegetation (in drier years). From 1987 

to 2003, 6.7 times more of the forested area in the Western United States burned 

than did from 1970 to 1986 (Westerling et al. 2006). Large California wildfires 

are projected to increase by 53 percent by the end of the century according to the 

higher emissions scenario A2 (Westerling and Bryant 2008); a range of scenarios 

project large increases in burned areas in forests of the Sierra Nevada, northern 
7

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

California coast, and southern Cascade Ranges (Westerling et al. 2009). Forests 

at mid-elevations are at a greater risk for wildfire than lower or higher elevational 

bands; at high elevation, the conditions are less favorable for wildfires because it is 

cooler and the dry season is relatively short (Moser et al. 2009). Larger fires also 

release large carbon emissions, creating a positive feedback loop.

As a result of all these changes, California’s public resources may be highly 

stressed, particularly the water supply. The state’s water supply mostly comes from 

its reservoirs and cold season precipitation (Mote et al. 2005), with over half of 

southern California’s water supply fed by melting Sierra Nevada snowpack (Waliser 

et al. 2009). By 2025, the population of California is expected to grow by 7 to 11 

million people (PPIC 2006), compounding water management challenges under 

climate change. Much of California’s population growth occurs in the south, widening the gap between demand there and supply in the wetter north and east. Furthermore, population pressures could foment conflicts between urban and agricultural 

use and allocations for endangered fish and wildlife (Mote et al. 2005).

Figure 5—Occurrence of seasonal temperatures falling into coolest (blue) and warmest (red) thirds 

of their historical (1961–1990) distribution for two model simulations under A2 and B1 emission scenarios. Values plotted are counts in 10-year moving windows with the bars centered in each window 

(from Cayan et al. 2008, fig. 3). PCM = Parallel Climate Model, GFDL = Global Fluid Dynamics 

Laboratory, both global climate models.
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RESEARCH PAPER PSW-RP-262

Figure 6—April 1 snow level trends 1950–1997. The red points indicate percentage of decrease in April 1 snow 

levels, and blue points indicate percentage of increase (from Moser et al. 2009, fig. 6).

Climate Trends in the Sierra Nevada

The Sierra Nevada is particularly important in the context of climate change in 

the West because much of California’s precipitation falls as snow in this mountain range; snowmelt becomes especially critical for California’s water supply in 

the drier seasons of summer and fall (Kapnick and Hall 2009). As temperatures 

increase, there is more rain, less snow, and earlier spring snowmelt, increasing 

the risk of flooding in the spring and water shortages in the summer. As more 

snow falls as rain during the winter, and spring snowmelt occurs sooner, the risk 

of flooding increases and water shortages may occur in the summer (Moser et al. 

2009). The amount of water contained in the accumulated snow on April 1 has 

been declining in low-elevation areas and throughout most of the northern Sierra 

Nevada, although snowfall in the high-elevation southern Sierra Nevada has 

increased (Moser et al. 2009) (fig. 6).
9

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

Some of the largest precipitation decreases and temperature increases (up to 3 

°C by the mid-21st century; Kim et al. 2009) are projected for winter in the Sierra 

Nevada, particularly the latter half of the cold season. Snowpack in the high Sierra 

Nevada is projected to decrease by over 40 percent in fall and nearly 70 percent 

in winter, reducing winter snowmelt by 54 percent from the late 1900s, according 

to data generated using the NCAR CCSM3 model with the SRES-A1B emissions 

scenario (Kim et al. 2009). Figure 7 shows the projected reductions in snow accumulation (snow water equivalent, [SWE]) for the Northern Sierra Nevada. As an 

exception, cold season rainfall is expected to increase in the high-elevation Sierra 

Nevada. 

Topography has a substantial effect on temperature and precipitation changes. 

For example, snowmelt changes may not be as large in the southern Sierra Nevada, 

where the elevation is higher and air temperatures are generally lower than in other

California regions. These lower air temperatures could provide a buffered environment that, in spite of rising temperatures, could maintain snow (Kim et al. 2009). 

Lundquist and Cayan (2007), through their work in examining microclimate in 

Yosemite National Park, showed that topographic features create complicated 

temperature patterns that cannot be explained by elevation alone, such as cold air 

pooling.

Figure 7—April 1 snow accumulation (snow water equivalent [SWE]) from the CNRM A2 simulation for the northern Sierra Nevada. Years with less SWE than its historical 10th percentile (1961–

1990) are shown in red. The 90th percentile and 10th percentile SWE levels are indicated by blue

and black horizontal lines, respectively (from Cayan et al. 2009, fig. 6).
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RESEARCH PAPER PSW-RP-262

Cayan et al. (2008) used the Variable Infiltration Capacity (VIC) model, a macroscale hydrologic model, to project the effect of climate change on the springtime 

snow accumulation using GFDL A2 and B1 climate simulations. They projected 

early gains in SWE in some areas when compared to historical (1961 to 1990) averages but, by year 2100, showed between -32 and -79 percent snow accumulation, 

with virtually no snow left below 1000 m under the A2 (higher) emissions scenario. 

Both models predict a smaller effect in the southern Sierra Nevada, with no change 

in spring snow accumulation projected in some areas (fig. 8).

Climate change could cause a cascade of effects in Sierran ecosystems. Lake 

Tahoe is warming at almost twice the rate of the world’s oceans, similar to warming 

Figure 8—Change in springtime snow 

accumulation from the Variable Infiltration 

Capacity (VIC) hydrological model, driven 

by climate changes from Geophysical Fluid 

Dynamics Laboratory (GFDL) A2 and 

Parallel Climate Model (PCM) B1 climate 

simulations. Changes are expressed as ratio 

of 2070–2099 April 1 snow water equivalent (SWE) to historical (1961–1990) SWE 

(from Cayan et al. 2008 fig. 14).

Climate change could 

cause a cascade of 

effects in Sierran 

ecosystems.
11

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

reported in other big lakes around the world, including the North American Great 

Lakes (Coats et al. 2006, Mazur and Milanes 2009). Night-time air temperatures 

increased 2 °C (3.6 °F) from 1914 to 2002, resulting in an increase in Lake Tahoe’s 

water temperature of approximately 0.49 °C (0.88 °F) from 1969 to 2002 (Moser 

et al. 2009). Since the mixing that redistributes the lake’s nutrients, oxygen, and 

cool water is impeded by warm lake temperatures, climate change could ultimately 

reduce lake clarity, benefit existing and novel invasive species, and decrease populations of native fish and other organisms that depend on Lake Tahoe (Chandra

et al. 2009, Schladow 2009). 

Another complex effect of rising temperatures is increased wildfire. A recent 

study showed that the Sierra Nevada experienced an increase in the mean and 

maximum fire size, the area burned annually, and the extent of forest high-severity 

fire between 1984 and 2006 (Miller et al. 2009). These changes will have substantial effects on Sierran plant and animal communities and economic and social costs.

Ecological Effects of Changing Climate in the Sierra 

Nevada

The effects of increased atmospheric GHGs on species remain the least understood 

aspect of global climate change. The shifting seasons, increase in temperature and 

extreme weather, and decrease in snowfall and dry season moisture projected for 

California will likely have complex and occasionally unexpected impacts on plants, 

animals, and other organisms.

Loik capitalized on snow fences in the eastern Sierra Nevada to study the 

effects of different snow depths on vegetation. He found that if there is less snow, 

flowering occurs earlier and adult lodgepole (Pinus lambertiana Dougl. ex Loud.) 

and Jeffrey pine (Pinus jeffreyi Grev. & Balf.) trees grow less, although Jeffrey pine 

seedlings may establish better (Loik 2008). He predicted that, given widespread 

climate change, seed production will likely change and, if trees and pollinators 

respond to different cues, reproduction could be impeded.

Historical data can inform climate projections as well. Monthly minimum 

temperatures in the mid-elevation Sierra Nevada have increased by about 3 oC (5.4 

oF) over the last century (Thorne et al. 2006). Over the last century, there has been 

a shift away from freezing nights in the Sierra Nevada (Mazur et al. 2009). Warmer 

nights correlate with summer drought that can increase seedling mortality; a further analysis controlling for land use changes and wildfire effects strongly indicated 

that climate change had caused the observed decline in ponderosa pine (Moser et al. 

2009). Similarly, drought has been implicated in extensive mortality in aspen stands 

across the West (Morelli and Carr 2011).
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RESEARCH PAPER PSW-RP-262

Several authors have suggested that trees might show a diversity of responses to 

climate change, including changing stand density, growth rates, and mortality rates

(e.g., Millar et al. 2007b). Two models (PCM and HADLEY CM3) using both high

(A1FI) and low (B1) emissions scenarios have projected a reduction in Sierra 

Nevada alpine and subalpine forests (Hayhoe et al. 2004). Increasing drought 

stress could cause mortality among adult trees as they would be more vulnerable 

to insects, pathogens, and air pollution (Boisvenue and Running 2006; Morelli and 

Carr 2011; Raffa et al. 2008). Conversely, a recent study projected that ponderosa 

pine plantation yields will increase 9 to 28 percent by 2100 (Battles et al. 2009). 

There may be climate change refugia, areas that are not greatly affected by regional 

climate trends into which populations contract their ranges, that could be targeted 

for conservation (Loarie et al. 2008, Millar and Morelli 2009). 

The effects of changing climate on animal species will also be complex

(Parmesan and Yohe 2003, Root et al. 2003). Altered food availability, predator 

and prey abundance and distribution, and other species interactions will affect 

responses in unforeseen ways. They will be affected by altered snow and ice cover, 

precipitation, streamflow, humidity, soil moisture, and insolation (Parry 2007a). 

Suitable habitat may shrink or fragment as climate changes. Sierra Nevada species 

may need to move south to go up in elevation, and populations could increase or 

decrease depending on their migration ability and other factors. A comprehensive 

historical comparison conducted in Yosemite National Park indicated that many 

small mammals have shifted to higher elevations or contracted their range at high 

elevations, but some species had the opposite or no response to the warming climate 

(Moritz et al. 2008). 

Some animal species may be especially sensitive to climate change. Migratory 

songbirds may be in trouble if timing of their life history events, such as breeding 

and brooding, are mismatched against their habitat and food resources. Researchers 

have already noted some species arriving earlier in California (MacMynowski and 

Root 2007). Likewise, changing snowpack seasonality may already be negatively 

affecting songbird populations in Yosemite National Park (Stock 2008). Some 

animal species will be directly affected by a decrease in a major food source, such 

as Clark’s nutcrackers (Nucifraga columbiana) through their relationship with 

white bark pine (Pinus albicaulis Engelm.) (Davey et al. 2007). In addition, animal 

species in alpine or subalpine habitats have narrow physiological tolerances and 

may be vulnerable to thermal stress, both heat and cold. For example, lower snow 

cover may actually result in colder winter habitat for terrestrial animals, such as 

documented with the sensitivity of the American pika (Ochotona princeps) to cold 

temperatures (Beever et al. 2010, Morrison and Hik 2007). On the other hand,
13

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

many insects may get benefits from wetter winters, although aquatic insects would 

likely be negatively affected by summer drying conditions (Holmquist and Schmidt 

2008). 

Finally, ecosystem services that society has come to expect from nature may 

be endangered by human-caused climate change (Mazur and Milanes 2009, Moser 

et al. 2009). As an example, an analysis of cattle ranching in California concluded 

that average annual profits would be down $22 to $92 million by 2070 (Shaw et al. 

2009). In addition, increased fire severity and extent will undoubtedly have huge 

financial and social costs, as well as health costs in terms of air quality and lives.

Climate Projections for Inyo National Forest

The Inyo National Forest comprises land in the eastern Sierra Nevada and western 

Great Basin (fig. 9A) and is similarly affected by changing climate. For example, 

glaciers on the Inyo National Forest are retreating, just as they are across the Sierra 

Nevada (Mazur and Milanes 2009). Another concern is the increase in extreme 

weather events such as flooding, severe wildfires, high minimum temperatures 

in winter, and high maximum temperatures in winter and summer. Droughts in 

summer or fall, which could become more common, are already problematic. Many 

issues have social as well as ecological implications: increased wildfire, the impact 

of reduced snowfall on the ski industry, possible heat waves in Bishop (the location 

of Inyo National Forest headquarters), and others.

We selected three GCMs (HADLEY CM3, MIROC3.2-medres, and CSIROMk3.0) in combination with three SRES carbon emissions scenarios (A2, A1B, and 

B1), representing as broad a range of temperature sensitivities as possible, to run 

the MC1 DGVM for a large section of the Inyo National Forest (fig. 9B). The grain 

of the MC1 model study was 800 m and it was run with high CO2 input and nitrogen limitation (McGlinchy 2011). The majority of the model analysis focused on 

MIROC3.2-medres A2 and CSIRO-Mk3.0 A2. These models are among those that 

encompass the widest available range of potential climate futures for the eastern 

Sierra Nevada, and A2 is currently the most realistic of the three carbon emissions 

scenarios used here (see “Glossary” and fig. 1 for a description of scenarios). 

MC1 is a dynamic general vegetation model (DGVM, also known as a dynamic 

global vegetation model) that uses climate and soil inputs to simulate ecosystem 

processes in response to changing climatic conditions, relying on three interacting 

modules. A full description of the model can be found elsewhere (Bachelet et al. 

2008, Daly et al. 2000, Lenihan et al. 2008). Low-resolution GCM scenarios

were downscaled to the 800-m grid using an anomaly method and high-resolution 

climate data produced by the PRISM climate group at Oregon State University 
14

RESEARCH PAPER PSW-RP-262

Figure 9—Inyo National Forest in relation to California (A) and as outlined in black (B). Area outlined in (B) with no 

color fill is outside the area of analysis.

(Daly et al. 2002). The downscaled climate data for the nine scenarios reflect 

greater similarities within GCMs than within emissions scenarios. For example, 

results from the three scenarios using HADLEY CM3 GCM are more alike than 

results from the three A2 scenarios across different models. Thus, the choice of 

GCM has a large effect on results.

The HADLEY CM3 and MIROC3.2-medres scenarios project temperature increases from 2.5 to 10 °C (4.5 to 18 °F), with greater warming in the north end of 

the forest and milder warming in the south (fig. 10). For both models, the temperature increase is not concentrated in one season, but is rather a uniform warming 

throughout the year. In contrast, the CSIRO-Mk3.0 scenarios project very little 

warming in the Inyo region. 
15

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

There is a larger discrepancy among GCMs when projecting future precipitation (fig. 11). The MIROC3.2-medres scenarios are the most extreme, with greater 

than 30 percent reductions in precipitation throughout much of the region. CSIROMk3.0 A2 scenario, on the other hand, predicts a substantial increase in precipitation, especially in the mountainous regions. All nine scenarios depict an increase 

in precipitation along the Sierra Crest just west of Mono Lake; further investigation 

is underway to discover whether this represents a natural process or is merely a 

peculiarity of the model. 

As a result of rising temperatures, the increased precipitation will primarily fall 

as rain rather than snow. In fact, both scenarios show a decrease in annual snowpack in the eastern Sierra, leading to an earlier drying out of fuels and a longer fire 

season. Other results (see figs. 19 and 20 in appendix) indicate a more severe and 

extensive future fire season for the Inyo National Forest. The increase in fire occurrence and severity may in turn lead to the increase in both woodland and grassland 

types in the eastern Sierra modeled by MC1, although it is apparent in both the dry 

and wet climate scenarios.

Figure 10—Projected change in maximum monthly temperature comparing two 30-year averages 

(1971–2000 versus 2071–2100) using three global climate models (HADLEY CM3, MIROC3.2 

medres, and CSIRO-Mk3.0) and three carbon emission scenarios (A2, A1B, and B1).

Both scenarios show 

a decrease in annual 

snowpack in the 

eastern Sierra, leading 

to an earlier drying out 

of fuels and a longer 

fire season.
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The most striking vegetation change in the Inyo National Forest projections is 

the reduction in alpine/subalpine ecosystems. In MC1, alpine/subalpine reduction is 

a consequence of increased growing degree days in these areas, potentially allowing 

lower elevation species to expand their ranges into higher elevations. It is important 

to note when analyzing these results that MC1 does not model the mechanisms for 

ecosystem change (e.g., species migration, dispersal, or establishment). Therefore 

it is not likely that high-altitude ecosystem change will happen as quickly as the 

model predicts. However, conditions are changing so that lower elevation species 

should be better able to compete with established species in these higher elevation 

areas. 

For validation, we compared the MC1 historical vegetation types to the

CalFIRE CWHR (California Department of Forestry and Fire Protection-California 

Wildlife Habitat Relationships) data set (http://frap.cdf.ca.gov, accessed July 2009). 

MC1 is a potential vegetation model; it simulates vegetation type in the absence 

of any human disturbance, e.g., agriculture, fire suppression, grazing, and urban 

development. On the other hand, the CWHR extrapolates based on current ground 

Figure 11—Projected percentage of change in annual precipitation comparing two 30-year averages 

(1971–2000 versus 2071–2100) using three global climate models (HADLEY CM3, MIROC3.2 

medres, and CSIRO-Mk3.0) and three carbon emission scenarios (A2, A1B, and B1).
17

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

cover. The difference in methodology causes the discrepancies seen between the top 

two maps in fig. 12A. For instance, in areas where CWHR shows forest and MC1 

shows woodland, there may be more carbon on the landscape as a result of fire 

suppression, which was not simulated for this particular study with MC1. 

Overall, there is considerable agreement between the two climate GCMs 

(MIROC3.2-medres and CSIRO-Mk3.0) used for the MC1 vegetation projections. 

They both project an increase in grassland and woodland on the Inyo National Forest, with a decrease in shrubland. They also project a reduction of subalpine forest 

and a severe or complete loss of tundra habitat. Finally, a novel habitat for the 

eastern Sierra Nevada, desert vegetation, emerges (fig. 12B). Further projections 

produced using the MC1 model can be found in the appendix.

Researchers have attempted to quantify the uncertainty inherent to any climate 

change projections (e.g., Ganguly et al. 2009). There is uncertainty in trying to 

project the future; this is only partly addressed by using a range of scenarios. There 

is also inherent uncertainty in downscaling climate anomalies from coarse-scale 

GCMs to the regional and local scales. This uncertainty is extended by using the 

MC1 model, as it does not consider some local processes and conditions, such as 

the simplification of soil types mentioned above. Further, MC1 adds uncertainty by 

not incorporating anthropogenic land use and by not explicitly modeling dispersal, 

seedling establishment, or animal impacts such as grazing and insect damage. 

For example, the MC1 model projects an increase in the mixed-forest type (e.g., 

ponderosa and black oak) throughout the mid-elevations of the west slope all along 

the Sierra (bottom two maps of fig. 12A). Much of the increase is due to an underestimation of the present distribution of broadleaf, deciduous trees and thus is not a 

true indication of a massive increase in the mixed-forest vegetation type. Likewise, 

MC1 classifies an area as alpine based solely on climate variables, not vegetation 

(i.e., carbon) or soil quality. The model then grows vegetation based on soil characteristics derived from STATSGO (State Soil Geographic Database) maps, which 

do not accurately represent soil types and rock outcrops in the complex terrain of 

the Sierra Nevadas. Thus, MC1 overestimates vegetative growth at high elevation 

near the Sierran crest. However, despite these stipulations, the MC1 model creates a 

good representation of the vegetation of the area. 

The MC1 projections presented here resemble similar analyses conducted elsewhere in the Sierra Nevada. Another DGVM MC1 model of Yosemite National 

Park (Panek et al. 2009), using the same GCMs (HADLEY CM3, MIROC3.2-

medres, and CSIRO-Mk3.0) and emissions scenarios (SRES A2, A1B, and B2) and 

also comparing the historical period of 1961–1990 to the future of 2071–2100, found 

similar results. It projected temperature increases overall and especially in winter 
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RESEARCH PAPER PSW-RP-262

Figure 12—(A) Mapped distribution and (B) percentage of cover by vegetation class of simulated historical (1971–2000) and future (2071–2100) MC1 vegetation type (30-year mode) using two global climate 

models (MIROC3.2-medres and CSIRO-Mk3.0) and one carbon mission scenario (A2). The (California 

Department of Forestry and Fire Protection’s California Wildlife Habitat Relationships) CWHR map 

for 2002 is included for comparison. The CWHR vegetation categories have been aggregated to match 

the classification scheme used in our analysis as follows: alpine includes alpine-dwarf shrub; subalpine 

forest includes subalpine forest, lodgepole pine, red fir; conifer forest includes eastside pine, Jeffrey pine, 

Sierran mixed conifer; mixed forest includes ponderosa pine/black oak, montane hardwood; woodland 

includes blue oak/foothill pine, pinyon/juniper; shrubland includes sagebrush, mixed chaparral; grassland includes annual grassland. The dark gray patch in the southwest corner of the CWHR figure is the 

development of the Central Valley. Small patches of desert habitat emerge in the Owens Valley in the 

2071–2100 scenarios. Map colors appear darker than legend because of topographic shading.
19

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

minimum temperature, reduced snowpack, earlier snowmelt, and larger fire area. 

The Yosemite MC1 model also found an increase in woodlands and maintenance 

of the conifer forest cover, a severe reduction or disappearance of subalpine forest 

and loss of the alpine ecosystem, and novel desert vegetation appearing on the 

eastern side of the Sierra Nevada (Panek et al. 2009). Hayhoe et al. (2004) similarly 

projected a reduction in Sierra Nevada alpine and subalpine forests using the GCMs 

HADLEY CM3 and PCM with both high (A1FI) and low (B1) emissions scenarios. 

Finally, the highly variable precipitation projections resemble results from other 

climate projections for the Sierra Nevada.

Adaptation

Given that the effects of anthropogenic climate change are set in motion, and in 

some cases are already apparent, responding to them is a pressing goal for land 

managers. The objective of adaptation, “the adjustment in natural or human 

systems in response to actual or expected climatic stimuli or their effects” (Parry 

2007a: 869), is to lessen the negative effects of climate change. There are many 

considerations before adaptation options are pursued, including the planning horizon, the actors involved, the predictability of effects, and current and future climate 

hazards (fig. 13) (Füssel 2007). Because one of the biggest concerns for adaptation 

Figure 13—Diversity of adaptation contexts (figure created by author from text in Füssel 2007). 
20

RESEARCH PAPER PSW-RP-262

is the uncertainty of the climate change effects, the ideal adaptation option is one 

in which there will be benefits regardless of the climate trajectory (“no regrets”). 

Otherwise, it is necessary to decide which is worse: risk of unavoided impacts or of 

increased cost with limited effectiveness of adaptation strategies owing to insufficient information (table 1) (Füssel 2007).

As set out by Millar et al. (2007a), major adaptation options include resistance 

(forestall impacts and protect highly valued resources); resilience (improve the 

capacity of ecosystems to return to desired condition after disturbance); response 

(facilitate transition of ecosystems from current to new conditions); and realignment 

(adapt to future conditions). Table 2 lays out adaptation options in relation to the 

forest management goals of the U.S. Forest Service. Many of the adaptation options 

are not new but focus on extending current practices already used by land managers. For example, intensive fuel breaks, involving total deforestation and vegetation removal to soil level, have been used in some high-value chaparral zones in 

southern California. Thinning may be extended to a broader scale or more frequent 

timeline to increase the resilience of stands to drought, disease, and wildfire (Oliver 

and Larson 1996, Peterson et al. 2005). Snow-making can enable a ski resort to 

stay open despite lower snowpack, thus increasing resilience in a rural community 

context. Adjacent resorts in the eastern Sierra Nevada provide dramatic examples: 

Mammoth Ski Area stays open because of snow-making capacity, whereas June 

Mountain Ski Area has no snow-making capacity, causing them to remain closed 

through part or all of two winter seasons.

Germplasm management could be modified to increase species response to 

climate change. Traditional seed transfer and seed zone rules for planting, developed with the assumption that plants are genetically adapted to the environment in 

which they are currently growing, will likely be violated by climate change. Thus, 

seed transfer and seed mixes within species can be modified either by mixing 

a proportion of seed from all adjacent seed zones or by anticipating the climate 

effect and vegetation response by, for example, moving warmer adapted (usually 

lower elevation) germplasm to higher planting sites. In both cases, seeds would be 

planted at slightly higher density than routine, allowing natural selection to cull. 

Additionally, studies have shown that increasing the ability of species to disperse, 

e.g., by extending habitat connectivity, could greatly increase the ability of species 

to respond to changing climates (Loarie et al. 2008).

On the other hand, new tools are being developed; for instance, the potential 

loss of the Joshua tree is motivating park managers to consider relocating trees 

to higher elevations in anticipation of shifting climate (Welling 2008). Another 

innovative approach would be to manage habitat networks that include climate 

Because one of the 

biggest concerns 

for adaptation is the 

uncertainty of the 

climate change effects, 

the ideal adaptation 

option is one in which 

there will be benefits 

regardless of the 

climate trajectory.
21

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

Table 1—Adaptation decision tree 

Use adaptation if: Postpone adaptation if:

•	 Climate-sensitive risks are already urgent

•	 Increasing risks are projected reliably	 •	 Current and anticipated future risks

 are moderate

•	 Future impacts are potentially catastrophic	 •	 Adaptation is very costly

or irreversible

•	 Decisions have long-term effects	 •	 Timely response options are readily

 available

•	 Adaptation measures have a long lead time

Source: Fussel 2007. Table was created by author from text.

Table 2—Impacts of climate change on the forest management goals of the U.S. Forest Service 

Desired or intended Possible climate change

Goal outcome impacts Adaptation options

Restore, sustain, Maintain forest health, Longer, warmer growing seasons Reduce fuel loads in forests

and enhance productivity, diversity

national forests	 and resistance to severe	 Altered fire regimes	 Increase use of wildland fire use

disturbances

Shifts in seasonality of Enhance the early detection and

hydrological processes response strategy associated with

Intense droughts nonnative invasive species

Provide and sustain Maintain multiple Climate change interacting with Increase efforts to reduce current

benefits to the	 socioeconomic	 current stress factors such as insect	 stress factors

Nation’s citizens	 benefits to meet society’s	 pests and disease, wildfire, legacy of

needs over the long term, past management and air pollution Incorporate long-term climate

including a reliable supply	 	 change into wildland fire planning

of forest products, energy Shifts in forest species composition

resource needs and Develop silvicultural treatments

market-based conservation Increased erosion events impairing to reduce drought stress

watershed condition

Review genetic guidelines for

 reforestation

Conserve open Maintain the Large-scale forest dieback or Provide technical assistance to

space environmental, vegetation type conversions as urban foresters to sustain urban

social and economic a result of more frequent extreme trees

benefits of forests,	 events

protecting these resources Develop corridors for species

from conversion to other Altered landscape and successional migration and habitat protection

uses, and helping private dynamics

landowners and

communities manage their Increasing fragmentation of forest

land as sustainable forests ecosystems and wildlife habitat

Sustain and Maintain high-quality Increased air and stream Evaluate recreational impact on

enhance outdoor outdoor recreation temperatures ecosystems under a changing

recreation opportunities in national climate

opportunities forests available to the Reduced snowpack

public Expand recreational opportunities

Altered in-stream flows	 across all four seasons

Redesign roads and trails to

withstand increased rainfall

 intensity
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Table 2—Impacts of climate change on the forest management goals of the U.S. Forest Service (continued)

Desired or intended Possible climate change

Goal outcome impacts Adaptation options

Maintain basic Develop administrative Poor accessibility or lack of Increase technical understanding by

management facilities, information current information on climate developing educational material for

capabilities of the systems, and land change projections, ecosystem employees and stakeholders

Forest Service ownership management impacts and socioeconomic

strategies to support wide- impacts on local communities Incorporate climate change into

ranging natural planning processes

resource challenges Uncertainty associated with that Enhance research partnerships

 information

Engage urban Provide broader access to Exacerbation of the stress that urban Expand conservation education

citizens long-term environmental, environments place on ecosystems, programs to include climate change

social, economic, and other as a result of warming temperatures

benefits provided by the	 	 Seek opportunities to educate

Forest Service	 Increased wildfire and drought risks	 national forest visitors on climate

in surrounding landscapes, which change

may compromise ability to maintain

water quality and availability

Provide science- Ensure that the best Need for management tools Establish stronger relationships

based applications	 available science-based	 that incorporate climate change	 between scientific researchers and

and tools for knowledge and tools considerations management to help identify

sustainable natural inform Forest Service resilience thresholds for

resource management decisions Need to revise current management key species and ecosystem

management practices that are based on processes, determine which

assumptions about ecosystems and thresholds will be exceeded, 

climate that may be invalid in the prioritize projects with a

future high probability of success, and

identify species and vegetation

structures tolerant of increased

 disturbance

Source: Blate et al. 2009.

change refugia as places where species or ecosystems, such as American pika and 

their talus habitat, may be buffered from the effects of changing climates (Millar 

and Morelli 2009). 

Changes in human interactions with habitats and species must also be considered. The season of use may shift or even get longer if winters become shorter and 

less severe. Likewise, areas that are normally not heavily used, such as subalpine 

and alpine habitats, may see greater use, and water bodies that already see heavy 

use may be more heavily impacted if environments dry (Meldrum 2008). Wildlife 

may be more greatly affected if animals shift ranges and thus increase encounters 

with humans. Finally, activity shifts will have unpredictable effects on the landscape and the species that live on it. 

Ideas for good management options and available tools can be found within the 

burgeoning literature of climate adaptation (see table 3 for some online resources). 

Overall, it is important to remember that there is a lot of uncertainty in climate 
23

A Climate Change Primer for Land Managers: An Example From the Sierra Nevada

projections. For example, most projections are for scales larger than is relevant to 

land managers. Moreover, there are many yet unanswered questions regarding how 

current stressors (e.g., invasive species, habitat fragmentation, insect and disease 

outbreaks) will interact with climate effects. As a result, it will be good management practice to optimize resources by taking “no regrets” actions where possible, 

such as increasing resilience in ecosystems. Rapid changes that are expected in 

physical conditions and ecological responses suggest that management goals and 

approaches will be most successful when they emphasize ecological processes, 

rather than focusing primarily on structure and composition.

Acknowledgments

This review was conducted as part of a case study of climate change adaptation 

options for the USDA Forest Service. Dominique Bachelet, Jim Baldwin, Deanna 

Dulen, and Hugh Safford provided useful reviews of the manuscript. We thank 

Connie Millar for her guidance throughout this work.

Table 3—Some key online climate change adaptation resources

2009 California climate adaptation strategy: discussion draft www.energy.ca.gov/2009publications/CNRA-1000-2009-

027/CNRA-1000-2009-027-D.PDF

California Climate Tracker www.wrcc.dri.edu/monitor/cal-mon/frames_version.html

Climate change 2007: impacts, adaptation and vulnerability www.ipcc.ch/publications_and_data/publications_ipcc_

fourth_assessment_report_wg2_report_impacts_adaptation_

and_vulnerability.htm

Climate change 2007: synthesis report-summary for www.ipcc.ch/pdf/assessment-report/ar4/syr/ar4_syr_spm.pdf

policymakers

Indicators of climate change in California http://oehha.ca.gov/multimedia/epic/pdf/

ClimateChangeIndicatorsApril2009.pdf

Preliminary review of adaptation options for climate- downloads.climatescience.gov/sap/sap4-4/

sensitive ecosystems and resources. Final report,	 sap4-4-final-report-Ch3-Forests.pdf

synthesis and assessment product 4.4, chapter 3:

national forests

U.S. EPA Climate Change site www.epa.gov/climatechange/index.html

U.S. Forest Service Climate Change Resource Center http://www.fs.fed.us/ccrc

(CCRC)

Rapid changes that 

are expected in 

physical conditions 

and ecological 

responses suggest 

that management goals 

and approaches will 

be most successful 

when they emphasize 

ecological processes, 

rather than focusing 

primarily on structure 

and composition.
24

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English Equivalents

When you know: Multiply by: To get:

Millimeter (mm) 0.0394 Inches

Meters 3.28 Feet

Hectares 2.47 Acres

Grams per square meter (g/m2

) .00328 Ounces per square foot

Degrees Fahrenheit	 .56( ̊F-32)	 Degrees Celsius

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Glossary

adaptation—“Adjustment in natural or human systems in response to actual 

or expected climatic stimuli or their effects, which moderates harm or exploits 

beneficial opportunities” (Parry et al. 2007a: 869).

climate change—“Climate change refers to any change in climate over time, 

whether due to natural variability or as a result of human activity. This usage 

differs from that in the United Nations Framework Convention on Climate Change 

(UNFCC), which defines ‘climate change’ as: ‘a change of climate which is

attributed directly or indirectly to human activity that alters the composition of the 

global atmosphere and that is in addition to natural climate variability observed over 

comparable time periods” (Parry et al. 2007a: 871).

dynamic general vegetation model/dynamic global vegetation model 

(DGVM)—See GVM.

ecosystem services—Ecological processes or functions having monetary or 

nonmonetary value to individuals or society at large. There are (1) provisioning 

services such as fiber, fuel, and food; (2) regulating services such as air quality and

water purification; (3) cultural services such as aesthetic values and ecotourism; and

(4) supporting services such as pollination and nutrient cycling (Joyce et al. 2008).

extreme weather events—Extremes refer to rare events based on a statistical model 

of particular weather elements, and changes in extremes may relate to changes in 

the mean and variance in complicated ways. Changes in extremes are assessed 

at a range of temporal and spatial scales. Extreme is generally defined as events

occurring between 1 percent and 10 percent of the time at a particular location 

during a particular reference period. Some of the measures of extreme weather 

events include number or timing of frost days, growing season length, number of 

warm nights, number of warm summer nights, heat wave duration and magnitude, 

precipitation intensity, number of consecutive dry days, and number of 5-day 

precipitation events (Mastrandrea et al. 2009, Trenberth et al. 2007).

forest sustainability—Sustainability is “the capacity of forests, ranging from 

stands to ecoregions, to maintain their health, productivity, diversity, and 

overall integrity, in the long-run, in the context of human activity and use” 

(Helms 1998).

general circulation model/global climate model (GCM)—A GCM is a type of 

“numerical representation of the climate system based on the physical, chemical, 

and biological properties of its components, their interactions and feedback 
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processes, and accounting for all or some of its known properties” (Parry et al. 

2007: 872). There were six GCMs run for the Intergovernmental Panel on Climate 

Change (IPCC) Fourth Assessment (Parry et al. 2007a, Solomon et al. 2007b) 

using the Special Report on Emissions Scenarios (SRES) A2 and B1 emission 

scenarios, employed to assess climate changes and their impacts for the 2008 

California Climate Change Assessment. For the assessment, the National Center 

for Atmospheric Research (NCAR) Parallel Climate Model (PCM), the National 

Oceanic and Atmospheric Administration (NOAA) Geophysical Fluids Dynamics 

Laboratory (GFDL) version 2.1 model, the NCAR Community Climate System 

Model (CCSM), the Max Plank Institute’s ECHAM3, the Japanese Model for 

Interdisciplinary Research on Climate (MIROC), and the French Centre National 

de Recherches Météorologiques (CNRM) models were selected (CAT 2009). Two 

other models that are included in the latest IPCC assessment are from Australia’s 

Commonwealth Scientific and Industrial Research Organisation (CSIRO) and the

United Kingdom’s Met Office Hadley Centre (HADLEY). Given current computer

constraints, the GCMs must be run on relatively coarse spatial grids of about 2 to 

3 degrees of latitude by longitude, measuring several hundred kilometers on a side. 

Later the output can be downscaled to higher resolution grids of observed climate to 

provide scales that are biologically meaningful. There are a number of downscaling 

methods, each with its own drawbacks and tradeoffs; high-resolution ecosystem 

simulations, including those presented here, should only be used to infer possible, 

plausible futures and should not be overinterpreted.

general vegetation model/global vegetation model (GVM)—Process-based 

models include both biogeographic (vegetation distribution) and biogeochemical 

(nutrient and water cycling) processes. The GVMs operate on the principle that 

most ecosystems will produce just enough leaves to utilize all the available soil 

water during an average growing season, i.e., optimizing soil available water. 

They calculate maximum leaf area index (LAI) at any location for both the 

woody overstory and the grassy understory. They combine LAI with information 

on thermal zone and leaf type to determine a physiognomic classification of the

vegetation, e.g., Temperate Deciduous Forest. They use a spatial grid of monthly 

climate as input to produce a map of vegetation type distribution that can be directly 

compared to observed vegetation maps. The GVMs also reflect growing vegetation

or dieback, given climate inputs and vegetation type. Dynamic general (or global) 

vegetation models, DGVMs, combine biogeographic and biogeochemical GVMs 

with a disturbance regime to simulate the trajectory of vegetation change over time 

as the climate changes (Bachelet et al. 2008, Daly et al. 2000, Lenihan et al. 2008, 

Neilson 1995).
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greenhouse gases (GHGs)—“Greenhouse gases are those gaseous constituents 

of the atmosphere, both natural and anthropogenic, that absorb and emit radiation 

at specific wavelengths within the spectrum of infrared radiation emitted by the

Earth’s surface, the atmosphere, and clouds. This property causes the greenhouse 

effect. Water vapour (H2O), carbon dioxide (CO2), nitrous oxide (N2O), methane 

(CH4), and ozone (O3) are the GHGs in the Earth’s atmosphere. As well as CO2, 

N2O, and CH4, the Kyoto Protocol deals with the GHGs sulphur hexafluoride (SF6), 

hydrofluorocarbons (HFCs) and perfluorocarbons (PFCs)” (Parry et al. 2007a: 875).

mitigation—“An anthropogenic intervention to reduce the anthropogenic forcing 

of the climate system; it includes strategies to reduce greenhouse gas sources and 

emissions and enhancing greenhouse gas sinks” (Parry et al. 2007a: 878).

Pacific decadal oscillation (PDO)—The PDO is a pattern of Pacific climate

variability, which can persist for decades. Contrary to the El Niño/Southern 

Oscillation (ENSO), PDO effects are more obvious in the North Pacific than in

the tropics. Until recently, the PDO regime has been warm, contrasting the three 

decades that preceded 1976 (Mantua 1999).

scenarios—In the context of climate change science, scenarios are “projections of 

a potential future, based on a clear logic and a quantified storyline” (Nakicenovic

et al. 2000). There are 40 SRESs (Special Report on Emissions Scenarios), derived 

using integrated assessment models developed by six modeling teams. All are 

considered equally valid, with no assigned probabilities of occurrence; however, the 

estimated emissions growth for 2000 to 2007 was above that of the most fossil-fuelintensive scenario of the IPCC (Global Carbon Project 2008). The most commonly 

used scenarios are part of four storylines (see appendix fig. 1), one of which was

further categorized by alternative developments of energy technologies: A1FI 

(fossil intensive), A1T (predominantly nonfossil), and A1B (balanced across energy 

sources) (Nakicenovic et al. 2000). The emissions scenarios are run through GCMs 

to produce estimates of present, past, and future climates.

vulnerability—“Vulnerability is the degree to which a system is susceptible to, and 

unable to cope with, adverse effects of climate change, including climate variability 

and extremes. Vulnerability is a function of the character, magnitude and rate of 

climate change and the variation to which a system is exposed, its sensitivity and its 

adaptive capacity” (Parry et al. 2007a: 883).
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uncertainty—“An expression of the degree to which a value (e.g., the future state 

of the climate system) is unknown. Uncertainty can result from lack of information 

or from disagreement about what is known or even knowable. It may have many 

types of sources, from quantifiable errors in the data to ambiguously defined

concepts or terminology, or uncertain projections of human behaviour. Uncertainty 

can therefore be represented by quantitative measures (e.g., a range of values 

calculated by various models) or by qualitative statements (e.g., reflecting the

judgment of a team of experts)” (Parry et al. 2007a: 882).
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Appendix

Simulating the effects of climate change on vegetation distribution, carbon, fire and 

hydrology on the Inyo National Forest (see Climate Projections for Inyo National 

Forest on page 13 for methods details).

Figure 15—Projected percentage change in relative humidity (May–October) comparing two 

30-year averages (1971–2000 versus 2071–2100) using three global climate models (HADLEY CM3, 

MIROC3.2-medres, and CSIRO-Mk3.0) and one carbon emission scenario (A2).

Figure 14—Projected change in minimum monthly temperature comparing two 30-year averages 

(1971–2000 versus 2071–2100) using three GCMs (HADLEY CM3, MIROC3.2-medres, and CSIROMk3.0) and three carbon emission scenarios (A2, A1B, and B1).
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Figure 17—Relative change in total live carbon (aboveground and belowground) as projected by MC1 

comparing simulated history (1971–2000) versus future (2071–2100) using two global climate models 

(MIROC3.2-medres and CSIRO-Mk3.0) and one carbon emission scenario (A2) as (A) live carbon

(g/m2

) and (B) percentage of change in live carbon.

Figure 16—Relative change in total cover by vegetation class as predicted by MC1 using two global 

climate models (MIROC3.2-medres, and CSIRO-Mk3.0) and one carbon emission scenario (A2) for 

the Inyo subregion. Mixed forest is projected to increase 1,300 percent under the MIROC3.2-medres 

model and increase 600 percent according to the CSIRO-Mk3.0 model.
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Figure 18—Relative change in total ecosystem carbon (live and dead, aboveground and belowground) 

as projected by MC1 comparing simulated history (1971–2000) versus future (2071–2100) using two 

global climate models (MIROC3.2-medres and CSIRO-Mk3.0) and one carbon emission scenario 

(A2) as (A) total carbon (g/m2

) and (B) percentage of change in total carbon.

Figure 19—Relative change in biomass consumed by fire (g/m2

) as projected by MC1 comparing

simulated history (1971–2000) versus future (2071–2100) using two global climate models 

(MIROC3.2-medres and CSIRO-Mk3.0) and one carbon emission scenario (A2).
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Figure 21—Relative change in the rate of fire spread, possibly owing to an earlier fuel dry-out, as 

projected by MC1 simulated 1950–2000 average using two global climate models (MIROC3.2-

medres and CSIRO-Mk3.0) and one carbon emission scenario (A2) across the greater Sierra Nevada 

subregion (Mount Whitney to Mount Lassen). Intensity of fire behavior is projected to increase.

Figure 20—Relative change in area burned as projected by MC1 simulated historical (1950–2000) 

11-year running average for the Inyo subregion using two global climate models (MIROC3.2-medres 

and CSIRO-Mk3.0) and one carbon emission scenario (A2). Burned area is projected to increase.
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Figure 22—Percentage of change relative to simulated historical (1950–2000) average as projected 

by MC1 using two global climate models (MIROC3.2-medres and CSIRO-Mk3.0) in the 5-year 

running average of (A) coarse fuel moisture across the greater Sierra Nevada subregion (Mount 

Whitney to Mount Lassen), May to November, and (B) annual maximum snow depth averaged for 

the Inyo subregion.
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Figure 23—Projected change in streamflow at approximately 70 percent (~800.00 ha) of the Owens watershed 

area was simulated. (A) Three of the watersheds that drain into the Owens River were included in the study area 

and are indicated on the map (a =18090101; b =18090102; and c =18090103). Points mark the U.S. Geological 

Survey gauges; pink outlines the Inyo National Forest boundary; green outlines the major watershed boundaries; and grey indicates the study area. Changes in the Owens River streamflow relative to 1950–2000 average 

(B) showing interannual variability and (C) 5-year running average. The CSIRO scenario suggests an increase 

in interannual variability, whereas a downward trend is apparent under the MIROC scenario.
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