United States Department of Agriculture / Forest Service

Rocky Mountain Research Station

General Technical Report RMRS-GTR-319

February 2014

Climate-FVS Version 2: Content, Users 

Guide, Applications, and Behavior

Nicholas L. Crookston 
Rocky Mountain Research Station

Publishing Services

Available only online at http://www.fs.fed.us/rm/pubs/rmrs_gtr319html

Crookston, Nicholas L. 2014. Climate-FVS Version 2: Content, users guide, applications, 

and behavior. Gen. Tech. Rep. RMRS-GTR-319. Fort Collins, CO: U.S. Department of 

Agriculture, Forest Service, Rocky Mountain Research Station. 38 p.

Abstract

Climate change in the 21st Century is projected to cause widespread changes in forest 

ecosystems. Climate-FVS is a modification to the Forest Vegetation Simulator designed to 

take climate change into account when predicting forest dynamics at decadal to century 

time scales. Individual tree climate viability scores measure the likelihood that the climate 

at a given location and at a given point in time is consistent with the climate recorded for 

species’ contemporary distribution. These viability scores are input into Climate-FVS. A 

web-based service is available for providing this input for climate predictions generated 

by down scaling general circulation model (GCM) outputs run using several models and 

scenarios from the IPCC third (IPCC 2000) and fifth assessments (IPCC 2013). Climate-FVS 

contains components that modify mortality and growth rates, plus rules for establishing 

new trees. Commands are presented that control the model. These commands enable the 

users to explore the model’s sensitivity to model components and parameters, to include 

pertinent information unknown to the model, and use the model to simulate management 

alternatives. Model outputs are very sensitive to the mortality component, are moderately 

sensitive to growth rate modifications, and are sensitive to maximum density adjustment 

only when a stand’s maximum density is being approached. The intended model uses are 

to provide insights into future forest dynamics that are not otherwise evident, to provide 

model outputs that are relevant to forest managers, to provide a consistent way to compare 

management alternatives, and to do so using defensible methods. 

About the author

Nicholas L. Crookston has been an Operations Research Analyst with the USDA-Forest 

Service since 1983. His central focus is the development and implementation of the Forest Vegetation Simulator (FVS). He also conducts scientific investigations to fill gaps in 

the FVS model. Recently that work has included modeling climate, climate-change, and 

species-climate profiles for western tree species. He has also developed software used 

to impute required inventory data to sites where these data are not available. He can be 

reached by Email at: ncrookston.fs@gmail.com

About the cover: 

The cover art conveys fact that global warming is occurring, there are creditable predictions as to its magnitude, and although there are important differences in scenarios and 

GCM predictions, the future will not be the same as the past (the top graph is part of 

figure TS-15 reprinted from Stocker and others 2013, p. 89). Climate-FVS, represented 

by the FVS logo, integrates climate predictions to provide site-specific estimates of forest 

dynamics, represented by the bottom graph (fig. 17, herein). 
Table of Contents

1. Introduction............................................................................................ 1

2. Model Content ....................................................................................... 2

2.1. Climate ..................................................................................... 3

2.2. Species Viability........................................................................ 4

2.3. Site Index.................................................................................. 7

2.4. Carrying Capacity ..................................................................... 9

2.5. Mortality ................................................................................. 10

2.6. Growth ................................................................................... 12

2.6.1. Genetic effects........................................................... 13

2.6.2. Growth modifier ........................................................ 14

2.7. Regeneration Establishment..................................................... 15

3. Users Guide ......................................................................................... 17

3.1 Keywords................................................................................. 17

3.2. Climate and Species Viability Data File ................................... 20

3.3. Data Available From the Moscow Forestry Sciences 

Laboratory Web Site ....................................................... 22

3.4. Output Reports ....................................................................... 23

4. Applications ......................................................................................... 26

4.1 West Cascades, Washington..................................................... 26

4.2 Northern Idaho ........................................................................ 28

4.2.1. Setting and no harvest run.......................................... 28

4.2.2. Management alternatives ........................................... 30

4.3 Western Colorado .................................................................... 31

4.3.1. Setting and no harvest run.......................................... 31

4.3.2. Management alternatives ........................................... 32

5. Sensitivity ............................................................................................. 33

6. Summary.............................................................................................. 34

Acknowledgments.................................................................................... 36

References................................................................................................ 36
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 1

1. Introduction

Climate is changing (Hansen 2010, Stocker and others 2013) and that change is 

changing forests (Rehfeldt and others 2006). How this driving force is used in 

prognoses of forest stand development in Climate-FVS (Crookston and others 

2010) is the primary purpose of this paper. Climate-FVS is an addition to the 

Forest Vegetation Simulator (Crookston and Dixon 2005; Dixon 2013 revision) 

that takes future climate projections as input and modifies the predictions for 

forest dynamics by including them in the calculations. To use this model proficiently requires knowledge of its scientific content and how it works.

Climate-FVS is designed to meet the same purposes as the core FVS system with 

the added benefit of taking climate change into account. The model’s central 

purpose is to provide users insights into how forests will change over time given 

alternative management actions. Using the model provides consistent, documented, 

and defensible methods for producing information useful to forest managers.

Climate-FVS currently applies to the western half of the contiguous United States 

(herein, “the West”). There are efforts underway to build a counterpart that covers 

the eastern half of the United States and indeed that model will likely contain 

some of the same features (Phil Radtke, personal communication). However, 

because it will cover different ecosystems than those in the West it will have 

different model content, options, and supporting documentation.

Many people hold the view that there is a great deal of uncertainty in predictions 

of future climate and that it is therefore not feasible to consider climate change 

in forest growth predictions. The base FVS model, used without the climate 

adjustments, predicts a future that is a reflection of climates that predominated 

the last half of 20th Century. That time frame is coincident with most of the 

measurements on which the model is based. The climates of the 21st Century 

are predicted to be warmer; assuming that they will not change is most likely 

wrong (IPCC 2013). While outputs from Climate-FVS may not turn out to be 

correct, ignoring climate change in prognoses of future forest species and size 

composition would misinform forest planning and forest management decisions.

While some detailed examples are presented below (sections 4 and 5), outputs 

from Climate-FVS tend to exhibit some general trends. First, since FVS starts 

with an inventory of existing site and tree measurements, there is generally no 

immediate effect of climate change on predicted stand dynamics. Within a few 

decades, climate-change induced mortality starts to ensue; first for species that 

are near the edge of their ranges and then for all species (1) because the new 

climates are not like those where the species currently exist, (2) because the new 

climate is simply markedly different than the current climate, or for both reasons. 

Mortality opens growing space that the model can fill with species adapted to 

the new climate. The impact is that existing trees are replaced with regeneration. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 2

As will be shown below, other effects of climate change—for example those that 

are related to changing growth rates—are of much less importance. 

The opportunities for management to adapt forests to climate change center on 

changing the timing of harvests and the selection of planting stock. Short rotations can be used to ensure adapted trees are on rapidly changing sites. Management actions can be timed so that the general trend of accelerated mortality 

is replaced by harvests and planting so that lands that can sustain forests in the 

future are indeed stocked with healthy growing trees.

Climate-FVS requires an input file of future climate information and accompanying tree species viability scores—a measurement of how consistent the new 

climate is with records of where trees of a specific species are known to exist 

today. Section 2, Model Content, includes details about where to get these data 

and how they are used. Section 3, Users Guide, covers the commands that control 

the model, input, and output. Section 4, Applications, presents examples that 

illustrate model use, including management approaches. Section 5, Sensitivity, 

briefly explores how sensitive the model outputs are to individual subcomponents. Lastly, Section 6, Summary, is a synthesis that includes identification of 

caveats and key findings.

2. Model Content

Crookston and others (2010) presented all of the components of the first version of Climate-FVS, hereafter called version 1. For the sake of completeness, 

some of that information is repeated in this report, along with model updates 

applicable to version 2. However, some of the discussion and scientific rationale 

for the approaches presented in the 2010 paper are omitted yet remain relevant.

Climate-FVS is designed to be sensitive to location. It starts with inventory 

information that represents conditions at a specific place. It also uses input generated from a climate model that provides estimates of climate that are specific 

to the location as defined by longitude, latitude, and elevation. 

Key facts about the model: (1) Climate-FVS represents general trends, and 

(2) changes in the variance in climatic conditions are not directly represented. 

Climate-FVS does not attempt to model exactly what will kill trees or exactly 

when they will die. For example, while the model may predict the demise of 

lodgepole pine, it does not simulate mountain pine beetle outbreaks or fires that 

may indeed become the causes. There are other modules in FVS that can be 

used to represent these ecosystem components. While there is evidence that the 

extremes may have a large influence on ecosystems, this model is based solely on 

changes in average conditions yet it still predicts huge impacts. If forest change is 

in fact driven primarily by climate extremes in addition to changes in averages, 

then the model projections of climate-caused impacts are probably conservative.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 3

2.1. Climate

Climate-FVS requires input that defines current and future climate. Estimates 

are provided using spline climate surfaces (ANUSPLIN; Hutchinson 2004) of 

monthly averages (called normals) of mean, maximum, and minimum temperature and precipitation for point locations. The surfaces are continuous functions 

of latitude, longitude, and elevation. They provide potentially unique values for 

specific combinations on these axes rather than raster grids that provide the same 

value for all locations within a grid cell, as done in many climate models (e.g., 

Daly and others 2008). The spline climate estimates (http://forest.moscowfsl.

wsu.edu/climate) include algorithms to generate from monthly means 35 variables with more direct relevance for plant ecophysiology, such as mean annual 

temperature and precipitation, degree days above 5 °C, degree days below 0 °C, 

the length of the frost period, and interactions such as annual dryness index, 

which reflects the balance between growing season warmth and precipitation.

Contemporary climate (the 1961-1990 climate normal period) is used as a base 

period because (1) this period mostly predates the beginning of accelerated climate change, (2) the data needed to compute the averages for this period were 

readily available and (3), much of the growth and mortality data used to calibrate 

components of the base FVS model and the species climate relationships are 

coincident with this period. The future climates are based on a downscaling of 

Global Circulation Models (GCMs), computed using a delta method (Daniels 

and others 2012; also see http://forest.moscowfsl.wsu.edu/climate/future/details.

php). This method essentially involves adding the change (the deltas) in climate 

predicted by the outputs for the GCMs to the observed climate values used to 

build the contemporary climate surface. The updated observations are then used 

to build new surfaces that reflect the climate change.

In version 1, climate data and GCM simulations that corresponded to seven 

combinations of three GCM model outputs and three scenarios from the Special Report on Emission Scenarios (SRES) identified in table 1 were provided. 

These GCM outputs provided the climate projections used in the 3rd assessment 

report of the Intergovernmental Panel on Climate Change (IPCC 2000). For version 2, new GCM outputs, those used in the fifth assessment report (AR5, IPCC 

2013) are used. These GCM runs are based on a different set of scenarios called 

Representative Concentration Pathways (RCPs), which are four greenhouse gas 

concentration trajectories adopted by the IPCC. Three of the four RCPs were 

processed for use in Climate-FVS: rcp45, rcp60, and rcp85. They are named 

after the three radiative forcing values in the year 2100: 4.5, 6.0, and 8.5 W/m2 

(vanVuuren and others 2011).

Of the available model runs (table 1), rcp60 is highlighted in this work as it 

roughly corresponds to SRES A2 and seems most relevant to study. Furthermore, 

in preparation for version 2, an ensemble of 17 GCM models were run in addition 

to three specific GCMs. The ability to generate predictions for specific locations 

is provided at http://forest.moscowfsl.wsu.edu/climate/customData/fvs_data.php. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 4

Three future time points are addressed: the 10 years surrounding 2030, 10 years 

surrounding 2060, and 10 years surrounding 2090. In Climate-FVS, points in 

time between those periods are linearly interpolated. As pointed out in section 1, this approach has low temporal resolution and ignores the within- and 

between-year fine-scale variation in weather and climate. On the other hand, 

the approach offers reasonably high spatial resolution whereby differences in 

climate along steep elevation gradients are reasonably represented. 

2.2. Species Viability

Climate is the most important single factor defining species ranges. Rehfeldt 

and others (2006, 2009) developed methods to relate presence and absence 

observations derived mostly from Forest Inventory Data (FIA; Bechtold and 

Patterson 2005) to contemporary climate. A regression and classification algorithm called Random Forests (Breiman 2001; as implemented in R by Liaw and 

Wiener 2002) was used to generate sets of classification trees, where each set 

can be used to estimate the likelihood that the climate at a location is suitable 

to the species. We interpret this likelihood as a continuous zero-to-one index of 

the species’ viability in the climate at a given location and hereafter refer to it 

as the viability score. Values near zero indicate that the climate is not suitable 

for the species, while values near one indicate that the climate is consistent with 

species presence. 

Table 1. GCM model/scenario names. 

Short name Version GCM group name and scenario identification

CGCM3_A1B 1 Canadian Center for Climate Modeling and Analysis, scenario A1B

CGCM3_A2 1 Canadian Center for Climate Modeling and Analysis, scenario A2

CGCM3_B1 1 Canadian Center for Climate Modeling and Analysis, scenario B1

GFDLCM21_A2 1 Geophysical Fluid Dynamics Laboratory, scenario A2

GFDLCM21_B1 1 Geophysical Fluid Dynamics Laboratory, scenario B1

HADCM3_A2 1 Hadley Center/Met Office (UK), scenario A2

HADCM3_B2 1 Hadley Center/Met Office (UK), scenario B2

CCSM4_rcp45 2 The Community Earth System Model, scenario rcp4.5

CCSM4_rcp60 2 The Community Earth System Model, scenario rcp6.0

CCSM4_rcp85 2 The Community Earth System Model, scenario rcp8.5

GFDLCM3_rcp45 2 Geophysical Fluid Dynamics Laboratory, scenario rcp4.5

GFDLCM3_rcp60 2 Geophysical Fluid Dynamics Laboratory, scenario rcp6.0

GFDLCM3_rcp85 2 Geophysical Fluid Dynamics Laboratory, scenario rcp8.5

HadGEM2ES_rcp45 2 Hadley Center/Met Office (UK), scenario rcp4.5

HadGEM2ES_rcp60 2 Hadley Center/Met Office (UK), scenario rcp6.0

HadGEM2ES_rcp85 2 Hadley Center/Met Office (UK), scenario rcp8.5

Ensemble_rcp45 2 Our ensemble of 17 AR5 model predictions, scenario rcp4.5

Ensemble_rcp60 2 Our ensemble of 17 AR5 model predictions, scenario rcp6.0

Ensemble_rcp85 2 Our ensemble of 17 AR5 model predictions, scenario rcp8.5
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 5

Table 2. Species list and summary of Random Forest predictors for Climate-FVS in the western United States.

Species

Plant 

code Common name

Number 

presenta

Commission 

errorb

Omission 

errorb

Lower 

viabilityc

Abies amabilis ABAM Pacific silver fir 4106 0.0539 0.0008 0.5936

Abies concolor ABCO White fir 8692 0.052 0.0017 0.5653

Abies grandis ABGR Grand fir 8220 0.066 0.0008 0.5723

Abies lasiocarpa ABLA Subalpine fir 11294 0.0748 0.0006 0.5913

Abies lasiocarpa var. 

arizonica ABLAA Corkbark fir 370 0.0454 0.0013 0.5463

Abies magnifica ABMA California red fir 1248 0.0668 0.0018 0.5516

Abies magnifica var.

shastensis ABSH Shasta red fir 600 0.0673 0.002 0.5358

Abies procera ABPR Noble fir 1522 0.0962 0.0014 0.5544

Acer glabrum ACGL Rocky Mountain 

maple 712 0.0818 0.0054 0.4873

Acer grandidentatum ACGR3 Bigtooth maple 348 0.0657 0.0012 0.5544

Acer macrophyllum ACMA3 Bigleaf maple 3616 0.1436 0.0011 0.5497

Aesculus californica AECA California buckeye 228 0.1804 0.0015 0.5515

Alnus rhombifolia ALRH2 White alder 208 0.1455 0.0018 0.5487

Alnus rubra ALRU2 Red alder 4882 0.1211 0.001 0.5594

Arbutus menzeisii ARME Pacific madrone 3098 0.0826 0.0008 0.5753

Betula papyrifera BEPA Paper birch 302 0.0789 0.0019 0.5535

Betula papyrifera var. 

commutata BEPAC Western paper birch 328 0.0379 0.0004 0.593

Calocedrus decurrens CADE27 Incense cedar 4868 0.1119 0.0017 0.5508

Castanopsis chrysophylla CHCH7 Golden chinkapin 1810 0.092 0.001 0.5654

Cercocarpus ledifolius CELE3 Curlleaf mountainmahogany 1192 0.0872 0.0012 0.5551

Chamaecyparis 

lawsoniana CHLA Port-Orford-cedar 6152 0.0243 0.0007 0.6056

Chamaecyparis 

nootkatensis CHNO Alaska cedar 472 0.1072 0.0024 0.5299

Cornus nuttallii CONU4 Pacific dogwood 1050 0.1435 0.0021 0.5389

Fraxinus latifolia FRLA Oregon ash 206 0.1321 0.0032 0.5343

Juniperus deppeana JUDE2 Alligator juniper 3046 0.0547 0.0021 0.5657

Juniperus erythrocarpa JUCO11 Redberry juniper 84 0.223 0.0021 0.5607

Random Forest models are available for the most important forest tree species 

that occur in the western conterminous United States (table 2). Predictions are 

generated for specific locations for current conditions and for future climates 

using http://forest.moscowfsl.wsu.edu/climate/customData/fvs_data.php. As 

discussed in section 3, these data are entered into Climate-FVS along with the 

climate information. They are used in several ways as described below. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 6

Species

Plant 

code Common name

Number 

presenta

Commission 

errorb

Omission 

errorb

Lower 

viabilityc

Juniperus monosperma JUMO Oneseed juniper 3866 0.0717 0.0012 0.5858

Juniperus occidentalis JUOC Western juniper 3152 0.0574 0.0007 0.5681

Juniperus osteosperma JUOS Utah juniper 9262 0.0804 0.0017 0.5573

Juniperus scopulorum JUSC2 Rocky Mountain 

juniper 3378 0.1297 0.0019 0.549

Larix lyallii LALY Subalpine larch 102 0.1178 0.0033 0.524

Larix occidentalis LAOC Western larch 9094 0.051 0.0006 0.5996

Lithocarpus densiflorus LIDE3 Tanoak 2158 0.0622 0.0009 0.5643

Olneya tesota OLTE Tesota-Arizona 

ironwood 110 0.0941 0.0046 0.5206

Picea breweriana PIBR Brewer spruce 348 0.0419 0.0014 0.5581

Picea engelmannii PIEN Engelmann spruce 10460 0.0847 0.0007 0.585

Picea pungens PIPU Blue spruce 328 0.1264 0.0032 0.5261

Picea sitchensis PISI Sitka spruce 946 0.0932 0.0021 0.5262

Pinus albicaulis PIAL Whitebark pine 3112 0.0491 0.0005 0.5876

Pinus aristata PIAR Bristlecone pine 162 0.0791 0.0004 0.5771

Pinus attenuata PIAT Knobcone pine 388 0.0943 0.0011 0.5464

Pinus contorta PICO Lodgepole pine 15386 0.0863 0.0005 0.5902

Pinus coulteri PICO3 Coulter pine 116 0.1476 0.0012 0.5473

Pinus edulis PIED Pinyon pine 9102 0.0736 0.0015 0.5729

Pinus flexilis PIFL2 Limber pine 1938 0.0982 0.001 0.5623

Pinus jeffreyi PIJE Jeffrey pine 2394 0.0898 0.0015 0.5542

Pinus lambertiana PILA Sugar pine 4032 0.1099 0.0013 0.5565

Pinus monophylla PIMO Singleleaf pinyon 3430 0.0531 0.0005 0.595

Pinus monticola PIMO3 Western white pine 3726 0.0894 0.001 0.5612

Pinus ponderosa PIPO Ponderosa pine 24280 0.0802 0.0014 0.5671

Pinus strobiformis PIST3 Southwestern white 

pine 560 0.0663 0.0065 0.4381

Populus deltoides ssp.

monilifera PODEM Plains cottonwood 76 0.1343 0.0009 0.5776

Populus tremuloides POTR5 Quaking aspen 6196 0.0804 0.0014 0.5527

Prosopis sp. PROSO Mesquite 264 0.1343 0.0011 0.5623

Prunus sp. PRUNU Cherry 724 0.0876 0.0015 0.5446

Psuedotsuga menziesii PSME Douglas-fir 39490 0.0788 0.001 0.58

Quercus agrifolia QUAG Coast live oak 440 0.1054 0.0024 0.5412

Quercus chrysolepis QUCH2 Canyon live oak 3310 0.1242 0.0011 0.558

Quercus douglasii QUDO Blue oak 778 0.1368 0.0017 0.5419

Quercus emoryi QUEM Emory oak 758 0.092 0.0012 0.555

Table 2. Continued
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 7

2.3. Site Index

Site index is a commonly used measure of the ability of a site to produce wood 

(Monserud 1984). Ideally, it is a species-specific height at a base age reached 

by dominant trees that have always grown without competition. Site index is 

known to be a function of climate (see Monserud and Rehfeldt 1990). Climate 

explained about 25 percent of variation in site index of lodgepole pine (Pinus

contorta var. latifolia) in Alberta, Canada (Monserud and others, 2006, 2008). 

In general, high site indices are correlated with long growing seasons and warm 

temperatures, provided that moisture is sufficient. Monserud and others’ showed 

that lodgepole pine site indices are altered by a change in climate.

Because FVS uses site quality, often measured by site index, to estimate tree 

growth, Climate–FVS requires a function relating site quality to climate that is 

applicable to all forest types and their ecotones and to non-forest across all of 

the western United States. To provide such a function, we defined S to be the 

proportionate change in site index caused by a change from one climate (called 

C1) to another (called C2), where Ci is a vector of climate metrics like those used 

to measure the viability scores.

Table 2. Continued

Species

Plant 

code Common name

Number 

presenta

Commission 

errorb

Omission 

errorb

Lower 

viabilityc

Quercus gambelii QUGA Gambel oak 4118 0.0609 0.001 0.5732

Quercus garryana QUGA4 Oregon white oak 1116 0.116 0.0017 0.5528

Quercus hypoleucoides QUHY Silverleaf oak 218 0.07 0.0027 0.54

Quercus kelloggii QUKE California black oak 3500 0.0982 0.0013 0.5513

Quercus lobata QULO Valley California 

white oak 150 0.1847 0.0025 0.5232

Quercus oblongifolia QUOB Mexican blue oak 106 0.146 0.0028 0.5283

Quercus wislizeni QUWI2 Interior live oak 752 0.1478 0.0019 0.5332

Robinia neomexicana RONE New Mexico locust 294 0.1152 0.0153 0.3716

Salix sp. SALIX Willow 456 0.081 0.0024 0.5359

Taxus brevifolia TABR2 Pacific yew 2106 0.1073 0.0014 0.5397

Thuja plicata THPL Western redcedar 6816 0.117 0.0006 0.574

Tsuga heterophylla TSHE Western hemlock 9992 0.0893 0.0012 0.5602

Tsuga mertensiana TSME Mountain hemlock 2796 0.0709 0.0013 0.5569

Umbellularia californica UMCA California-laurel 1034 0.1162 0.001 0.5601

a The number of observations where the species was present.

b Commission error is predicting the species is present when it is absent while omission error is predicting that it is absent when it 

is present.

c 99.5 percent of the viability scores are larger than this number among observations where the species is present.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 8

Let f be a function of Ci that predicts the site index, or at least a number that is 

proportional to the site index; then S = f(C2)/f(C1). Note that f(C1) > 0 because 

FVS is initiated with sites that are suitable for forests. To construct f, we used 

the FIA collection of site trees for the western United States, in which 82,649 

observations of height and age are spread over 21,553 plots in forested lands. 

Approximately 39 percent of the observations in this dataset were Douglas-fir 

(PSME), but the remainder included 61 other species. Calibration data consisted 

of a random sample of 40,000 observations drawn without replacement from the 

full data set. To represent climates where there are no trees, 5000 points were 

randomly selected from lands in the western United States that are not capable 

of supporting forests. 

Estimating site index for each tree was hampered by the several disparate regional 

models that use different base ages and a variety of model forms. This problem 

was circumvented by using Monserud’s (1984) model that was calibrated for 

Douglas-fir to estimate a site index for each tree (Crookston and others 2010). 

This equation was used for all species despite the well-known differences in 

growth rates among species. Because Climate–FVS uses the ratio S instead of 

actual site index, bias introduced from using a single site curve for all species is 

alleviated, while the noise associated with disparate site curves and underlying 

techniques is avoided. 

Site index is not the only determining factor of growth and therefore S is not 

used directly as a multiplier. The function depicted in figure 1 is used to scale 

S to compute a site-index related growth multiplier. This multiplier is used to 

represent shifts in site productive potential and is used in all variants, even those 

that do not directly use site index to measure productivity. 

Figure 1. The site-index growth multiplier as a function of S, 

the proportionate change in site index.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 9

2.4. Carrying Capacity

In FVS, carrying capacity is measured as a stand maximum basal area and as 

a maximum stand density index (Reineke 1933)—an internal formula converts 

one to the other so that both are always defined. Changes in these maximum 

stand densities are computed by FVS over time by calculating a weighted average maximum density among the species growing in the stand. Each species is 

given a default maximum density used in this calculation. Weights used in the 

calculation of the weighted average are the basal areas of the species present. 

This weighted average establishes the stand maximum in effect at a given point 

in simulated time.

Climate-FVS modifies the maximum carrying capacity computed by FVS by 

calculating a proportional change from two weighted average maximum densities. For the first of the two values, denoted D1, the weights equal the species 

viability scores at the beginning of the simulation period. For the second value, 

D2, the weights are the viability scores computed for the specific point in simulated time to which the modifier applies. As for computing S (section 2.3), D1 

is always greater than zero because FVS is initiated with data from sites that 

can support forests. The proportional change in carrying capacity is r = D2/D1. 

According to this logic, maximum stand density will increase when the site 

becomes more suitable for species that carry high densities, but will decrease 

when the climate favors species that carry lesser density. This approach leaves 

two contingencies to address: the weighted average maximum density and the 

proportional change in density. 

First, the weighted average maximum density (D2) could be high even though 

viability scores are all low. For example, if viability scores of all species were 

less than 0.2 but their maximum densities were high, then the weighted average 

maximum density would be high because weighted averages are relative to the 

sum of the weights. To address this issue, an additional entry was made into the 

calculation of the weighted average maximum density to represent non-forests. 

This entry was given a carrying capacity of zero and a weight of one minus the 

maximum viability among the species. Secondly, the proportional change in 

density (r) could become extraordinarily high if the denominator in the ratio is 

very small compared to the numerator. To circumvent this potential problem, 

the magnitude of r is limited to 1.5.

The modified carrying capacity is used by FVS in its usual mortality calculations. As a stand approaches maximum density, density-dependent mortality 

ensues. It is also often used in specifying rules for thinning whereby a thinning is 

scheduled based on the stand density relative to the maximum. In Climate-FVS, 

the automatic establishment feature is also sensitive to this value (section 2.7). 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 10

2.5. Mortality

In Climate–FVS, mortality is a function of species viability scores described 

in section 2.2 and denoted as MV and change in climate denoted as MdC. The 

logic supporting MV is that if viability scores drop below those at which the 

species occurs currently, mortality rates increase, eventually resulting in extirpation. Figure 2a illustrates the relationship between viability and mortality, 

plotted in the figure as survival. Experience in developing climate profiles of 

various species (e.g., Rehfeldt and others 2006) has shown that species rarely 

occur when viability scores are less than 0.5. Indeed, most sites where Pacific 

silver fir (ABAM) occurs receive viability scores greater than 0.9 (fig. 2b), while 

essentially no trees occur at viability scores less than 0.4. Table 2 shows that 

the histogram for Pacific silver fir is typical of many species; of the sites in 

which a species is present, about 60 percent had viability scores above 0.9 and 

99.5 percent had viability scores above 0.55.

Figure 2. Representation of the logic governing a rule-based 

relationship between species viability scores and 10-year 

survival (A), and a histogram of the observations in which 

Pacific silver fir is present that supports the logic (B).
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For Climate–FVS, we assume that at viability scores less than 0.2, a species is 

absent and, therefore, survival is zero. For lack of information to the contrary, 

we use a linear relationship to describe the decline in mortality between scores 

of 0.5 and 0.2 (fig. 2a). A comparison of figure 2a with figure 2b suggests that 

the mortality function in figure 2a overestimates survival. Note that no attempts 

are made to apportion mortality into causes, such as insect outbreaks, diseases, 

or climate-induced stress.

Experience using version 1 has led to improvements in version 2. In some 

cases, the Random Forest models that predict species viability commit errors of 

omission. They predict extremely low scores given contemporary climate even 

though the input tree inventory data indicates that a species is indeed present. 

To address this issue, an additional feature was added to scale the relationship 

of figure 2a. The scaling logic essentially slides the curve to the left such that 

100 percent survival is predicted for the viability score computed using contemporary climate. Mortality estimated using this model, therefore, ensues if 

and when species become less viable in the future. The component of the model 

dealing with carrying capacity (section 2.4) is not adjusted.

The second factor MdC, also new in version 2, is independently computed for each 

tree, and is based on climate change. The proposition is this: if climate changes 

at a location more in magnitude than is equivalent to changing elevation 300 m 

(~1,000 ft), then trees start to die, even if their viability score stays high. This 

mortality factor is herein called the dClim rule because it is based directly on 

changing climate. The rule ramps up the 10-yr mortality rate to a maximum of 

90 percent; it reaches 50 percent when the magnitude of change is equivalent to 

twice as much climate change as expected in 300 m (fig. 3) of climate change. 

Figure 3. Ten-year mortality 

as a function of climate 

change that is equivalent 

in magnitude to changing 

elevation 300 m. 
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The choice of 300 m is made because it is roughly the elevation range of a seed 

zone (Rehfeldt 1994); trees growing beyond their adaptive range experience 

higher mortality rates. Model outputs from a landscape located in the western 

Cascade Mountains, Washington (presented by Crookston and others 2010) 

motivated the addition. Those simulations allowed Douglas-fir to persist on the 

landscape in the face of large changes in climate. Indeed, the climate remained 

within that tolerance for Douglas-fir as a species, but not for the population of 

Douglas-fir that was adapted to the site at the beginning of this century. 

The magnitude of climate change is computed by comparing the value of six 

climate metrics for the year a given tree was born to those that correspond to 

a specific year in a simulation. Trees entered into the model without ages are 

all tagged as being born when the simulation starts. Trees established during 

the simulation are tagged with the birth year according to rules set in the Regeneration Establishment Model (see Dixon 2013 revision). This logic implies 

that trees that become established are adapted to the climate of the stand at the 

time of establishment.

The average of the proportionate change in the six variables measures the magnitude of change in figure 3. The six variables are: mean temperature of the 

warmest and coldest months, degree days above 5 oC (dd5), degree days below 

0 oC, mean annual precipitation times dd5, and summer dryness index computed 

as (gsdd5/gsp).5 where gsdd5 is dd5 computed for only a few months during 

the summer and gsp is precipitation in the corresponding period. 

The two estimates of mortality MV and MdC are reconciled with the rate FVS 

computes assuming no climate change (except for changing carrying capacity, 

section 2.4). If the estimate from FVS is higher than that computed by ClimateFVS, then the rate from the base model is used instead. This can happen when 

one of the other extensions is being used, such as the Fire and Fuels Extension 

(Rebain and others 2009) or one of the insect or disease extensions, which 

sometimes estimates very high mortality rates. 

2.6. Growth

To address the effects of a changing climate on growth rates, Climate–FVS 

modifies the growth estimate of FVS. The modifier is multiplicative and is denoted as PS, where the subscript indicates species specificity. There are three 

parts to the logic used to compute this modifier. The first part addresses the 

change in site quality, S, as defined in section 2.3. The second part addresses the 

expectation that living trees whose viability is decreasing should exhibit declining growth rates (see Rehfeldt and others 1999, 2001). For these trees, we added 

a species-specific viability, denoted by VS and set equal to the survival rate for 

the species (fig. 2a). The third part (GS) codes the adaptedness of trees as the 

climate changes and reflects intraspecific responses to a change in climate. Of 

these three effects, GS requires elaboration before deriving PS from S, VS and GS.
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2.6.1. Genetic effects

It is well known from provenance tests conducted for most of the world’s widespread tree species that trees grown from various seed sources exhibit different growth rates, but the expression of these differences depends on the local 

environment. These tests provide the best source of data for estimating change 

in growth associated with a change in the climate.

Re-analyses of common garden data (see Rehfeldt 1989) by Leites and others 

(2009, 2012) quantified Douglas-fir height growth as a function of climate 

metrics at the seed source and the difference in climate between the planting 

site and seed source. An update to version 1 replaces the earlier Douglas-fir 

climate-transfer model (Leites and others 2009) with the final model published 

by Leites and others (2012; fig. 4, left). This model was fit to height growth data 

from common gardens and predicts 3-year height growth as a function of mean 

temperature in the coldest month (mtcm) at the seed source and the difference 

between mtcm at the seed source and planting site. Two steps were taken to 

construct the model for use in FVS (fig. 4, right). The first step was to replace 

space with time; seed source location and planting site location were replaced 

with birth year and the current year of the simulation. The second step was to 

transform the model to compute a proportionate change in growth (GS) so that 

if there is no climate change the growth multiplier is 1.0 yielding the growth 

FVS would otherwise estimate for the trees. 

Figure 4. Left illustrates the climate transfer function for Douglas-fir from Leites and others (2012), and 

right illustrates the function used in Climate-FVS to compute proportion of height growth (MTCM is mean 

temperature of the coldest month).
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The function in figure 4 indicates that increasing winter temperatures would 

initially benefit trees growing where winters are cold but otherwise would cause 

a reduction in growth. Reducing winter temperatures provides a growth decrease. 

In Douglas-fir, clines relating genetic differences among seed sources to environmental gradients are relatively steep, with differences in growth potential 

occurring at relatively short intervals along climatic gradients. Seed sources 

tend to be genetically attuned to relatively specific environmental conditions 

and, under those conditions, are capable of expressing their growth potential (see 

Morgenstern 1996). Rehfeldt (1994) used the term specialist to refer to species 

like Douglas-fir in which clines are steep. In species with a generalist approach 

to adaptation (e.g., western white pine, PIMO3), clines tend to be flat; seed 

sources are capable of expressing their growth potential across a broad range 

of environments. Obviously, specialists and generalists require different sets 

of response functions to describe the relationship between growth and climate.

Similar, yet preliminary, response functions have been developed for western 

larch (LAOC) and ponderosa pine (PIPO; Leites 2009a,b), two species in which 

clines in genetic attributes are moderately steep. In western larch, growth is most 

sensitive to changes in winter temperature, while in the pine growth is most 

sensitive to changes in moisture index. In general, small changes in climate tend 

to cause either little effect or moderate increases in growth of existing populations, while large climate changes, positive or negative, always reduce growth.

Although models like these are not available for many species, some species in 

the western United States besides Douglas-fir have broad geographic distributions with steep clines, such as lodgepole pine (see Rehfeldt 1994). As a result, 

we use the values of GS, which were calibrated for Douglas-fir, for lodgepole 

pine. For generalists like western white pine, Engelmann spruce (PIEN), and 

western hemlock (TSHE), for which clines tend to be flat, we use the values 

for western larch. In version 1, for all species for which geographic patterns of 

genetic variation are poorly documented or unknown, GS is 1.0. In version 2, 

the average from the three species models is computed and it is applied to all 

other species. However, also in version 2, a cap of 3 times the base growth rate 

is imposed.

2.6.2. Growth modifier

With S, VS, and GS all defined, the growth modifier, PS is chosen by the logic: 

PS = min(S, VS, GS) if min(S, VS, GS) < 1.0, and 

PS = max(S, VS, GS) otherwise. 

The rationale is that if nothing is limiting growth, then the factor that results in 

the most growth is working in the ecosystem. Growth decreases if (1) the climate 

at the site becomes unsuitable to the species, (2) the site quality deteriorates, or 

(3) the seed source becomes maladapted to the climate.
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2.7. Regeneration Establishment

Version 2 uses the same three rules used in version 1 to estimate the establishment of new trees added to under-stocked stands. The rules consider species 

viability scores (section 2.2) and stocking; they assume that seeds are available 

for regeneration or that the trees will be planted. 

The first rule is that establishment will be initiated when stand density falls 

below a threshold set by default to 40 percent of full stocking. Full stocking 

corresponds to carrying capacity (section 2.4), and as a result, the density that 

constitutes full stocking will change as the climate changes. This default setting 

reflects choices made by many FVS users to simulate episodes of regeneration 

establishment that are usually dependent on disturbance. The second rule deals 

with the calculation of the number of trees to be established, which is initially 

set to a default of 500 trees/a (1235 trees/ha). From this maximum, the trees to 

be added are computed from the actual stocking and the viability scores of the 

species suited to the climate of the site. The proportion of the maximum number 

eligible for reforestation is determined by stocking levels (fig. 5a), that is, by a 

linear function between zero for the stocking threshold (as set by the first rule) 

and 1.0 when stocking is less than 25 percent of full stocking. According to 

figure 5a, which assumes a reproduction threshold of 50 percent of full stocking, 

a stand that is only 45 percent of full stocking would be allowed to receive only 

20 percent of the maximum number of trees that could be established.

Figure 5. Regeneration–establishment rules for determining the maximum number of trees to be established in relation 

to full stocking (A), the scaling of species-specific viability scores (B), and the proportion of target trees to be established 

as a function of the proportion of full stocking and the largest scaled score (C). Vertical hashed lines in A correspond to 

the three values (25, 35, and 45 percent) of full stocking illustrated in (C).
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The number of trees to be established also depends on species viability scores. 

To use viability scores for this purpose, they are scaled between values of zero 

(all scores less than 0.4) and one (scores greater than 0.8), as depicted in figure 5b. If the viability score is less than 0.4, then no trees will be established, 

regardless of allowable proportion; but, if viability is greater than 0.8, all of 

the allowable trees will be established. These threshold values were selected 

in order to be consistent with the occurrence of species (figure 1 and the lower 

viability thresholds in table 2). To compute the proportion of trees that will be 

established, the allowable proportion (fig. 5a) is multiplied by the scaled viability 

score of the species with the highest score (fig. 5b) to arrive at the proportion 

to be established (fig. 5c). For example, if the target is 500 trees/a, the proportion of full stocking is 0.35, and the maximum of the scaled viability scores is 

1.0, then the number of trees to be established is 300 trees/a. However, if the 

maximum scaled viability score is 0.6, the number to establish would be about 

150 trees/a, and if the score is 0.4 or less no trees would be established.

In the third rule, the trees to be established are allocated among species. To accomplish this, all species with scaled viability scores less than 0.40 are ignored. 

From the species remaining, a maximum of four (by default) species are selected 

according to their viability scores. The number of trees to be established is apportioned among the species using the scaled scores as weights; the proportion 

allocated is determined by the ratio of the scaled score for a species and the 

sum of the weights. Figure 6 provides four examples of applying these rules. In 

figures 6A and 6B the viability scores are the same, but because the percentage 

of full stocking differs, the numbers of trees to be established differ greatly. In 

figure 6C and 6D, the percentage of full stocking is low and equal, but species 

viability scores differ greatly. As a result, most of the trees to be established are 

those best suited for the climate of the site.

Figure 6. Four examples of applying the establishment rules, each illustrating responses for three species according 

to their viability score.
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3. Users Guide

3.1 Keywords

Climate-FVS is controlled using standard FVS keywords, see Dixon (2013 revision) 

for an explanation. As with other FVS extensions, the Climate keywords must 

be preceded with the Climate keyword and finish with the End keyword, that 

signal the start and end of the Climate-FVS keywords. The FVS User Interface 

(Suppose) will automatically add these keywords to the simulation, therefore 

there is no need to add them if you are using Suppose. Only one of the keywords 

is required: ClimData is used to signal Climate-FVS to read a data file to get 

necessary climate and species viability information. It is also used to specify 

which of several general circulation models (GCM) and scenarios will be used 

in the simulation. The other keywords control the establishment, growth, and 

mortality features of Climate-FVS allowing the model to be tuned as needed. 

Note that the arguments to four Climate-FVS keywords can be specified using 

the Parms feature of FVS (Dixon 2013 revision, p 157) as an alternative to 

using the fixed fields normally used to enter data using keywords. Those that 

support this feature are AutoEstb, GrowMult, MxDenMlt, and MortMult.

ClimData Signal that the climate and species-viability data be read from an 

external file. Climate-FVS reads this file and stores information 

for the current stand and for a specific GCM/scenario combination. 

This keyword must follow the specification of the stand identification in the keyword file. 

Supplemental data: 

There are two supplemental data records that follow the ClimData

keyword. The first is the short name (table 1) of the general circulation model (GCM) and scenario that is being run. Optional values 

for these entries depend on the contents of the climate-viability 

file (section 3.2). The second record contains the climate-viability 

file name. By convention this name is: FVSClimAttrs.csv but 

can be any other name and there is no default.

SetAttr Change the values for a single attribute from those in the climate attributes file to new values. This keyword must follow the 

ClimData keyword.

Field 1: A character string that matches one of the attributes in 

the climate attributes file. The string is case-sensitive 

and there is no default. 

Field 2: The value associated with the first row (year) of the data; 

blank values are interpreted as zeros.

Field 3: The value associated with the second row of the data; 

blank values are interpreted as zeros.
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Field 4: The value associated with the third row of the data; blank 

values are interpreted as zeros.

Field 5: The value associated with the fourth row of the data; 

blank values are interpreted as zeros.

AutoEstb Signal that Climate-FVS automatic establishment logic is turned on 

and that the base FVS automatic establishment features are turned 

off. If this keyword is not used, Climate-FVS establishment features 

are turned off. As stated above, this keyword supports using the 

Parms feature of FVS. 

Field 1: The FVS cycle number or the calendar year when automatic establishment starts, or when the values in fields 

2, 3, and 4 below are changed to new values. When this 

field is left blank the option takes effect immediately. 

Field 2: The stocking threshold, expressed as a percentage of full 

stocking. If stand stocking is below this value, ClimateFVS plants new trees. Default is 40 percent.

Field 3: The number of trees per acre that are planted. The species 

planted are determined by Climate-FVS and depend on 

the viability data. Default is 500 trees/a.

Field 4: The number of species to establish. Default is 4.

Note: The logic within Climate-FVS in addition to the fields 

specified by the AutoEstb keyword, generate the number of seedlings by species. Climate-FVS automatically 

inserts one or more of the Natural keywords into the 

simulation and then the Establishment Model adds new 

trees to the projection.

GrowMult Specify a species-specific adjustment to the magnitude of the 

growth-rate multiplier computed by Climate-FVS. The details of 

the implementation are defined below. Three examples illustrate 

how to use this adjustment: (1) code a zero to turn off the ClimateFVS impacts on growth; (2) code 0.9 if you want 90 percent of the 

Climate-FVS impact to be used (for example, a computed growth 

decrease of 10 percent would become a 9 percent decrease, and 

a computed increase of 10 percent would become a 9 percent increase); or (3) code 1.5 if you want the Climate-FVS impact to be 

150 percent of its original value (for example, a 10 percent growth 

decrease would become a 15 percent decrease, and a 10 percent 

increase would become a 15 percent increase). The growth rate 

adjustment is applied to the height growth for small trees and the 

diameter growth for large trees. Lastly, note that this keyword 

supports using the Parms feature of FVS.
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Field 1: The FVS cycle number or the calendar year when the 

multipliers are changed. When this field is left blank the 

multipliers are changed immediately.

Field 2: Species code to which the multiplier is applied. 

Default = All.

Field 3: The adjustment factor. Default = 1.0.

Details: Let xi be a growth rate multiplier applied to tree i, ci be 

the growth rate multiplier computed by Climate-FVS, and m be 

the value specified in field 3 of this keyword, then xi = 1 + ((ci – 1) m). 

Note that xi and ci are always bounded to >= 0. 

This logic is illustrated in figure 7. Note that a consequence of the 

logic is that for any value of m the growth adjustment actually applied to a given tree (xi ) will be less than one for any tree where 

ci is less than 1.0 and it will be greater than 1.0 for trees where ci 

is greater than one. The values of ci can be less than one for some 

trees and greater than one for others within the same FVS cycle. 

Figure 7. The logic used to scale the growth rate multiplier 

computed by Climate-FVS (ci

) that are applied to individual 

trees (xi

) given multiplier specified on the keyword (m). 

MortMult Specify two species-specific mortality multipliers. The first multiplier applies to the mortality rate related to species viability scores 

as in version 1.0 of Climate-FVS and the second is related to the 

new mortality component related to the magnitude of climate change 

as covered in section 2.5. Climate-FVS multiplies climate-caused 

mortality rates by these multipliers. It then compares these rates to 

the background rates computed by FVS for each tree and applies 

the highest rate. Therefore, you can turn off the climate-caused 

mortality features for a species by setting the corresponding multipliers to zero. Note that this keyword supports using the parms

feature of FVS.
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Field 1: The FVS cycle number or the calendar year when the 

multipliers are changed. When this field is left blank the 

multipliers are changed immediately.

Field 2: Species code to which the multiplier is applied. Default 

= All.

Field 3: The multiplier applied to viability-related mortality, blank 

implies no change to existing values. Default = 1.0.

Field 4: The multiplier applied to mortality due to climate change 

magnitude (dClim rule). A blank implies no change to 

existing values. Default = 1.0.

MxDenMlt Specify an adjustment of the maximum density multiplier computed by Climate-FVS. The details of the implementation follow 

the pattern for GrowMult, including Parms feature support. 

Field 1: The FVS cycle number or the calendar year when the 

multiplier is changed. When this field is left blank the 

multiplier is changed immediately.

Field 2: The adjustment factor, blank implies no change. 

Default = 1.0.

ClimRept Generates the Climate-FVS output report (as described in section 3.4).

3.2. Climate and Species Viability Data File

This section describes the general specifications for the climate and species 

viability data file. Following the specifications, a source for files that comply 

with these specifications is presented. Climate-FVS does not require users to 

use the source specified and it is possible for users to edit the data from the 

source prior to using that data in Climate-FVS. Indeed, a user can supply new 

information or edit the names of the scenarios, the climate data, the viability 

data, or any of these in combination so long as the requirements of the general 

specification are met. In addition, the SetAttr keyword can be used to modify 

values after they are read. 

Version 2 of Climate-FVS uses additional data items when compared to version 1.0. Note that if you use a version 1.0 file with the version 2, features of 

the model that require the additional data will be turned off. For example, if the 

values that measure the magnitude of climate change corresponding to 300 m 

change in elevation are not present (the dClim rule), the mortality model (section 2.5) that relies on this information is not used. If you use a file generated 

for version 2 with a version 1 of Climate-FVS, the additional information in 

the file is ignored. 
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The climate and species viability data file contains sets of climate information for contemporary climate and for three points in future time, for a total of 

4 data records per set (table 3). Sets are labeled by the stand identification to 

which they apply and the short name of the GCM and scenario they represent 

(table 1). There can be many sets in the file. Logic in Climate-FVS scans the 

file for the stand being simulated and for the GCM and scenario desired, storing 

the information needed for the simulation. 

Table 3. Column names used in the Climate and Species Viability Data file; all temperature 

values are in centigrade and precipitation measures are in mm.

Column

identification Definition

Time stamp The first column of the data is the stand identification (case sensitive); 

however, starting in version 2, this column name field is used to 

convey the date and time the Climate-FVS data was created when 

the web-based method described in section 3.3 is used.

Scenario The scenario short name (see table 1)

Year The year to which the data record corresponds 

mat Mean annual temperature

map Mean annual precipitation 

gsp Growing season precipitation

mtcm Mean temperature of the coldest month

mmin Mean minimum temperature

mtwm Mean temperature of the warmest month

mmax Mean maximum temperature

sday Julian date of first frost-free day in the spring

ffp Number of frost-free days

dd5 Degree days above 5 degrees centigrade

gsdd5 dd5 accumulated within the frost-free period

d100 Julian date that dd5 reaches 100

dd0 Degree days below 0 degrees

smrpb Summer precipitation balance: (jul+aug+sep)/(apr+may+jun)

smrsprpb Summer/spring precipitation balance: (jul+aug)/(apr+may)

PlantCD1 Plant code for the first species (see table 2)

... Additional plant codes (see table 2)

PlantCDn Plant code for the nth species (see table 2)

pSite The predicted site index, used to compute proportionate change, see 

Crookston and others 2010, p. 1204.

DEmtwm Change in mtwm over a 300 m elevation gradient.

DEmtcm Change in mtcm over a 300 m elevation gradient.

DEdd5 Change in dd5 over a 300 m elevation gradient.

DEsdi Change in sdi over a 300 m elevation gradient.

DEdd0 Change in dd0 over a 300 m elevation gradient.

DEpdd5 Change in map times dd5 over a 300 m elevation gradient.
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Each time period is labeled with a year. Values associated with the earliest year 

are used when simulated time proceeds the earliest year found in the file and 

values associated with the latest year are used when simulated time exceeds 

the latest year in the file. Within the time span covered by the input data, linear 

interpolation is used to get values for simulated years that do not coincide with 

one of the four years specified in the file.

The file format must be comma-separated values (csv). The first line is a header 

that identifies the contents of the columns. Climate-FVS assumes that the order 

of the first three columns are as shown in table 3, but the other columns may 

be in any order so long as the column identifications are exactly as shown in 

table 3 (they are case sensitive and exact matches are made). 

Generally two kinds of information are present: climate data and species viability 

data. Short names for both are used to identify the contents of each column. The 

short names for the climate date are shown in table 3 and species abbreviations 

for the viability data are listed in table 2. Note that the list in table 3 is not exhaustive; columns for other species may be added and columns for species that 

are present at the site but whose presence should be ignored by the model can 

be removed from the file.

The species viability data are numeric scores between 0.0 and 1.0, where zero 

signifies that the species is not found in places with the corresponding climate 

and 1.0 signifies that the climate is consistent with places where the species is 

found. 

3.3. Data Available From the Moscow Forestry Science Laboratory 

Web Site

A source of the climate and species viability data file can be found at http://

forest.moscowfsl.wsu.edu/climate/customData/fvs_data.php. Instructions are 

present on the web site. In general, first prepare a file of location information 

for each stand, then send the file to the web site, receive the data back from the 

site, and make the file available to FVS to read. The location file specification 

is as follows: stand (or location) identification, longitude (decimal degrees), 

latitude (decimal degrees), and elevation in meters (see the web site for details). 

(The coordinate system of longitude and latitude is WGS84.)

Species viability scores are computed for the species listed in table 2. However 

only those species that have scores of more than 0.1 in any stand in your list, 

for any time period, and any climate scenario, are returned. 
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3.4. Output Reports

The effects of changing climate on growth, mortality, and regeneration are 

evident in the standard FVS output reports. Climate-FVS provides two outputs, 

one that displays the data read from the external climate and species viability 

data file (section 3.2), and another report designed to display how the model is 

working. The first of these outputs is generated when the ClimData keyword 

is used. Figure 8 is an illustration of that information, except that several lines 

and columns have been deleted from the figure so that it will easily fit in this 

document. 

Figure 8. Output generated in the FVS keyword table when the ClimData keyword is used.
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Figure 9. Climate-FVS output.

Climate metrics directly used by the model are marked with a tag *USED* while 

the others are reported for information only. In figure 8, for example, mat (mean 

annual temperature) is being used and mmax (mean maximum temperature in 

the warmest month) is not, although it may have been used in predicting species 

viability for some species. 

Species viability scores from the file are also reported. Those that are used by 

Climate-FVS are marked with the FVS internal species numeric code and alpha 

code. For example, in the case illustrated in figure 8, grand fir (ABGR) is tagged 

as being identified by Climate-FVS as FVS species 4 with alpha code GF. White 

fir (ABCO) is not recognized by the regional variant of FVS used in this example. 

At the end of the table, species that are recognized by FVS but have no viability 

data are listed. In this case, generic cottonwood (Plant code POPUL) as well as 

FVS species 22 and 23 (other hardwood and other softwood) have no viability 

data. If tree records are present in the FVS run coded with these species codes, 

the viability score is set to 1.0 for the entire time period. 

The second output report, created by including the ClimRept keyword, is 

illustrated in figure 9. Table 4 provides detailed information on the column 

headings. 
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Table 4. Column heading descriptions for the Climate-FVS output illustrated in figure 2.

Title Description

YEAR The year corresponding to the first year of the FVS cycle in which the 

output is generated.

SPEC The FVS species code.

SP VIA- The species climate viability score which ranges from 0 to 1. 

BILITY

BA/A The basal area per acre at the beginning of the FVS cycle 

TPA The trees per acre at the beginning of the FVS cycle

VIAB. The probability that a tree will die due to low viability scores (this is

MORT a 10-year rate, regardless of thelength of the cycle). This number will 

reflect values coded using field 1 of the MortMult keyword. Note 

that it does not display the base model mortality rate for the species 

and also that the value displayed will be applied during the model 

run only when it implies the highest mortality rate for trees of the 

given species. Also see the MXDEN MULT information below as it also 

affects mortality. 

dCLIM The probability that a tree will die due to climate change

MORT corresponding to the amount of change expected with an elevation 

change of 300 m. The actual rate may be different for each tree 

because it is based on the year the tree was born. What is reported 

here is the basal-area-weighted average rate for trees of a given 

species. Values reported here reflect multipliers coded using field 2 

of the MortMult keyword. Also see the MXDEN MULT information 

below as it also affects mortality.

GROWTH The proportion of FVS growth that trees of this species will get. This

MULT number reflects values coded using the Climate-FVS GrowMult

keyword. 

SITE The growth rate multiplier related to changes in site index. Note that

MULT the value reported is after the Climate-FVS GrowMult keyword is 

applied. 

MXDEN A multiplier of base FVS maximum stand density. This number is a

MULT stand-level value and the same value is repeated for each species. 

Note that the value reported is after the Climate-FVS MxDenMlt 

keyword is applied. This option impacts FVS chiefly by changing the 

maximum density point which would trigger density-related mortality 

but it can also trigger harvest activities if they are dependent upon 

rules related to how close the stand is to its maximum carrying 

capacity. 

AUTOESTB This is the number of trees of a given species that would be inserted in

TPA to the FVS simulation if (1) the AutoEstb feature were turned on, and 

 (2) the density were low enough to trigger establishment.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 26

4. Applications

Crookston and others (2010) illustrated model behavior for three landscapes in the 

West that showed that climate change impacts on species- and size-composition 

as well as carbon loads compared to no climate change. The magnitudes vary 

depending on the climate change scenario and GCM model used as well as the 

initial inventory. In general, lessons learned for that paper remain valid even in 

light of changes in the GCMs and emission scenarios and model changes. While 

specific comparisons between the results in that paper and this one are not offered, the difference in the outputs between versions 1 and 2 of Climate-FVS 

can be traced to the introduction of the dClim rule (section 2.5). 

This section provides examples of how to use the model to meet practical purposes, while simultaneously illustrating model behavior. Additional information 

on model behavior is presented in section 5. Three stands are used, one each 

from the three areas used by Crookston and others (2010). However, only the 

Ensemble_rcp60 scenario (table 1) is used in these examples. 

4.1 West Cascades, Washington

We start with a predominately Douglas-fir stand growing on the Gifford Pinchot 

National Forest in southwestern Washington. The stand was inventoried in 2008, 

mean elevation is 3357 feet (1023 m), slope is low, and the Douglas-fir site index 

is 167. There are 230 small western hemlock trees/a (568 t/ha) over topped with 

about the same number of larger Douglas-fir and about 100 western redcedar 

trees/a (247 t/ha) and a few Pacific silver fir. The quadratic mean DBH is 6 in 

(15 cm), and there is 4036 ft3/a (282 m3/ha) total volume. 

Climate change predicted for this site using the Ensemble_rcp60 scenario includes about a 5 percent increase of annual precipitation over the century. More 

significantly, mean annual temperature rises from -0.5 °C to 3.1, degree days 

above 5 °C increase from 694 to 1266, and the frost-free period increases from 

54 days a year to 93.

Viability scores for several species are high for the entire century. Pacific silver 

fir (ABPR) is one that starts high and loses its viability over time at this location. For this stand, however, it is Douglas-fir that dominates now and into the 

future and it is this species that is highlighted here.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 27

Figure 10. Cubic volume plotted over time for three run options using the example stand from western 

Cascade Mountains, Washington. The solid line is standing volume and the dotted line shows total production 

which is the standing volume plus prior removals (note that 10,000 ft3/a is about 700 m3/ha).

The left side of figure 10 shows predicted volume over time for this stand. 

Note that the standing volume starts to decline at about 2060. This mortality is 

caused by the dClim rule described in section 2.5. When MdC becomes greater 

than one, climate has changed so much that the current stock is deemed to be 

not adapted to the site and starts to die. Trees of the other species die as well. 

However, because Douglas-fir remains viable, Climate-FVS simulates that 

Douglas-fir regeneration will become established. The model tags these new 

trees with the year they are established and subsequent climate change driving 

the dClim rule is based on the difference between the climate at a specific point 

in future time and the year the trees are established. The assumption, therefore, 

is that new trees are adapted stock. As you can see from studying figure 10, 

the stand begins to grow again and by the end of the simulation it is well on 

its way. A logical management option in this case is to harvest the stand prior 

to the ensuing mortality and thereby capture the mortality as yield and get the 

stand back into production sooner rather than later. The middle panel of figure 

10 shows the yield trajectory for this option. 

The third panel of figure 10 shows the yield for a no harvest run with the dClimcaused mortality factor turned off. Climate-FVS outputs are sensitive to this 

mortality component; this is a recurring theme of all the examples in sections 

4 and 5.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 28

4.2 Northern Idaho

4.2.1. Setting and no harvest run 

The second example is from the Clearwater National Forest, Idaho. That stand 

was inventoried in 2005, it is on a west-facing 20 percent slope, the elevation is 

5800 ft (1768 m), and the current habitat is recorded as grand fir/beargrass (code 

510, Abies grandis/Xerophyllum tenax). At the time of the inventory, the stand 

is predominately Douglas-fir, with some grand fir, subalpine fir, Engelmann 

spruce, and mountain hemlock (TSME). The quadratic mean diameter is 17 in 

(43 cm), there is 187 ft2/a (43 m2/ha) basal area, 5675 ft3/a (397 m3/ha) total 

volume, and the species have similar size distributions rather than one growing 

in the understory of the others. 

The climate data from the Ensemble_rcp60 scenario indicates about a 5 percent 

increase of annual precipitation, mean annual temperature rises from 2.7 °C to 

5.9, degree days above 5 °C increase from 911 to 1555, and the frost free period 

increases from 38 days a year to 95. 

Figure 11 illustrates which species are considered viable now and in the future. 

An interesting fact is that the initial viability score is very low for mountain 

hemlock (TSME) yet it is present in the inventory. This is an error of omission; 

the viability models used to generate viability predict that the climate is not suitable for this species when in fact it is recorded as present. Model rules described 

in section 2.5 automatically adjust for this fact and indeed the viability-related 

mortality rates for mountain hemlock are low at the beginning of the simulation 

but increase over time because the viability decreases. Viability-related mortality 

remains low for the remaining four species that are present at the initial inventory. However, mortality rates increase for all species due to the dClim rule. 

Figure 11. Species viability scores for (A), those species that are present in the initial inventory 

and (B) those that are not present but have greater than 0.5 viability scores at the end of the 21st

century (Northern Idaho example).
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 29

Figure 12. Trees per acre by species plotted for three simulations and two points in time (species codes 

are in table 2; Northern Idaho example). Note that 200 trees/a is 494 trees/ha. 

Figure 13. Volume per acre by species plotted for three simulations and two points in time for the 

Northern Idaho example. Note that 5000 ft3/a is 350 m3/ha.

The bar charts in figures 12 and 13 show the species composition in trees and 

volume for a no management simulation at 2045 and 2085. The figures show 

that Douglas-fir remains an important component of the species composition 

throughout the century, yet, as shown in figure 13, the volume plummets in the 

no-harvest simulation. This model behavior is due to (1) the mortality model 

killing Douglas-fir as discussed above, followed by (2) the regeneration model 

adding Douglas-fir trees. Recall from section 2.7 that species of high viability 

are added into the model and that furthermore, the trees are considered adapted 

to the location’s climate at the time of establishment. Additional species also 

become viable and are indeed introduced as the existing trees die.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 30

4.2.2. Management alternatives

The no harvest simulation indicates that the future is bleak for this stand, with 

standing volume rapidly declining after 2055 (fig. 14, left) as well as the forest 

productivity. Can something be done to improve this prognosis? Two timing 

alternatives for harvesting are considered. The first is to harvest in 2025 and 

the second delays the harvest until 2055, about the time mortality ensues. 

If maximum total production is the goal, then delaying harvest until climatechange induced mortality ensues is the best option. Harvesting in 2025 removed viable growing stock and replaced it with a new stand that was viable 

at the time of establishment. However, climate change continued, and the 

trees that were established soon after the 2025 harvest remained viable for 

about 50 years (ca. 2085), when they started to endure accelerated mortality. 

Indeed, the model output indicates that climate is projected to change so fast 

that trees will not live long enough to complete a rotation. Although delaying 

the harvest until 2055 indicated higher total production, it is likely that the 

same thing will happen to the stand established in the wake of that harvest. 

If the trees do not become merchantable ahead of the climate change, the 

indicated production will not be realized. Perhaps a fourth alternative could 

be contemplated: planting a species and genetic stock that can be expected 

to reach merchantable size in much shorter rotations. 

Figure 14. Standing volume and total production (dotted lines) for three simulations from the Clearwater, 

Idaho example.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 31

4.3 Western Colorado

4.3.1. Setting and no harvest run

The last example comes from the Gunnison National Forest of western Colorado. 

It is situated at 10,500 ft., slopes slightly to the west, and has an Engelmann 

spruce site index of 62 ft (19 m). Like the other examples, climate change predicted for this site using the Ensemble_rcp60 scenario includes about a 5 percent 

increase of annual precipitation over the century. Mean annual temperature rises 

from -0.5 °C to 3.1, degree days above 5 °C increase from 694 to 1266, and the 

frost free period increase from 54 days a year to 93. This stand was part of the 

Black Mesa example in Crookston and others (2010), but it is actually located 

north of that area.

Current stocking is about 600 subalpine fir and 350 Engelmann spruce trees/a 

(1483 and 865 trees/ha, respectively). The stand is dominated by large spruce 

that make up almost 90 percent of the 6770 ft3/a (474 m3/ha) at the inventory 

year of 2001. Species viability is generally high initially (fig. 15) for existing 

species but falls off quickly for spruce followed by subalpine fir. Aspen (POTR5) 

is viable for the life of the simulation but is not present in the initial inventory. 

Remarkably, Gamble oak (QUGA) becomes viable in the second half of the 

century. Not shown is corkbark fir (ABLAA), which briefly becomes viable but 

otherwise plays no important role in this stand’s trajectory.

Figure 15. Viability plotted over time for the 

western Colorado example.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 32

The first panel of figure 16 plots trees/a by species over time under a no management alternative. The plot shows that the two predominant species start to die 

quickly at about midcentury and that the model adds aspen, and small numbers 

of corkbark fir and Gamble oak trees. 

4.3.2. Management alternatives

The challenge for this stand is to keep it stocked with trees. Two approaches 

were tried, one was to harvest once in 2051 and the other was to harvest twice 

leaving a residual of about half of the basal area at each entry (figs. 16-17). 

The reasoning behind the first alternative was to salvage dead and harvest 

live trees at a time coincident with the accelerated mortality. This is followed by regenerating aspen and Gamble oak. The objective of the second 

alternative was to open the existing stand and to establish aspen prior to 

the projected loss, beginning in the 2030s, of the currently growing trees. 

Figure 16. Trees/a plotted over time for the western Colorado example run under three management alternatives. 

Note that 900 trees/a is 2223 trees/ha.

Figure 17. Standing volume and total production (dotted lines) plotted over time for three simulations of 

the western Colorado example. Note that 7500 ft3/a is 525 m3/ha.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 33

The reasoning was that this would ensure that some minimal stocking would 

exist throughout the century. Figure 16 clearly shows that the trees became 

established but figure 17 shows that the resulting actions did not notably 

improve the yield. Indeed, the minimum standing volume in the two entry 

approach was hardly more than letting the stand die on its own and regenerate after the mortality has occurred. The reason for this result is partly due 

to the dClim rule that played an important role in the results in the previous 

two examples. In this case, the aspen planted in early 2020 was not adapted 

for the entire period, resulting in the decline late in the century. 

5. Sensitivity

Figure 18 displays total volume plotted over time for each of the three examples 

run for nine cases. The purpose is to explore the sensitivity of the model to its 

key subcomponents. The cases were created using the keyword commands (section 3.1) to alternately turn off subcomponents and double their effectiveness. 

There are three panels each corresponding to the examples discussed in section 

4; the solid black lines repeat the “No harvest” options in those examples. That 

run is identified as Case M = 1 G = 1 D = 1 in the legend because the values for 

the keywords used are all 1.0 that signals that the corresponding Climate-FVS 

subcomponents should not be increased, as would be done by setting a multiplier 

to a value greater than one, or decreased as would be done using values less than 

one. The values were set using the MortMult keyword for M, the GrowMult

keyword for G, and MxDenMlt keyword for D. Note that MortMult allows 

users to independently adjust the viability score based and the dClim mortality 

components but in this case the same value of M was used for both components.

Figure 18. Yield forecasts that illustrate the sensitivity of the model to modifying the mortality components (M), the growth 

rates (G) and the density effect (D) to the values indicated in the legend. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 34

A lot can be said about these outputs. Carefully studying the figure may provide 

the best understanding of Climate-FVS. 

Clearly the mortality component is the biggest driver. Turning it off (M = 0) 

provides drastically different results than leaving it as is (M = 1) or doubling its 

effectiveness (M = 2). The trees die a bit faster with M = 2 as compared with 

M = 1 and slightly sooner. 

Turning off versus doubling Climate-FVS’s modification of growth rates does 

have an effect on the model outputs. Turning it off slowed the growth compared 

to both the base case and the cases where it was doubled in the West Cascades 

and northern Idaho examples. At these two locations, Climate-FVS increased 

growth due to climate change. In the western Colorado example, the growth rate 

modifications had little influence on the outputs. 

In the western Colorado example, the total volume hit an apparent maximum 

of just below 10,000 ft2/a. The dashed blue line essentially corresponds to a 

no-climate-change run and in that run, the stand simply stops growing. Note 

that each of these examples uses different regional variants of FVS. The West 

Cascades variant is used in the first example, the Inland Empire variant in the 

Idaho example, and the Central Rockies variant is used in the Colorado example. 

Different model formulations used among the regional variants influence the 

model results. 

Changing a site’s carrying capacity by modifying the maximum density (see 

section 2.4) has comparatively little effect. Only in the Northern Idaho example 

was the effectiveness of this model component evident. In that case, increasing the density effects had two opposite impacts on yield. When the mortality 

component was turned off (M = 0), doubling the density effect (D = 2) shows an 

increase in volume compared with it being off, but when the mortality component 

doubled (M = 2), the density effect showed a decrease in volume. In the other 

two examples, setting D = 0 verses D = 2 were practically identical. 

6. Summary

How long will the trees survive? That is the key question that must be answered 

for any prediction of forest stand dynamics. Growth and yield models have always 

confronted this issue; if the mortality predictions are accurate, the projections 

can generally be used for many practical purposes. 

How this question is addressed in Climate-FVS has an enormous consequence 

to the predictions. Most significantly, however, is that the answer to this question has an enormous consequence on the future for actual forests as opposed 

to simulation outputs. 
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 35

Ecological geneticists have studied adaption for years and it is research results 

from that body of knowledge that lead to the mortality assumptions in ClimateFVS (Morgenstern 1996). Yet, an unsettled feeling sets in when the outputs are 

studied. Surely, existing trees will survive and if they are alive, they are growing—perhaps slowly. 

Climate-FVS default settings are set up to represent science and analysis results covered in this document. Model options are included that allow users to 

change the model. This practice is explicitly encouraged for two reasons. First, 

it is important to understand the impact of model assumptions on the outputs as 

was done briefly in section 4.1 and in section 5. The second reason is that model 

users often possess ancillary information that should be considered in making 

projections. Taking that information into account can often be done using the 

commands presented in section 3. 

There are many criticisms of Climate-FVS. Perhaps the most significant is 

directed towards using the species climate viability scores to drive mortality. 

These scores reflect the realized climate niche of species that is, by definition, 

smaller than their potential. Surely, it is argued, these species will have opportunities to exist in some conditions that are now unknown. A key idea is that 

the realized niche is a product of climate and competition and if the competition is removed, species will occupy a larger portion of their potential ranges. 

However, these expansions will not be observed until the composition changes, 

allowing the expansions to take place. Users that have information regarding 

such expansions are encouraged to override the model’s values, replacing their 

better knowledge for the model’s weaker evidence. 

Another criticism is that Climate-FVS does not represent the underlying key 

processes driving growth. One of those has to do with the effect increasing carbon dioxide concentrations will have on water use efficiency. Indeed, this effect 

could result in trees being more tolerant of drought and thereby able to maintain 

viability beyond the conditions implied by this model. Again, the implications of 

this effect can be simulated using Climate-FVS by editing the viability scores to 

imply greater viability or by modifying growth rates. As was done in Version 1.0, 

representing the effects of increased carbon dioxide concentrations has been put 

off. Note that the sensitivity (section 5) results indicate that moderate changes in 

growth rates are not very important. Any factor that greatly influences mortality 

rates, on the other hand, is of urgent importance to include.

Generally, within the inland western United States the frost free period in forested stands is expect to more than double, and average temperatures increase 

between 3 and 4 °C, all while precipitation remains about the same. These are 

actually large changes. To get an idea of how large they are, consider two locations within your own range of experience. Pick two forested zones, one that has 

a mean average temperature that is 3 °C warmer than the other, with a growing 

season that is twice as long. Now, ask yourself just how different are the two 

ecosystems you have selected? When studying the outputs from Climate-FVS 

the magnitude of implied changes can be judged in that context.
USDA Forest Service Gen. Tech. Rep. RMRS-GTR-319. 2014 36

The introduction states that providing insights into the future is an intended 

model use. In the West Cascades example (section 4.1), the model outputs suggest 

that a conversion of growing Douglas-fir growing stock from one seed source 

to another will be needed to keep this stand functioning as a wood producer. In 

the Colorado example, a high-volume conifer stand like the one that currently 

exists is not likely to exist in the future. Some managers may already know about 

these predictions and others may not. Running FVS without considering climate 

change will not provide relevant insights into the future as it would predict that 

no growing stock conversions need to be contemplated. 

Acknowledgments

I thank Aaron Weiskittel, Phil Radtke, Andrew Hudak, Gerald Rehfeldt, Laura 

Leites, Michael VanDyck, and Erin Smith-Mateja for consultations and review. 

This work was funded by the Rocky Mountain Research Station.

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or in any program or activity conducted or funded by the Department. (Not all 

prohibited bases will apply to all programs and/or employment activities.) For 

more information, please visit the USDA web site at: www.usda.gov and click on 

the Non-Discrimination Statement link at the bottom of that page.

Reno, Nevada

Albuquerque, New Mexico

Rapid City, South Dakota

Logan, Utah

Ogden, Utah

Provo, Utah

Flagstaff, Arizona

Fort Collins, Colorado

Boise, Idaho

Moscow, Idaho

Bozeman, Montana

Missoula, Montana

To learn more about RMRS publications or search our online titles:

www.fs.fed.us/rm/publications

www.treesearch.fs.fed.us