Certainty and Uncertainty in Climate Change and Forest Response Part 1: The Climate System

By Jennifer Hushaw

(Click to download a pdf of this complete article or a one-page synopsis)

While the basic mechanics of climate change are well understood, uncertainties associated with future greenhouse gas emission rates and various climate system feedbacks make it difficult to know the exact rate and extent of warming. Understanding both the degree and the sources of uncertainty is key to effective decision making and, in this bulletin, we will identify aspects of the science that are well established and active areas of research. Part 1 of this bulletin covers certainty and uncertainty associated with the climate system. Next month, Part 2 will cover certainty and uncertainty associated with forest response to climate change.

Even as we identify areas of uncertainty, we recognize that there are situations in which a hazard is not absolutely certain but still poses a risk – the primary reason why we might be interested in flood or fire insurance, for example. Ultimately, all decision-makers, including forest managers, want to understand what is known and unknown, minimize uncertainty wherever possible, and choose the best strategy (based on individual risk tolerance) for dealing with it.

What We Know

The earth’s climate system is immensely complex and, not surprisingly, there is some uncertainty in our understanding of global climate change. However, the uncertainty is primarily in the details – refining the projections of how climate will change in the near-term and on a regional or local scale. The core underlying phenomena have been well-understood for over a century, beginning with Joseph Fourier’s discovery of the greenhouse effect in 1824, John Tyndall’s discovery that CO2 is a greenhouse gas in 1859, and Svante Arrhenius’s initial estimates of how much the earth would warm from human emissions of CO2 in 1896 (history buffs can find a more complete timeline here or here). So, before we delve into the major areas of uncertainty, we’ll recap what we do know:

  • Greenhouse gases (e.g. water vapor, carbon dioxide, methane, surface-level ozone, nitrous oxides and fluorinated gases) are warming the planet
  • Other pollutants (i.e. aerosols, such as sulphur dioxide) are cooling the planet
  • When all climate forcings are totaled (anthropogenic and natural) the total net effect is warming the planet
  • The planet will continue to warm while this imbalance in the energy budget persists
  • Significant regional differences in the rate of warming will continue, with areas near the poles generally warming more rapidly than lower latitudes
  • Drought will be more impactful as temperatures increase
  • Precipitation patterns are changing, with some regions getting wetter and some drier
  • The probability of extreme heat and precipitation is increasing as the planet warms
  • Sea levels will continue to rise for several centuries and beyond


Uncertainty about Future Climate

At the most fundamental level, climate change is about the earth’s energy budget – when there is more energy coming in than going out things must get warmer, and vice versa. While there are many ways to change the temperature in a particular region of the globe, there are only three ways to change the average temperature of an entire planet:definitions_box

  1. Change the amount of energy coming in (i.e. solar activity)
  2. Change the amount of energy reflected back out to space (i.e. albedo/reflectivity)
  3. Change the amount of energy trapped by the atmosphere (i.e. strength of the greenhouse effect)

Accounting for the influence of the sun is fairly straightforward because solar activity follows predictable cycles. Also, changes in solar output are modest compared to these other factors (e.g. the difference between the minimum and maximum of a solar cycle is only 7% as much energy as the amount of additional energy from all human greenhouse gas emissions since pre-industrial times). The major areas of uncertainty about future climate change are related to the last two items – these variables are affected by feedbacks in the climate system and the last is related to the amount of future emissions.


There is uncertainty in our estimates of future global greenhouse gas emissions (hence why researchers typically utilize different emissions ‘scenarios’) because it will depend on how much the world population grows, the nature of future economic development, and the technology we use to meet our energy demands. As of now, global emissions are tracking the highest emissions scenario developed by the Intergovernmental Panel on Climate Change.

Long-lived greenhouse gases, like CO2, are of particular concern because they will stay in the atmosphere for centuries and continue to affect the climate long after we reduce or eliminate human emissions. This long residence time allows concentrations to build and the science has shown that “climate change results from the cumulative buildup of GHGs [greenhouse gases] in the atmosphere over time, not emissions in any particular year” (Baumert et al 2005), highlighting the significant long-term influence of rising greenhouse gas levels.


There are a number of positive and negative feedbacks in the climate system, which amplify or reduce the effect of a given climate forcing. Climate models include these processes, but each model may have slight differences in the relative magnitude of individual feedbacks. This is why there is some uncertainty about the exact amount of warming we will experience from a particular concentration of greenhouse gases.

This question of climate sensitivity has been a central area of research for decades and, as cited in a previous bulletin, the best current estimates suggest that doubling atmospheric CO2 concentrations (to about 550 ppm) will ultimately result in 2.7 to 8.1⁰F of global average warming. We will likely reach those concentrations by the middle of this century, if we continue on the current global emissions trajectory. Forty years of research from independent lines of evidence, including computer models and the study of past climate change, have given us confidence that the answer lies within this range. Although, this range is really a bell curve of possibility (not all values are equally likely) and research has not been able to narrow that range.

Feedbacks also play out on different timescales – from some that occur over the course of several years (e.g. changes in snow/ice cover) to others that take place over millennia (e.g. changes in the carbon cycle or the mass of ice-sheets on land), and beyond.  There is a lot of inertia in the earth’s climate system and this is also why past emissions have already committed us to a certain amount of warming.

Some examples of “fast” feedbacks include:

  • Snow/ice albedo (+)
    • Warmer temperatures melt bright snow/ice cover, revealing darker land and ocean water surfaces that absorb more solar radiation, which increases local warming that leads to more snow/ice melt, and so on.
  • Water vapor (+)
    • A warmer atmosphere can hold more water vapor, which traps more heat, which allows the atmosphere to become even more saturated, which warms things further, and so on. Likewise, cooling causes water vapor to condense and rain out, which reduces temperature, leading to further precipitation, and so on.
    • Water vapor is a very potent greenhouse gas, but it does not contribute significantly to the long-term greenhouse effect because its typical residence time in the atmosphere is only about ten days, unlike CO2 which stays in the atmosphere for centuries.
  • Clouds (+/-)
    • Feedbacks from clouds are complex and they are one of the biggest areas of uncertainty because we don’t know exactly how cloud cover will change under warmer conditions. Whether clouds have a warming or cooling effect depends on cloud formation, persistence, and altitude, for example:
      • Increase cumuliform = decrease % cloud cover = increase temp (+)
      • Increase stratiform = increase % cloud cover = decrease temp (-)

An example of a “slow” feedback would be:

  • Forests (+/-)
    • We spend a lot of time considering how the climate affects forests, but it is not a one-way relationship – forests also interact with the atmosphere and contribute to climate feedbacks. Forests affect the amount of energy absorbed and reflected from the surface (dark forest canopy has lower albedo), the hydrologic cycle (through evapotranspiration), and the carbon cycle (through photosynthesis and carbon sequestration). Through these processes, forests can act as both a negative and positive feedback, and the magnitude of these effects varies depending on forest type (see table below) (Bonan 2008).


Tipping Points

Another important area of uncertainty is related to so-called ‘tipping points’ in the climate system – these are points “beyond which an abrupt or irreversible transition to a different climatic state occurs” (Walsh et al 2014). Tipping points, such as the runaway loss of arctic sea ice, the collapse of some ocean circulation patterns, or large-scale release of carbon from melting permafrost, involve (practically) irreversible impacts that occur when a process crosses a threshold, kicking off feedbacks that will continue to push the climate in one direction, even if we reduce emissions.

There is evidence that these types of tipping points have been reached repeatedly in the past. The challenge is that they are much more difficult to predict than gradual climate changes and they are hard to detect until you’ve already passed them. Despite this uncertainty, the potential for this kind of abrupt change is a big concern because it will be high impact and have major consequences for both human societies and natural systems.


Additional uncertainty comes not from imperfect understanding or modelling of the large-scale climate system, but from the challenge of “downscaling” the results of global climate models. As mentioned in a previous bulletin, the resolution used to simulate global-scale processes does not match the scale of forest management and the use of either statistical or dynamical downscaling methods introduces a new layer of uncertainty in regional climate projections – an important caveat to keep in mind when viewing climate projections for your particular region.



At the simplest level, climate change is about an imbalance in the earth’s energy budget – a stronger greenhouse effect is trapping more energy in the climate system and the planet is getting warmer to radiate an equal amount of energy back out. We know that the average global temperature will continue to increase because of this imbalance, but there is still some uncertainty in the details of exactly how these changes will play out, especially at a regional level. There are also a host of additional side-effects, such as changing precipitation, ecological shifts, changing extremes, and so on. In next month’s bulletin, we will focus on the uncertainty related to forest impacts and discuss the range of strategies for coping with uncertainty in the realm of forest management.



Baumert, Kevin A., Timothy Herzog, and Jonathan Pershing. 2005. “Chapter 6: Cumulative Emissions.” In Navigating the Numbers: Greenhouse Gas Data and International Climate Policy. World Resources Institute.

Bonan, Gordon B. 2008. “Forests and Climate Change: Forcings, Feedbacks, and the Climate Benefits of Forests.” Science 320 (5882): 1444–49. doi:10.1126/science.1155121.

Le Page, Michael. 2011. “What We Do Know – and What We Don’t.” NewScientist, October 22. 

“Making Sense of Palaeoclimate Sensitivity.” 2012. Nature 491 (7426): 683–91. doi:10.1038/nature11574.

McGuffie, F., and A. Henderson-Sellers. 1997. “1.4 Climate Feedbacks and Sensitivity.” In: Climate Modelling Primer, 2nd ed., 31–39. Chichester, West Sussex, England: John Wiley & Sons.

Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, K. McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, 2012: Changes in climate extremes and their impacts on the natural physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.

Walsh, J., D. Wuebbles, K. Hayhoe, J. Kossin, K. Kunkel, G. Stephens, P. Thorne, R. Vose, M. Wehner, J. Willis, D. Anderson, V. Kharin, T. Knutson, F. Landerer, T. Lenton, J. Kennedy, and R. Somerville, 2014: Appendix 4: Frequently Asked Questions. Climate Change Impacts in the United States: The Third National Climate Assessment, J. M. Melillo, Terese (T.C.) Richmond, and G. W. Yohe, Eds., U.S. Global Change Research Program, 790-820. doi:10.7930/J0G15XS3.


Global Precipitation Part 2: Ecosystem & Management Implications

By Jennifer Hushaw

This bulletin is the second in a two part piece on changes in global precipitation. In it, we discuss how water availability shapes forests and recent observations of forest decline linked to drought and heat stress. We then turn our attention to the issue of drought, including the factors that contribute to drought risk and the management options for mitigating it. While there will be a mix of wetter and drier conditions in the future, depending on the region, the background trend of warming temperatures will exacerbate drying – making more frequent and severe drought one of the more obvious climate-related risks.

(click here to download a pdf of this article or a one-page synopsis)

Changes in Global Precipitation: A Recap

Predicting how global climate change will affect future precipitation is one of the most challenging and uncertain areas of climate modeling research. However, there are some consistent patterns that have emerged from model projections, as described in part one of this bulletin. These general rules-of-thumb are summarized here:

  • Increase in total global precipitation
  • Regional differences, i.e. changes will not be uniform – increases in mid and high latitudes; decreases in subtropics
  • General pattern of wet-get-wetter, dry-get-drier
  • Increased contrast between wet and dry seasons
  • Increased winter precipitation in mid and high latitudes
  • Increased frequency of heavy precipitation events
  • A few areas of model agreement in terms of drying (soil moisture), including northeast and southwest South America and southwestern U.S.

Why Water Availability Matters    

Water availability is likely to change in the future, due to the combination of increasing average temperatures and changes in the total, timing, and intensity of precipitation events. Water availability, in terms of soil moisture, is a particularly important metric for forest management, since it can drive changes in forest structure and species composition over time. Water availability affects tree mortality, seedling recruitment, and resource allocation within individual trees (e.g. root-shoot ratio), and these factors ultimately influence competition between species.

Tree Species Migration

Severe or long-term decreases in water availability can predispose forest areas to large-scale die-off that opens the door for colonization of new species. While climate-induced tree species movement is usually a gradual process, it can happen more rapidly when sporadic mortality events eliminate competition from established species – arguably the biggest immediate barrier to species migration. We have seen some examples of this mortality-facilitating-colonization pattern in places like the Green Mountains of Vermont, where there is evidence that the upslope migration of hardwood species was likely accelerated by canopy turnover after red spruce experienced dieback from acid rain in the 1960’s and 70’s (Beckage et al. 2008) .

Tree Resource Allocation

Water availability, including the amount and timing of rainfall, is critical to forest structure because it changes how individual trees allocate their resources between the above and belowground portions of the stem. Under drier conditions, trees will generally respond by decreasing inputs to foliage and aboveground woody biomass while increasing fine roots, which improves their ability to draw on limited soil water resources. This is a helpful adaptation, but it reduces growth in the merchantable part of the tree. Likewise, in wetter areas, trees will put more resources into foliage and increase growth rate, with fewer fine roots. This capitalizes on growth potential and increases competitiveness, but it can also result in a shallower root system that increases risk of blow down and vulnerability to future drought conditions (Farrior et al. 2013; McDowell et al. 2008).

Different species have different amounts of plasticity in the degree and speed with which they can shift resources in response to changing conditions and, as with any adaptation, there are tradeoffs, e.g. it has been shown that this flexibility, while beneficial in terms of adaptability, can be detrimental in stressful environments (Richter et al. 2012).

Water Stress and Forest Mortality

Recently, researchers have documented widespread tree mortality on a global scale that is at least partly attributed to drought and heat stress (see this map from Hartmann et al. 2015, which shows locations of substantial drought- and heat-induced tree mortality around the globe since 1970). The impacts were observed in wet areas, as well as semi-arid regions, which indicates that increasing temperatures may be playing a significant role – by increasing water loss through transpiration, reducing tree vigor, and accelerating insect development and reproduction (Allen et al. 2010; Hartmann et al. 2015).

There are many examples of forest impacts in the U.S. that have been linked to water stress, including aspen decline in the west (Worrall et al. 2013), increased mortality of pine and oak species in the Central Coast and Southern Sierra Ranges of California (USFS 2015), loss of big trees (>2 ft dbh) throughout California (McIntyre et al. 2015), and regional-scale die-off of piñon pine (Breshears et al. 2008). The ultimate consequences of forest die-off driven by drought and heat stress are unclear and researchers have highlighted the importance of investigating these implications (Anderegg, Kane, and Anderegg 2013).


At this time, we don’t have sufficient data to know whether forest mortality is increasing globally. Although, these observations have sparked a huge body of research on the physiological mechanisms that influence how plants avoid, tolerate, and/or recover from drought stress. A better understanding of exactly how and why certain trees succumb to drought will improve predictions of global-scale forest impacts from climate change.

Presently, the understanding is that drought-related mortality happens via three interrelated pathways – carbon starvation, hydraulic failure, and biotic attack (Figure 1). Carbon starvation occurs when photosynthesis is reduced and trees are forced to use up their carbon reserves – a consequence of closing leaf stomata, which reduces water loss but does not allow CO2 uptake. Hydraulic failure occurs when plants dehydrate past the point of no return. Insects and pathogens can amplify or be amplified by both of these processes (McDowell et al. 2008), e.g. carbon starvation will reduce resin production and make it difficult for trees to pitch out an attacking beetle.

Evaluating Drought Risk

Evidence suggests we will experience more frequent and severe drought due to climate change, but this risk is not universal and it varies with site characteristics and forest type.  Determining whether it is a significant risk for your forestland involves considering all the factors that influence intensity, exposure, and vulnerability to drought.


Higher temperatures increase the intensity of individual drought events by water loss through direct evaporation and forest transpiration (collectively known as evapotranspiration). Additionally, as conditions dry, there is a feedback that exaggerates this process – less soil moisture means less cooling from transpiration and temperatures go up even further. This is similar to the way human sweat helps reduce body temperature – if you lose your ability to sweat when you are hot, your body temperature will begin to increase rapidly.

Including evapotranspiration in model simulations (rather than precipitation alone) increases the percentage of global land area that is projected to experience moderate drying by the end of this century (from 12 to 30%). Importantly, researchers found that this effect will even make relatively wet areas more drought prone: “Increased PET [potential evapotranspiration] not only intensifies drying in areas where precipitation is already reduced, it also drives areas into drought that would otherwise experience little drying or even wetting from precipitation trends alone” (Cook et al. 2014). This interaction with temperature has also been implicated in the severity of the recent California drought, where researchers have found that the occurrence of drought years has increased primarily because of the increased probability of warm-dry conditions, rather than a substantial change in the probability of a precipitation deficit (Diffenbaugh, Swain, and Touma 2015). The bright side is that these drying trends will be beneficial in some areas where conditions have historically been excessively wet – this will help alleviate issues of reduced productivity and limited access in these locations.


Site characteristics, including soil texture, depth to water table, and topography, have a big influence on the amount of drought exposure on a given site (i.e. the likelihood that a given location will experience drought conditions). These factors influence soil water holding capacity, run off, and evaporation rates, which all mediate the direct effects of precipitation change.



Vulnerability is primarily determined by the tree species mix on site, specifically the level of drought tolerance exhibited by each species. The variability in forest drought tolerance from region to region creates some unique advantages and disadvantages in terms of vulnerability. For example, the southeastern U.S. has a species mix with a relatively high drought tolerance compared to the northeast, which reduces the risk of negative drought impacts in that region. However, there is a much higher diversity in terms of the mix of drought tolerant and intolerant species in the Appalachian region and the northeast, which might be beneficial if future conditions are highly variable.

Note: We recommend referencing Russell et al. 2014 for maps of average drought tolerance and diversity of drought tolerance classes among tree species in the eastern U.S. 


Overall Risk

Taken together, these factors tell us that sites that are projected to have large increases in temperature and decreases in precipitation, with low soil water holding capacity, and a drought-intolerant species mix will have the highest levels of drought risk (in terms of intensity, exposure, and vulnerability).  In contrast, an area may have a high likelihood of intense drought in the future, but the risk may be mitigated by a drought tolerant species mix and better site conditions. The regions of greatest concern going forward will be places where all these factors overlap.

Reducing Drought Risk Through Management

From the perspective of an individual forest manager, there is not much that can be done to reduce the intensity of future drought conditions, but the following areas offer opportunities to reduce risk by reducing exposure and/or vulnerability:

  • Land base
    • Focus resources on sites with soil and topographic characteristics that generally retain moisture
  • Species mix
    • Use silvicultural techniques that favor regeneration of drought tolerant species
    • Plant genotypes from warmer and dryer areas of a species range
  • Reduce stocking
    • A number of studies conducted in different forest types throughout the U.S. and Europe (primarily pine-dominated) have highlighted the utility of thinning for moderating drought impacts on growth, increasing drought resistance, and improving the speed of recovery after drought events (D’Amato et al. 2013; Kerhoulas et al. 2013; Kohler et al. 2010; Slodicak, Novak, and Dusek 2011).
    • Although it is worth noting that thinning has been shown to have negligible effects on drought tolerance in sparse forest canopies (B. Law, personal communication, May 6, 2015), such as some dry western forests where self-thinning has naturally taken place.



Allen, Craig D., Alison K. Macalady, Haroun Chenchouni, Dominique Bachelet, Nate McDowell, Michel Vennetier, Thomas Kitzberger, et al. 2010. “A Global Overview of Drought and Heat-Induced Tree Mortality Reveals Emerging Climate Change Risks for Forests.” Forest Ecology and Management 259 (4): 660–84. doi:http://dx.doi.org/10.1016/j.foreco.2009.09.001.

Anderegg, William R. L., Jeffrey M. Kane, and Leander D. L. Anderegg. 2013. “Consequences of Widespread Tree Mortality Triggered by Drought and Temperature Stress.” Nature Clim. Change 3 (1): 30–36. doi:10.1038/nclimate1635.

Breshears, David D, Orrin B Myers, Clifton W Meyer, Fairley J Barnes, Chris B Zou, Craig D Allen, Nathan G McDowell, and William T Pockman. 2008. “Tree Die-off in Response to Global Change-Type Drought: Mortality Insights from a Decade of Plant Water Potential Measurements.” Frontiers in Ecology and the Environment 7 (4): 185–89. doi:10.1890/080016.

Cook, BenjaminI., JasonE. Smerdon, Richard Seager, and Sloan Coats. 2014. “Global Warming and 21st Century Drying.” Climate Dynamics 43 (9-10): 2607–27. doi:10.1007/s00382-014-2075-y.

D’Amato, Anthony W., John B. Bradford, Shawn Fraver, and Brian J. Palik. 2013. “Effects of Thinning on Drought Vulnerability and Climate Response in North Temperate Forest Ecosystems.” Ecological Applications 23 (8): 1735–42. doi:10.1890/13-0677.1.

Diffenbaugh, Noah S., Daniel L. Swain, and Danielle Touma. 2015. “Anthropogenic Warming Has Increased Drought Risk in California.” Proceedings of the National Academy of Sciences 112 (13): 3931–36. doi:10.1073/pnas.1422385112.

Farrior, Caroline, E., Ray Dybzinski, Simon A. Levin, and Stephen W. Pacala. 2013. “Competition for Water and Light in Closed-Canopy Forests: A Tractable Model of Carbon Allocation with Implications for Carbon Sinks.” The American Naturalist 181 (3): 314–30.

Hartmann, Henrik, Henry D. Adams, William R. L. Anderegg, Steven Jansen, and Melanie J. B. Zeppel. 2015. “Research Frontiers in Drought-Induced Tree Mortality: Crossing Scales and Disciplines.” New Phytologist 205 (3): 965–69. doi:10.1111/nph.13246.

Kerhoulas, Lucy P., Thomas E. Kolb, Matthew D. Hurteau, and George W. Koch. 2013. “Managing Climate Change Adaptation in Forests: A Case Study from the U.S. Southwest.” Journal of Applied Ecology 50 (6): 1311–20. doi:10.1111/1365-2664.12139.

Kohler, Martin, Julia Sohn, Gregor Nägele, and Jürgen Bauhus. 2010. “Can Drought Tolerance of Norway Spruce (Picea Abies (L.) Karst.) Be Increased through Thinning?” European Journal of Forest Research 129 (6): 1109–18. doi:10.1007/s10342-010-0397-9.

McDowell, Nate, William T. Pockman, Craig D. Allen, David D. Breshears, Neil Cobb, Thomas Kolb, Jennifer Plaut, et al. 2008. “Mechanisms of Plant Survival and Mortality during Drought: Why Do Some Plants Survive While Others Succumb to Drought?” New Phytologist 178 (4): 719–39. doi:10.1111/j.1469-8137.2008.02436.x.

McIntyre, Patrick J., James H. Thorne, Christopher R. Dolanc, Alan L. Flint, Lorraine E. Flint, Maggi Kelly, and David D. Ackerly. 2015. “Twentieth-Century Shifts in Forest Structure in California: Denser Forests, Smaller Trees, and Increased Dominance of Oaks.” Proceedings of the National Academy of Sciences 112 (5): 1458–63. doi:10.1073/pnas.1410186112.

Richter, Sarah, Tabea Kipfer, Thomas Wohlgemuth, Carlos Calderón Guerrero, Jaboury Ghazoul, and Barbara Moser. 2012. “Phenotypic Plasticity Facilitates Resistance to Climate Change in a Highly Variable Environment.” Oecologia 169 (1): 269–79. doi:10.1007/s00442-011-2191-x.

Slodicak, Marian, Jiri Novak, and David Dusek. 2011. “Canopy Reduction as a Possible Measure for Adaptation of Young Scots Pine Stand to Insufficient Precipitation in Central Europe.” Forest Ecology and Management 262 (10): 1913–18. doi:http://dx.doi.org/10.1016/j.foreco.2011.02.016.

USDA Forest Service. 2015. Forest Health Protection Survey: Aerial Detection Survey April 15th-17th, 2015. Accessed online at: http://www.sierranevada.ca.gov/our-work/docs/southsierrasdroughtsurveyapr2015.pdf

Worrall, James J., Gerald E. Rehfeldt, Andreas Hamann, Edward H. Hogg, Suzanne B. Marchetti, Michael Michaelian, and Laura K. Gray. 2013. “Recent Declines of Populus Tremuloides in North America Linked to Climate.” Forest Ecology and Management 299 (0): 35–51. doi:http://dx.doi.org/10.1016/j.foreco.2012.12.033.



Global Precipitation Part 1: Trends & Projections

By Jennifer Hushaw

The discussion of global precipitation will be covered in two parts. This bulletin (part one) provides an overview of the precipitation trends observed over the last century, as well as projections of future change. Part two will cover a range of forest ecosystem and management implications of changing precipitation patterns and drought stress.

(click here to download a PDF of this article or a one-page synopsis)

Observations of Global Precipitation

Observations suggest that there has been an increase in globally averaged precipitation on land over the last century.  This is based on several global datasets that all show a statistically significant increase since 1900 (Figure 1). However, the datasets do not agree on how much it has increased. This primarily stems from a lack of sufficient data across the globe, especially during the early 1900’s.  The upward trend is also much clearer in the early part of the record than in the years after 1950. As a result, there is only low confidence in the observed global change prior to 1950 and medium confidence in the trends since that time.


The data also reveal notable variability in precipitation trends at different latitudes, but problems with data quality, coverage, and agreement have resulted in low confidence in most regional observations. However, there is at least one area of the world where clear trends have emerged – namely, the mid-latitudes of the Northern Hemisphere (30⁰N to 60⁰N). Here, the data show a likely overall increase in precipitation, with medium confidence prior to 1950 and high confidence afterward (Figure 2).


Future Global Precipitation

There are several metrics to consider when thinking about changes in future precipitation, these include changes in the total annual amount, shifts in timing, and changes in the intensity of individual precipitation events – all of which have important ramifications for soil moisture dynamics and water availability for forests.


On average, models project a gradual increase in global precipitation over the 21st century. This is because warmer temperatures drive more evaporation and the increase in water vapor leads to more precipitation. While we expect an increase in the total global amount, this does not mean every individual region will receive more precipitation. Instead, some areas will actually become much drier because we also expect existing regional precipitation profiles to be amplified – a wet-get-wetter and dry-get-drier pattern. In particular, it is very likely we will see an increase in precipitation at high and some mid-latitudes, whereas in the subtropics an overall decrease is more likely than not (Figure 3).


Note: In the near-term, these projected changes are likely to be fairly small compared to natural variability from circulation patterns like the El Nino-Southern Oscillation, for example. This will also be true for some regions even in the long-term under a low emissions scenario (see extensive hatching in low emissions map, above). This dominance of internal variability at certain scales is an important recurrent theme.


Shifts in the timing and/or amount of precipitation from one season to another can significantly affect ecosystem processes and the hydrologic cycle. Models suggest that we will see these types of shifts and the largest changes will be at high latitudes. In particular, the contrast between the wet and dry season is likely to increase in most places as temperatures warm. Although, there will be some notable regional exceptions along the equator and poleward edges of the subtropical dry zone, where changes in atmospheric circulation will cause precipitation patterns to shift completely. In both mid- and high-latitude regions the projected increase is larger for winter than summer (Figure 4). However, this increase in average winter precipitation will occur at the same time that increasing temperatures contract the length of the snow season.



Warmer temperatures allow the air to hold more water vapor when it is saturated, so when it rains or snows in a warmer world there is simply more water coming down, which will increase the intensity of individual precipitation events. As a result, the probability of heavy precipitation is projected to increase, and the effect will be stronger with increased warming (see Fischer and Knutti 2015, Figure 3 (a), (b), and (c) for maps showing projected change in the probability of heavy precipitation events under different levels of warming across the globe; units are probability ratios, i.e. the ratio of current or future probability to the pre-industrial probability). As a greater fraction of total rainfall comes in the form of these heavy downpours it will also affect soil moisture because more precipitation will be lost as runoff, rather than being absorbed by the soil or going to replenish ground water supplies. For these reasons, intense rainfall events will also increase the likelihood of flash floods.

Uncertainty of Precipitation Projections

We have a high level of confidence in estimates of future temperature because those projections are based on basic physical principles and significant model consensus, but precipitation is much more uncertain. And, as with temperature, the uncertainty grows as we scale down – from estimates of change in the global average to regional projections. There is less agreement among global climate models regarding the direction and magnitude of precipitation change, especially in certain regions.

There are a number of reasons for this uncertainty, including:

  1. The small spatial scales on which precipitation dynamics take place (e.g. cloud microphysics)

The scale of these processes is much smaller than the resolution of global climate models, so those dynamics cannot be simulated from first principles, instead they must be described using parameterization, which introduces some room for error.

  1. A large degree of spatial variability from one location to the next

More data coverage is necessary to accurately capture precipitation trends, in contrast with temperature, which is highly autocorrelated over long spatial scales (i.e. the temperature at one location is closely related to the temperature nearby, so just a few data points can be used to calculate a large area).

  1. The indirect relationship between greenhouse gases and precipitation change

At a very basic level, greenhouse gases influence the climate by changing the amount of heat, which changes atmospheric pressure, which leads to changes in rainfall patterns. Because the influence of greenhouse gases is several steps removed, it is more challenging and complex to simulate how changes in those concentrations will affect precipitation. However, in recent years scientists have improved their understanding of the mechanics underlying projected changes in precipitation and, as a result, we have a much higher degree of confidence in some of the patterns that have emerged from global climate models (e.g. wet-get-wetter, dry-get-drier).


Drought: A Closer Look

Changes in the length and severity of drought are important metrics of precipitation change. Generally, drought implies a moisture deficit relative to some previous norm. More specifically there are three types of drought: meteorological, hydrological, and soil moisture (Figure 5). Of these, soil moisture drought is perhaps the most relevant for forests because it is closely tied to plant water availability.

Soil moisture drought is driven by reduced precipitation and/or increased evapotranspiration. In fact, at seasonal or longer time scales, an increase in evapotranspiration (which is indirectly driven by temperature) can lead to more frequent and intense periods of soil moisture drought. Soil characteristics, rooting depth, vegetation, an on-going lack of precipitation, and previous soil moisture and groundwater conditions also play an important role in determining this type of drought risk.

The 2013 Intergovernmental Panel on Climate Change improved previous work on soil moisture projections by using a consistent soil depth across all climate models in their report. The results yielded high confidence in regions where surface soils are expected to dry, but little-to-no confidence in locations where surface soils are projected to be wetter. The models also consistently projected drying conditions in certain regions, specifically the Mediterranean, northeast and southwest South America, southern Africa, and the southwestern U.S. (Figure 6). Although, they disagreed on the direction of change (wetter or drier) in other large regions, such as central Asia or the high northern latitudes. Consequently, there are only a handful of locations where we can currently be confident about how soil moisture will change in the future and those locations are all projected to become drier.

Part two of the global precipitation bulletin will delve into how and where these projected changes in precipitation, drought, and soil moisture are likely to impact forests. It will also include a discussion of the optimal species characteristics, silvicultural decisions, and infrastructure design for minimizing risk from these climate variables and capitalizing on growth potential.



Collins, M., R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, X. Gao, W.J. Gutowski, T. Johns, G. Krinner, M. Shongwe, C. Tebaldi, A.J. Weaver and M. Wehner, 2013: Long-term Climate Change: Projections,

Commitments and Irreversibility. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Fischer, E.M. and Knutti, R. 2015. Anthropogenic contribution to global occurrence of heavy-precipitation and high-temperature extremes. Nature Climate Change. Advance online publication: http://dx.doi.org/10.1038/nclimate2617.

Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, 2013: Observations: Atmosphere and Surface. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Kirtman, B., S.B. Power, J.A. Adedoyin, G.J. Boer, R. Bojariu, I. Camilloni, F.J. Doblas-Reyes, A.M. Fiore, M. Kimoto, G.A. Meehl, M. Prather, A. Sarr, C. Schär, R. Sutton, G.J. van Oldenborgh, G. Vecchi and H.J. Wang, 2013: Near-term Climate Change: Projections and Predictability. In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Seneviratne, S.I., N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, Y. Luo, J. Marengo, McInnes, M. Rahimi, M. Reichstein, A. Sorteberg, C. Vera, and X. Zhang, 2012: Changes in climate extremes and their impacts on the natural physical environment. In: Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation [Field, C.B., V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, K.J. Mach, G.-K. Plattner, S.K. Allen, M. Tignor, and P.M. Midgley (eds.)]. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC). Cambridge University Press, Cambridge, UK, and New York, NY, USA, pp. 109-230.


Global Temperature Part 2: Future Projections

By Jennifer Hushaw

In the January bulletin, we focused on global trends in the modern temperature record. Now we look ahead to what climate models can tell us about the future of global temperature.

(click here to download a PDF of this article or a one-page synopsis)


Climate Modeling Explained

From economic forecasting to predicting solar eclipses, we are familiar with the use of computer models to describe economic, social, biological, and physical systems – we use them in all realms of science, to improve our understanding of the mechanics of a system and allow for prediction of the future under different conditions. While they necessarily involve simplifications of complex systems, they do have utility – hence the familiar adage “all models are wrong, but some are useful.”

In some respects, global climate models (GCMs) are a fairly straightforward type of model because they describe physical processes, i.e. the basic physics of the climate system, which can be much simpler to accurately characterize than phenomena like biology or human behavior. However, it is the sheer complexity of the system that presents the biggest challenge and leads to some potential limitations:

  1. There may be unknown processes that are not included in the models because we are not aware of them.
  2. There may be processes that are not understood well enough to model accurately.
  3. The models may be utilizing a coarse spatial or temporal scale that cannot capture certain processes.

For example, most GCMs have grid cells of 60 to 100 miles per side, so they cannot directly simulate processes that occur on smaller spatial scales, such as cloud formation. Instead, they account for these within-cell processes using parameterization (NCA 2014).

Today’s comprehensive GCMs have many coupled components, including atmosphere, land surface, ocean and sea ice, aerosols, the carbon cycle, dynamic vegetation, atmospheric chemistry, and land ice. Their ‘performance’ is tested by evaluating how well they can reproduce the actual temperature variations we have observed in the past, which helps validate that they are effectively simulating the most important mechanisms in the system. Climate models perform particularly well with simulations of average global temperature, where they demonstrate good agreement with the decade to decade changes we have observed (IPCC  2012a).


Future Global Temperature

In the near-term (2016-2035), it is expected that the average global surface temperature will warm between 0.5 and 1.3⁰F (0.3 – 0.7⁰C), compared to the 1986-2005 reference period. However, much larger changes are projected for the latter half of this century (2081-2100), when it is likely that we will exceed 2.7⁰F (1.5⁰C) of warming under all IPCC scenarios except those with the most significant emissions reductions. The greatest warming is projected under the highest emissions scenario, which is likely to exceed 5.4⁰F (3⁰C) (Figure 1; IPCC 2013).


There will also be changes in temperature extremes, specifically “more frequent hot and fewer cold temperature extremes over most land areas on daily and seasonal timescales, as global mean surface temperature increases” (IPCC 2014). It is also likely that heat waves will increase in length, frequency, and/or intensity over most land areas (IPCC 2012b, Table 3-1).

Note: Attributing extreme weather events, like heat waves, to climate change and predicting exactly how the frequency and intensity of these events will change in the future is an important and emerging field of research that will be discussed in more detail in a future bulletin.


As we have seen in the last 100+ years, we do not anticipate that these changes will occur uniformly across the globe. Instead, we expect the pattern of greater warming over land and at high latitudes to continue into the future, as shown below for the late 21st century (Figure 2). These regional differences will be far more important for land managers than changes in the average global temperature, but additional analysis is needed to bring large-scale projections down to a scale that is relevant for local decision-making.


There have been efforts in many regions to achieve higher resolution information (typically 6 to 30 miles per grid cell) through the use of downscaled climate models. The two most common approaches are dynamical and statistical models. Dynamical models directly simulate how regional climate processes respond to global change, whereas statistical models use observed relationships between large-scale weather features and local climate to translate future projections to a smaller scale (information on the merits of each approach can be found HERE). The regional projections used in the U.S. National Climate Assessment are an example of outputs from a statistical downscaling model.


Why so many possible futures?


Simulation of Feedbacks

If we kept everything in the climate system constant and only doubled the amount of CO2 in the atmosphere, we would eventually expect to see about 2.2⁰F (1.2⁰C) warming from those emissions (Manabe and Wetherald 1967; Hansen et al. 1985). That value is derived from an understanding of the fundamental physics of the greenhouse effect, which has been well-understood since the mid-1800’s (American Institute of Physics 2015).

All climate models simulate the greenhouse effect the same way, but they diverge in their projections because they have slightly different ways of simulating feedbacks, from sources such as clouds and albedo (reflectivity). These feedbacks can amplify or dampen the warming signal. The IPCC suggests that the likely range of warming is slightly higher – between 2.7 and 8.1⁰F (1.5-4.5⁰C) (IPCC 2013), which is not surprising, given that we live in a world with many positive feedbacks that tend to magnify warming over time.


Different Emissions Scenarios

We know that cumulative CO2 emissions will determine the total average surface warming by the end of this century (IPCC 2014), but uncertainty regarding future emissions levels is one of the reasons why we have such a spread of possible trajectories for the future. This is why it is necessary to utilize different emissions scenarios for climate model projections, such as the Representative Concentration Pathways (RCPs) outlined by the IPCC.

The RCPs layout different CO2 emission pathways based on assumptions about future global economic activity, population growth, the types of energy we will use, and how efficiently we will use it – from RCP2.6 that assumes our emissions will peak in the next five years and then decline, to RCP8.5, which is a business-as-usual scenario where emissions continue to rise throughout the century. The level of change depends on the RCP and time frame in question (Figure 3).


The “Bumpy” Road Ahead

Anyone who has lived through the past few winters in the eastern U.S. may begin to think ‘global warming’ sounds like wishful thinking – between severe cold snaps and monumental snowfall – but this is short-term, local variability that doesn’t necessarily reflect how average global conditions are changing. We will also see short-term variability at the global scale, despite the long-term warming trend. In fact, we expect the future global temperature trajectory to be much ‘bumpier’ than the smooth curves you see in Figure 1, due to short-term (sub-decade) temperature changes driven by internal climate variability, such as the El Niño-Southern Oscillation (ENSO). The simple curves are an artifact of averaging multiple climate model simulations, which removes the ‘noise’ of internal variability. Rather than one year always being warmer than the next, we anticipate short-term variability in global temperature that is similar to what we might observe in a single model simulation (e.g. see colored lines in Figure 4).


Forest Impacts

Future temperature changes will impact forest resources in both positive and negative ways, through the direct effect of temperature or the indirect effect of temperature on other stressors.


Extreme Heat

More frequent and intense heat waves will exacerbate periodic drought conditions, adding to physiological tree stress and potential mortality. However, there is ample evidence that extreme heat alone can have a wide variety of effects on tree function from the molecular level to the entire tree. Heat waves are of particular concern because they can have negative effects on growth that are more severe than the same amount of heat applied as a change in average temperature.

A recent paper  by Teskey et al. (2014) reviewed the current science regarding tree response to heat waves and extreme heat events. Their review highlights the many physiological and morphological responses that help them cope with extreme heat stress. For example, some will cool themselves through transpiration by keeping their stomata open, even when reduced photosynthesis would typically cause them to close and in even conditions when this causes more water loss. Research suggests that this is an especially important mechanism for seedlings, which can experience extreme temperatures in the early stages when the soil is exposed to full sun. It is not known how many tree species have the capacity to avoid heat stress this way, but it has been experimentally observed in loblolly pine, northern red oak, red maple, and ponderosa pine (Teskey et al. 2014).

Another temperature-relevant adaptive response is the ability to tolerate heat stress more effectively after becoming acclimated to warmer temperatures. For each tree species, there is a critical temperature for the stability of important proteins involved in photosynthesis. When temperatures rise above that critical threshold, those proteins will generally be damaged. However, experimental evidence has shown that that critical temperature can be increased if the plant is exposed to higher temperatures and allowed to acclimate for a period of time – the warmer the acclimation temperature, the higher the temperature at which those important proteins can function normally and continue photosynthetic reactions (Teskey et al. 2014). Although the implications of this response were not discussed in the Teskey et al. paper, it is particularly relevant for considering how trees might adapt to warmer ambient conditions in the future. Consequently, this is an active area of research that we will continue to monitor.


Longer Growing / Frost-Free Season*

One of the most important effects of temperature change on forest productivity will be future change in the length of the growing season. As the first fall frost has happened later in the year and the last spring frost has happened earlier, we have seen a corresponding increase in the overall length of the frost-free season in the U.S. (Figure 6). In fact, frost-free season length increased by approximately two weeks during the last century and the increase was much greater in the western part of the country (Kunkel et al. 2004).


This increase is projected to continue in the future, with the U.S. growing season lengthening by as much as 30 to 80 days by the end of the century (2070-2099), as compared to the 1971-2000 base period. The largest changes are expected in the mountainous regions of the western U.S. and smaller changes expected in the Midwest, Northeast, and Southeast (NCA 2014) (Figure 7).


Growing season length ties directly to forest productivity and these projected changes may increase growth and productivity in forests, especially where moisture is not a limiting factor. There are also a number of operational considerations that are directly affected by frost-free and growth season length, including planting, herbicide applications, and timber harvesting – the timing of planting and herbicide use may change as plants react to temperature and, in wetter regions, some sites that were only accessible in winter may dry out and be accessible in the summer months as well.

A potential downside is that a longer warm season is also beneficial to other organisms, such as competing invasive plants and both native and invasive forest pests, so the net effect on desirable species is not guaranteed to be a positive one. A great example of this has been seen in the Rocky Mountains, where bark beetles enjoyed greater over-winter survival and faster reproduction rates as a result of warmer temperatures and milder winters (Funk et al. 2014).

* Changes in the length of the frost-free season and growing season are related and expected to be similar, so we use the terms interchangeably here.


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American Institute of Physics. 2015. “The Discovery of Global Warming: The Carbon Dioxide Greenhouse Effect.” February. http://www.aip.org/history/climate/co2.htm.

Funk, J., S. Saunders, T. Sanford, T. Easley, and A. Markham. 2014. Rocky Mountain Forests at Risk: Confronting Climate-Driven Impacts from Insects, Wildfires, Heat, and Drought. Cambridge, MA: Union of Concerned Scientists and the Rocky Mountain Climate Organization.

Hansen, J., G. Russell, A. Lacis, I. Fung, D. Rind, and P. Stone. 1985. “Climate Response Times: Dependence on Climate Sensitivity and Ocean Mixing.” Science 229 (4716): 857–59. doi:10.1126/science.229.4716.857.

Intergovernmental Panel on Climate Change (IPCC). 2012a. S.I. Seneviratne, N. Nicholls, D. Easterling, C.M. Goodess, S. Kanae, J. Kossin, et al. “Changes in Climate Extremes and Their Impacts on the Natural Physical Environment.” In Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation: A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change (IPCC)., edited by C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, et al., 109–230. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

Intergovernmental Panel on Climate Change (IPCC). 2012b. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II of the Intergovernmental Panel on Climate Change, edited by C.B. Field, V. Barros, T.F. Stocker, D. Qin, D.J. Dokken, K.L. Ebi, M.D. Mastrandrea, et al., 582 pp. Cambridge, UK and New York, NY, USA: Cambridge University Press.

Intergovernmental Panel on Climate Change (IPCC). 2013. M. Collins, R. Knutti, J. Arblaster, J.-L. Dufresne, T. Fichefet, P. Friedlingstein, et al. “Long-Term Climate Change: Projections, Commitments and Irreversibility.” In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

Intergovernmental Panel on Climate Change (IPCC). 2014. Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II, III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change.

Kunkel, Kenneth E., David R. Easterling, Kenneth Hubbard, and Kelly Redmond. 2004. “Temporal Variations in Frost-Free Season in the United States: 1895–2000.” Geophysical Research Letters 31 (3): n/a – n/a. doi:10.1029/2003GL018624.

Manabe, Syukuro, and Richard T. Wetherald. 1967. “Thermal Equilibrium of the Atmosphere with a Given Distribution of Relative Humidity.” Journal of the Atmospheric Sciences 24 (3): 241–59. doi:10.1175/1520-0469(1967)024<0241:TEOTAW>2.0.CO;2.

National Climate Assessment (NCA). 2014. Climate Change Impacts in the United States: The Third National Climate Assessment. doi: 10.7930/J0Z31WJ2. U.S. Global Change Research Program.

Teskey, Robert, Timothy Wertin, Ingvar Bauweraerts, Maarten Ameye, Mary Anne McGuire, and Kathy Steppe. 2014. “Responses of Tree Species to Heat Waves and Extreme Heat Events.” Plant, Cell & Environment, doi:10.1111/pce.12417.

Global Temperature Part 1: Observed Trends

By Jennifer Hushaw

In this bulletin, we explore how the average global surface temperature has changed historically and how these changes have affected forest ecosystems. We discuss the challenge of projecting future temperature trends in next month’s bulletin.

(click to download a pdf of this article or a one-page synopsis)

2014 Recap

It seems fitting to begin our first bulletin of the new year with a look back at how the previous year stacked-up in terms of temperature trends. You may have seen the recent headlines announcing that 2014 was a year of record-setting warmth on a global scale. There is, of course, some level of uncertainty with measuring the temperature of the planet, but it is very likely (90.4% probability) that last year was one of the five warmest years since people began keeping records (NOAA 2015) and it is at least in a statistical tie with 2010 and 2005 (Hansen et al. 2015).

Four independent datasets from agencies in the U.S., the UK, and Japan have confirmed this updated temperature record, with 2014 at the top of the charts (Figure 1). Last year simply continued the upward trend that we have observed over the past several decades.


The Modern Temperature Record

The modern instrumental temperature record goes back to 1880 and is based entirely on direct measurements of land and ocean surface temperature from thermometers. It is only when we go further back in time that we need to rely on proxy temperature records from sources such as tree rings, lake and ocean sediments, ice cores, and others. What is more striking than any single record-breaking year, is the fact that 14 of the 15 warmest years in this record have occurred since 2000. This surface warming, as well as documented increases in the temperature of the deep oceans (Levitus et al. 2012), are the result of the earth’s current energy imbalance (more energy staying in than going out).

Over this entire period, the average global temperature rose around 0.85⁰C (1.53⁰F) (IPCC 2013). While that might not seem like much of a change, it is worth remembering that the difference between the world today and the depths of an ice age are on the order of 4⁰C (7.2⁰F) (Annan and Hargreaves 2015). When we talk about global average temperature, relatively small changes can actually mean a big difference in the state of the world.

This 30 second video shows how the last 135 years of temperature change have played out across the globe:


A Regional View of Global Trends

When we view these temperature trends on a world map, it becomes immediately apparent that things have not changed uniformly across all regions (Figure 2).


In particular, we have observed greater warming at high latitudes and greater warming over land than oceans. You can see evidence of this spatial pattern in the map above. Most of this regional variation can be explained by three main factors:

  1. Polar Amplification

Warming temperatures lead to the loss of bright snow and ice cover, which exposes darker surfaces (e.g. ocean water) that absorb more solar radiation and create a feedback that accelerates warming at high latitudes.

  1. Ocean Heat Capacity

Oceans have a greater heat capacity than land, which means they warm much more slowly and absorb more energy per unit of temperature increase.

  1. Internal Climate Variability

There are a number of large-scale ocean-atmosphere circulation patterns that drive our regional weather and redistribute heat within the climate system, e.g. North Atlantic Oscillation (NAO), Pacific Decadal Oscillation (PDO), El Niño – Southern Oscillation (ENSO), etc.


Ecosystem-Climate Interactions

Now we turn our discussion to where the rubber meets the road for forest managers – a look at how these observed trends affect forest ecosystems at various time scales.

Long-Term Trends

Long-term changes in temperature can profoundly affect vegetation composition across landscapes. One of the best natural experiments for examining this influence is the deglaciation of North America following the end of the last ice age around 10,000 years ago. Using pollen records, researchers have catalogued the ways in which forest composition and individual species abundance have changed over time (Jacobson et al. 1987).

Of course, temperature alone cannot explain all vegetation change, but taken with moisture balance, there is evidence to show that forest composition responded to these climate changes within centuries or less (Shuman et al. 2004) and it has been well- established that past vegetation distribution is an indirect but effective record of global and regional climate change (Williams 2009).


Short-Term Variability

Long-term temperature trends can have a tremendous effect on forest ecosystems, but it is shorter-term climate variability that is most important for the typical timescales of forest management. Perhaps one of the most well-known and influential examples of an internal cycle that drives short-term (year to year) climate variability is the El Niño – Southern Oscillation (ENSO). ENSO is essentially a change in the surface temperature of the equatorial Pacific Ocean that has a hugely important influence on temperature and precipitation throughout the globe (a brief overview of ENSO can be found here) .

It is so influential that when ENSO is in a warm phase, also known as an El Niño event, it tends to increase global average temperature for the duration of that phase (typically one to three years). In fact, circling back to the beginning of this post, it’s worth mentioning that one of the most interesting aspects of the recent 2014 global temperature record is that it was the hottest year on record that was not an El Niño year. Consequently, it is not unreasonable to expect significantly warmer global temperatures when the next El Niño event materializes sometime in the next few years.

Other ramifications of ENSO vary across the globe, but the relatively short time span between the warm (El Niño) and cool (La Niña) phases means that we have been able to observe and study the effects of this phenomenon over many years and have a solid understanding of the weather patterns associated with each phase. Figure 3 displays the typical shifts in temperature and/or moisture that occur during an El Niño year.

This ENSO influence has far-reaching impacts on forest ecosystems throughout the globe. In the boreal forests of North America, it has been shown to drive changes in fire regimes (particularly total area burned) through its effect on atmospheric weather patterns that control fuel moisture (Macias Fauria and Johnson 2008). Researchers have also linked ENSO-driven seasonal changes, specifically warmer springs during strong El Nino events, with significant increases in the rate of carbon sequestration in these boreal ecosystems (Black et al. 2000).  Closer to the source, in equatorial forests, ENSO plays a big role in the variability of productivity and phenology from year to year (van Leeuwen et al. 2013; Asner et al. 2000).


Things to Consider

Variability in the earth’s climate can be separated into two categories, external variability (due to natural and anthropogenic external forcing, e.g. sun cycles, enhanced greenhouse effect) and internal variability (due to natural internal processes within the climate system, e.g. ENSO) (IPCC 2001). This internal climate variability modulates the long-term upward global temperature trend that we have observed over the past century, and the interplay between external and internal sources of variability ultimately determines the unique climate conditions felt in each region, making it particularly relevant for land managers. In instances where the forces of internal variability create problematic conditions for forests, such as mild winter conditions that are more favorable to pests, the background trend of ever warming temperatures has the potential to exacerbate those stressors. The flip side will also be true – for example, an El Niño-induced precipitation increase, when combined with the backdrop of warmer temperatures, may lead to increases in plant productivity. In order to prepare for these risks and capitalize on these opportunities, it will be useful to anticipate how both the background trend and these interactions may play out in the future – a task we take on in next month’s bulletin on global temperature projections.




Annan, J. D., and J. C. Hargreaves. 2015. “A Perspective on Model-Data Surface Temperature Comparison at the Last Glacial Maximum.” Quaternary Science Reviews 107 (0): 1–10. doi:http://dx.doi.org/10.1016/j.quascirev.2014.09.019.

Asner, Gregory P., Alan R. Townsend, and Bobby H. Braswell. 2000. “Satellite Observation of El Niño Effects on Amazon Forest Phenology and Productivity.” Geophysical Research Letters 27 (7): 981–84. doi:10.1029/1999GL011113.

Black, T. A., W. J. Chen, A. G. Barr, M. A. Arain, Z. Chen, Z. Nesic, E. H. Hogg, H. H. Neumann, and P. C. Yang. 2000. “Increased Carbon Sequestration by a Boreal Deciduous Forest in Years with a Warm Spring.” Geophysical Research Letters 27 (9): 1271–74. doi:10.1029/1999GL011234.

Bradley, R.S. and Eddy, J.A. (figure compiled by) based on J.T. Houghton et al., Climate Change: The IPCC Assessment, Cambridge University Press, Cambridge, 1990; published in EarthQuest, Vol 5, No 1, 1991.

Hansen, James, Makiko Sato, Reto Ruedy, Gavin A. Schmidt, and Ken Lo. 2015. “Global Temperature in 2014 and 2015.” Earth Institute | Columbia University – Climate Science, Awareness and Solutions. January 16. http://csas.ei.columbia.edu/2015/01/16/global-temperature-in-2014-and-2015/.

Intergovernmental Panel on Climate Change (IPCC). 2001. Appendix | Glossary in Climate Change 2001: The Scientific Basis. Contribution of Working Group I to the Third Assessment Report of the Intergovernmental Panel on Climate Change [Houghton, J.T., Y. Ding, D.J. Griggs, M. Noguer, P.J. van der Linden, X. Dai, K. Maskell, and C.A. Johnson (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 881pp. Accessed via: http://www.grida.no/publications/other/ipcc_tar/?src=/climate/ipcc_tar/wg1/518.htm

Intergovernmental Panel on Climate Change (IPCC). 2013. “Summary for Policymakers.” In Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by T.F. Stocker, D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex, and P.M. Midgley. Cambridge, United Kingdom and New York, NY, USA: Cambridge University Press.

Jacobson, G.L., T. Webb III, and E.C. Grimm. 1987. “Chapter 13: Patterns and Rates of Vegetation Change during the Deglaciation of Eastern North America.” In The Geology of North America – North America and Adjacent Oceans during the Last Deglaciation, K-3:277–88. Boulder, Colorado: Geological Society of America.

Levitus, S., J. I. Antonov, T. P. Boyer, O. K. Baranova, H. E. Garcia, R. A. Locarnini, A. V. Mishonov, et al. 2012. “World Ocean Heat Content and Thermosteric Sea Level Change (0–2000 M), 1955–2010.” Geophysical Research Letters 39 (10): n/a – n/a. doi:10.1029/2012GL051106.

Macias Fauria, Marc, and EA Johnson. 2008. “Climate and Wildfires in the North American Boreal Forest.” Philosophical Transactions of the Royal Society B: Biological Sciences 363 (1501): 2317–29. doi:10.1098/rstb.2007.2202.

National Oceanic and Atmospheric Administration. 2015. “State of the Climate: Global Analysis – Annual 2014 | Calculating the Probability of Rankings for 2014.” NOAA National Climatic Data Center. January. http://www.ncdc.noaa.gov/sotc/global/2014/13/supplemental/page-1.

Shuman, B., P. Newby, Y. Huang, and T. Webb III. 2004. “Evidence for the Close Climatic Control of New England Vegetation History” 85 (5): 1297–1310.

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Ecologically-Relevant Changes in Temperature Variability

By Jennifer Hushaw

(click to download a pdf of this article or a one-page synopsis)

The majority of the conversation about global or regional temperature involves discussion about how the average annual or monthly temperature has changed over time. However, for many organisms it is actually the range of temperatures they experience throughout the day (the diurnal temperature cycle or DTC) and throughout the year (the annual temperature cycle or ATC) that is most important in terms of biological processes, phenology, and other ecological interactions. Think, for example, about any forest pest that is a concern in your region – chances are its population is significantly affected by the magnitude of temperature ranges from summer to winter and from night to day. In this bulletin, we will explore this topic further and highlight a recent analysis that sheds new light on global temperature trends during the period 1975-2013.

Temperature Variation

Before we delve into the recent research, let’s take a moment to discuss how daily and annual temperature ranges have historically varied across the globe.

The range of temperatures experienced over a given year, or the annual temperature cycle (ATC), is generally larger in higher latitudes because winters are so much colder in those areas, compared to the tropics. Therefore, ATC increases as you move from the equator to the poles (Figure 1).


In contrast, the daily temperature cycle (DTC) is typically much larger in tropical regions than in higher latitudes because there is more intense solar heating during the day in the tropics. This means that DTC generally decreases as you move from the equator toward the poles (as you can see in this map—Bonebrake & Deustch 2012, Figure D1). The combination of DTC and ATC gives each latitude a unique temperature cycle profile (Figure 2).


Study Overview

So what did the researchers find out about how these temperature profiles have changes in recent decades?

In a study published in the journal Nature Climate Change (Wang and Dillon 2014), researchers estimated the diurnal (i.e., daily) and annual temperature cycles (DTC and ATC) from 1975 to 2013 by analyzing 1.4 billion hourly temperature measurements from over 7,900 weather stations around the globe.

These were the key findings:

(1) There has been a global increase in DTC since 1975, and this effect was stronger at higher latitudes.

Note: See Wang & Dillon 2014, Figure 2:
• Figure 2 (b)—showing the change in DTC for polar (grey), temperate (blue), and tropical (red) regions
• Figure 2 (f) showing a map of the difference in DTC from 1975-1980 and 2010-2013

(2) There has been a change in the magnitude of the ATC since 1975, but the direction and magnitude of the change varied by latitude:

decreased ATC in polar regions
increased ATC in temperate regions
no change in tropical areas

This effect was also stronger at higher latitudes.

(3) Altogether, these changes indicate that the temperature cycles of high latitude climates are becoming more like the tropics (a phenomenon the researchers call a ‘flattening’ of the global temperature profile) (Figure 3).

What’s New?

The observation that there has been an increase in the magnitude of the daily temperature cycle, is particularly interesting because it differs from previous research. The last global analysis of daily temperature range was done almost ten years ago and most of the subsequent regional studies have suggested that the daily temperature range has either decreased or remained mostly unchanged (IPCC 2013).

However, this study is data-rich, global in scope, and it employs a new statistical approach. Many of the techniques commonly used for this type of analysis require regularly sampled data, which forces researchers to average their temperature records over a particular time window, such as a monthly average. That may be fine for certain types of research questions, but it can also ‘smooth over’ changes that may be happening on the timescales that are most relevant to organisms. The type of analysis used here does not require that kind of data selection or aggregation, so they were able to capture more of the biologically-relevant temperature changes.


The Implications

The observed changes in the range of daily and annual temperatures will have important consequences for many species. In particular, short-lived organisms, such as the mountain pine beetle, have already benefited from increased winter temperatures that have allowed their populations to increase. Organisms adapted to a larger range of temperatures may fare better under conditions where the DTC is increasing, as opposed to more specialized organisms that can only survive or reproduce within a narrow temperature range. Also, in places where the daily and annual temperature ranges are becoming more similar (as they are in the tropics), we may see seasonal organisms that can now persist throughout the year. The most immediate concern for forest managers will be the effect of these changes on the life cycle and population dynamics of various pests and diseases.


Putting Things in Context

The researchers in this study primarily focused on what has happened with regard to temperature cycles, rather than why those changes may have happened. Other studies suggest that changes in the level of solar radiation reaching earth’s surface (resulting from factors such as pollution in the atmosphere and cloud patterns) are a big piece of the DTC puzzle. In fact, clouds have a particularly large influence on daytime temperatures, but cloud patterns are difficult to model, which is why they are an important area of uncertainty that explains why most models do not agree on future projections of DTC (Lobell et al. 2007). At the same time, cloud cover is an example of an atmospheric feedback that is influenced by the backdrop of a warming planet. These kinds of complex interactions illustrate why it is challenging to determine the role global warming will play in influencing existing climate dynamics.

Climate is a broad and dynamic subject, with new research and refinements to our understanding emerging every week. The potential for change in temperature variability is one of questions that will be particularly important because of its ecological implications. This studies raises a number of important questions on this subject and we will be sure to keep you informed as the science evolves.




Hartmann, D.L., A.M.G. Klein Tank, M. Rusticucci, L.V. Alexander, S. Brönnimann, Y. Charabi, F.J. Dentener, E.J. Dlugokencky, D.R. Easterling, A. Kaplan, B.J. Soden, P.W. Thorne, M. Wild and P.M. Zhai, 2013: Observations: Atmosphere and Surface, Section Diurnal Temperature Range (p. 188). In: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change [Stocker, T.F., D. Qin, G.-K. Plattner, M. Tignor, S.K. Allen, J. Boschung, A. Nauels, Y. Xia, V. Bex and P.M. Midgley (eds.)]. Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA.

Lobell, D., Bonfils, C., and Duffy, P. 2007. Climate change uncertainty for daily minimum and maximum temperatures: A model inter-comparison. Geophys. Res. Lett. 34(5). DOI:10.1029/2006GL028726.

Wang, G. and Dillon, M. 2014. Recent geographic convergence in diurnal and annual temperature cycling flattens global thermal profiles. Nature Climate Change. 4(11): 988-992.

Managing Forest Stands to Minimize Wind and Ice/Heavy Snow Damage: Part Two

By Si Balch

(click to download a one-page synopsis of this article)

This CSLN Bulletin provides a synopsis of management actions that can be employed to maximize the resiliency of forest stands to damage by wind, ice and heavy snow. The goal is the development of forest stands and trees that can withstand 70 MPH wind gusts. Part One of this Bulletin provides background on windfirmness.


Silvicultural Techniques for Wind Firmness

The best way to promote wind firmness is to reduce stocking levels. Lower-stocked stands have less standing inventory at risk, as well as bigger trees that add more value and are more efficient to harvest. The open quality of these stands also allows for more air movement, which creates a drier environment, so there is less fungal growth; interrupted small insect movement, such as bark beetle flight; and better conditions for birds to hunt insects.

Thinning is a better tool for building wind firmness in young stands, but it is possible to increase windfirmness in older stands if it is done very carefully. Below are more detailed summaries of what to consider when thinning stands of different ages.

Thinning Young Stands – sapling (conifers) to small pole (hardwoods)

This develops more drought resistant trees and increases the economic value of residual trees through better trunk growth caused by swaying (See Part 1- How Trees Grow and Respond to Physical Stresses). Young stands that are thinned have a much better chance of developing windfirmness than older stands. This is another reason to start new cohorts of trees across the ownership, so they can be thinned for greater windfirmness.

Things to consider:

  • Soil Moisture
    • Put your efforts on deeper, better drained sites. Shallow rooted or frequently saturated sites are poor candidates for thinning to develop windfirmness. On these sites, dense stands managed on an even-aged basis are probably a better way to reduce risk.
    • Wet soils are weaker at holding roots, even if it is deep soil. Thus, soils that are saturated and unfrozen for longer periods of time are likely to have more blowdown.
  • Crown Class
    • Remove suppressed and intermediate trees that are the same age as overstory trees. Trees in the upper canopy are already expressing both wind firmness and good growth.
    • Maintain high crown ratios. Conventional wisdom was that 30% crown was sufficient, new wisdom is that ratios in the 50% range are preferred.
  • Spacing
    • Space residual trees so that the crowns have space to sway some, but also have some support from neighboring trees near the likely limit of “sway stability”.
    • Sufficient spacing allows swaying that causes trees to increase diameter growth in those parts of the trunk where the stress of that movement occurs. The trick is to manage it so most of that growth occurs on the commercially valuable section of the tree. This will vary by species, site, and tree form.

Thinning Intermediate or Multi-aged Stands

It is difficult and risky to develop a truly windfirm stand when starting with an unmanaged, middle-aged forest. Working with forests in this condition is likely to be disappointing because you cannot simply do a typical partial harvest and have a guaranteed increase in windfirmness.

Forests that have been managed are better candidates for windfirmness development, but they also present their own set of challenges. These stands may have had a series of partial harvests over time and thus have a variety of age classes. The classic “uneven-aged” forest is really a forest with several individual age cohorts. Managing these for windfirmness, or any other purpose, requires knowing which age cohort the trees you are managing fall into. Treat the old trees, intermediates, and young trees appropriately to each development stage. Crown shape and bark character are normally good ways to identify age.

Despite the challenges, there are a number of thinning practices that can result in significant benefits to windfirmness and other forest values. Thinning also increases the economic value of residual trees through better trunk growth caused by swaying (See Part 1- How Trees Grow and Respond to Physical Stresses).

The spatial structure of the forest canopy can affect wind dynamics. In particular, dense crowns create a smoother surface and deflect wind as a continuous front. Wind flows up and over this tree front and then curls into a roll. Significant damage often starts 2 to 3 tree heights back from the edge, where this roll comes back into the crowns. Understory growth, regeneration, shrubs etc, do absorb some of the stress of wind, as well as provide habitat for more bird species, which prey on insects.


  • Trees growing at the edge of a forest.
    • These individuals are more windfirm, having grown in windier conditions. Be very cautious about thinning these and disrupting their unified front.
  • The stand edge perpendicular to the prevailing wind.
  • Well-formed dominant or co-dominant trees, i.e. trees with
    • Crown ratio of over 40
    • Balanced crown
    • Straight and vertical
  • Suppressed and intermediate trees under chosen crop trees.
  • Patches of trees between chosen crop trees that are not competing with crop tree crowns.


  • Trees likely to fail due to:
    • Poor Form
      • Co-dominant stems
      • Branches with included bark in the joint
      • Heaved roots
      • Height:diameter ratios over 90
      • Taller than many surrounding trees
    • Poor Health
      • Stem decay
      • Root decay
      • Large dead branches
  • Species either prone to blow down or to develop internal decay.
    • Less windfirm or more rot likely species from the list below.
  • Trees competing with crowns of chosen crop trees.
    • Remove trees whose crowns touch the crop tree crown. The desired outcome is more space for the crop tree crown to sway, but at the limit of that sway to meet a neighboring tree crown and get some support. This increased but limited sway space will result in the crop tree becoming more wind resistant and adding value through stem thickening in response to the added stress of swaying.

Note: In even-aged stands of spruce/fir, harvesting in patches results in less wind damage than uniform thinning.


Acclimation Period

Recently thinned stands are more vulnerable to wind and ice damage. It takes time for the residual trees to develop the extended root systems and the stronger, more tapered stems needed to withstand the increased motion cause by more wind within the stand. The commonly expressed time is five to ten years, but there is little research to back this up.

  • There is a difference in susceptibility between recently thinned and un-thinned stands, but it is not a very large difference. Even though a thinned stand may suffer a higher % loss, it may actually lose fewer trees than an un-thinned stand because it has fewer trees to start.
  • Recently thinned stands have a more irregular crown surface and therefore generate more turbulence in the wind flow. This leads to a higher wind load on individual trees and consequently more swaying. More swaying can lead to more trunk breakage and windthrow. This increased risk will last until the trees in the stand have acclimated to their new spacing.


Consider Setting a Terminal Height

Terminal height may be a concept worth introducing to tree management. Taller trees regardless of age, species, or any other factor, are more susceptible to windthrow, so setting a target terminal height may be a valid strategy.


Relative Wind Firmness  (listed from most to least windfirm)

Northern Species

  1. Yellow Birch
  2. Sugar Maple, Beech, Hemlock
  3. Red and White Oaks
  4. Red Maple
  5. White Pine
  6. Larch
  7. Poplar
  8. Other Conifers

Southern Species

  1. Live Oak
  2. Bald Cypress
  3. Blackgum
  4. Sweetgum
  5. Southern Red Oak
  6. White Oak
  7. Beech
  8. Sugar Maple
  9. Sycamore
  10. Ash
  11. Longleaf Pine
  12. Loblolly Pine
  13. Slash Pine
  14. Red Cedar
  15. Water Oak
  16. Cherry
  17. Basswood
  18. Yellow Poplar
  19. Red Maple
  20. Hickory


Other Methods for Reducing Risk of Wind/Ice/Snow Damage

  • Get Insurance against loss from wind, fire and possibly other threats.
    • Insurance is available from Outdoor Underwriters, Inc. of Columbia SC. The policies come through Lloyd’s of London and each insured property is evaluated based on location, a questionnaire, and assessment of the management plan. As an example, a 54 acre tract in Maine with $25,000 dollars of timber, can be insured against wind and fire for $500/year with a $5000 deductible.
  • Put equipment on site if you know a big storm is coming.
    • Weather forecasting is pretty good, so if you think there will be significant damage and road blockage, then putting machinery at key locations may be a good idea. Winds coming from abnormal directions are particularly damaging.


Bibliography, references, and credits:

  • William Ostrofsky – Maine State forest pathologist – personal correspondence
  • Greg Adams – JD Irving , Limited – Manager of research and development – personal correspondence
  • Tony Filauro – Retired Great Northern Silviculturalist – personal correspondence
  • Mike Dann – Retired Seven Islands Chief Forester – personal correspondence
  • Michael Greenwood – Univ. of Maine – personal correspondence
  • Frank Telewski – Univ. of Michigan – personal correspondence
  • Tim Scott – USFS – Forest Products Lab – Madison Wisconsin – personal correspondence
  • Dr. Claus Mattheck – Germany – tree biomechanics – website http://www.mattheck.de/english/english2.htm
  • JuliaSchofield, CISR, Outdoor Underwriters, Inc, 140 Stoneridge Drive, Suite 265,, Columbia, South Carolina 29210 – personal correspondence
  • Living with Storm Damage to Forests – What science can tell us – European Forest Institute – 2013 – Barry Gardiner, Andreas Schuck, Mart-Jan Schelhass, Chistophe Orazio, Kristina Blennow, Bruce Nicoll
  • Predicting Stem Windthrow Probability in a Northern Hardwood Forest Using a Wind Intensity Bio-Indicator Approach, Philippe Nolet1,2, Frédérik Doyon1,2, Daniel Bouffard3 1Insitut des Sciences de la Forêt tempérée, Ripon, Canada 2Université du Québec en Outaouais, Gatineau, Canada 3Insitut Québécois d’Aménagement de la Forêt Feuillue, Ripon, Canada, – Open Journal of Forestry 2012. Vol.2, No.2, 77-87 Published Online April 2012 in SciRes (http://www.SciRP.org/journal/ojf)
  • Tree Survival 15 Years after the Ice Storm of January 1998, USFS, Northern Research Station, Research paper NRS-25, February 2014. Shortle, Smith and Dudzik.
  • Overview of techniques and procedures for assessing the probability of tree failure – David Lonsdale, 33 Kings Road, Alton, Hampshire GU34 1PX, UK
  • wind and trees:lesson learned from hurricanes – chapter 5 – Publication No FOR 118 – Mary Duryea & Eliana Kampf – University of Florida, IFAS extension
  • Are Irregular stands more windfirm? – W.L. Mason – Forest Research, Northern Research Station, Roslin, Midlothian, EH25 9SY, Scotland
  • A mechanistic model for calculating windthrow and stem breakage in Scots pine at stand edge; by H Peltola – ‎1993 – Silva Fennica. 1993, Vol. 27 N20 2: 99-111.
  • Crown structure and wood properties: Influence on tree sway and response to high winds- 2009 Damien Sellier SCION, 49 Sala Street, Rotorua 3010, New Zealand and
  • Thierry Fourcaud CIRAD, UMR AMAP, TA-A51/PS2, Boulevard de la Lironde 34398 Montpellier Cedex 5, France                3732/ajb.0800226Am. J. Bot. May 2009 vol. 96 no. 5 885-896
  • Size- and Age-Related Changes in Tree Structure and Function:Size- and Age-Related Changes In Tree Structure and Function  – Frederick C. Meinzer, Barbara Lachenbruch, Todd E. Dawson Springer, Jun 29, 2011
  • Should Newly Planted Trees Be Staked and Tied? By William R. Chaney, Professor of Tree Physiology – Purdue – The Department of FNR-FAQ-6 FORESTRY AND NATURAL RESOURCES
  • http://www.uky.edu/~jmlhot2/Resources/The%20Practice%20of%20Silviculture-Smith-ch.2.pdf The response of trees to individual thinning and pruning.
  • Storm Damaged Trees: Prevention & Treatments, Kim D. Coder, Professor Silvics/Ecology
    Warnell School of Forest Resources , The University of Georgia, March, 1995



Managing Forest Stands for Wind and Ice/Heavy Snow Damage – two threats likely to increase with climate change

By Si Balch

(click to download a one-page synopsis of this article)

This Bulletin will be delivered in two parts; Part 1 provides background on stand and tree vulnerability to wind damage, Part 2 will cover management practices to enhance resiliency to wind damage with the goal of stands capable of withstanding 70 MPH wind gusts.


Forests are made up of stands that are, in turn, made up of individual trees, and we can think about the topic of building wind resistance at both of these scales. Forest stands can be managed to be windfirm, and the trees within those stands can also be managed to be windfirm. Similar principles apply to resistance against ice/snow damage as well.

There are two key areas of wind damage risk in trees: one is the tree’s grip on the ground and the other is tree stem strength. Crown breakage from snow and ice is ugly, but unless the main stem is broken, most trees recover pretty well. In fact, studies show that most hardwoods can lose up to 75% of their crown and recover. Of course, the downside is the increased threat of new internal decay and grade loss.

While there are certainly steps you can take to reduce the risk of windthrow, no stands or trees can be reasonably expected to withstand extreme winds over 95 MPH that are produced by derechos (line storms), downbursts (micro & macro), tornadoes and category 2+ hurricanes, regardless of the level of management. The greatest risk may be from derechos, which occur in warm weather along lines of severe thunderstorms that develop in humid air in the boundary between warm and cold air masses. These conditions are becoming more common in the Northeast, resulting in the potential for an increased frequency of derechos. Of course, some trees will withstand extreme winds over 95 MPH, but managing for that level of resistance does not seem reasonable in a forest situation, although it may be a suitable approach in parks.

Forest stands and individual trees may have few defenses against wind speeds over 95 MPH, but the relevant metric for forest management is critical wind speed, which is defined as the speed at which trees begin to blow over. It can be pretty low, depending on site and species. Shallow rooted trees on wet sites with saturated conditions may begin to topple at 40 MPH. Wind speeds over 50 MPH begin to damage many susceptible trees. The wind scales you normally hear referred to have the following wind speed associations:

· Beaufort Wind Scale defines hurricanes as having winds over 73 MPH.

· Saffir-Simpson Hurricane Wind Scale Category 1 hurricane is from 73-94 MPH

· Europeans define extreme storms as having wind gusts from 75-80 MPH

· Fujita Tornado Scale defines an F0 storm at 40-72 MPH and an F1 storm at 73-112 MPH

With an increased risk of strong storm events under future climate scenarios, the important question is: How do we develop stands and trees with sufficiently strong ground grip and stem strength to withstand 70 MPH gusts?

What do we know?

These characteristics are known to increase the risk of wind damage, by either blowdown or severe breakage.

· Tree height – Taller trees regardless of age, species or any other factor are more susceptible. This is simple physics, a longer lever (height) exerts more force at the fulcrum (root collar).

· Stem taper is an indicator of susceptibility. Trees with little taper are more susceptible, particularly small diameter trees. This is about stem strength because thicker stems are stronger. Trees grown in the open, where they are exposed to wind, have very tapered trunks. This makes them windfirm, but they are of little commercial value. The trick is to find the right combination of strength and value.

· Height diameter ratio – This relationship is expressed in like units: A 60 feet tall tree that is 10 inches DBH has a ratio of 72; (60X12=720/10=72). Trees with ratios between 60 and 80 tend to be stable, while trees with ratios over 100 are at high risk of damage.

· Shallow or restricted roots – Whether caused by thin soil, wet soil or species characteristics, shallow or restricted roots are a disadvantage because they reduce the tree’s grip on the ground. A related characteristic is small root area or lack of spread. In several big storms, poplar seemed particularly vulnerable. The root balls at the bottom of these trees were noticeably smaller than one might expect. Also planted trees whose roots were constrained in the pot may never grow a widely spread root system.

· Saturated unfrozen soils are physically weaker so the tree’s grip on the ground is diminished.

· Trees weakened by decay. Anytime the roots, trunk or branches have internal decay the structure is weakened and less able to withstand the bending force of wind or ice or snow.

· Trees with branching patterns that have proven to be susceptible. These include co-dominant stems and branch joints that include bark in the joint.

· Old trees, simply because older trees tend to be taller and have a higher likelihood of decay.

· Trees in recently thinned stands. Trees that have grown in fully stocked conditions, supported and constrained by adjacent trees, are susceptible when those adjacent trees are removed. This is a dilemma. Common thought is that it takes about five years for trees to adjust to more open conditions, by expanding their roots, crowns and stem configurations.

How trees grow and respond to physical stresses.

Understanding how trees respond to physical stress can lead to better management for windfirmness, ice resistance and economic value. To withstand deflecting forces, trees strengthen themselves by adding wood at the points of stress. The well-known phenomena of compression and tension wood in tree stems are exaggerated examples of this growth response. An individual tree will attempt to grow straight and balanced, in order to increase its ability to reproduce and collect carbohydrates from sunlight. This involves focusing wood growth in particular places, such as reinforcement where bending stress occurs. This differentiated growth is driven by auxin, which is the primary plant growth hormone. It originates in the buds and is distributed throughout the tree via a complex transfer system.

This additional growth occurs in the parts of the tree that most commonly experience high stress, such as points within the crown where branches move around a lot, the base of the crown where the whole moving crown meets the main trunk, and at the stump were the whole tree meets the root system. Thus, a tree grown in completely open conditions develops a very large crown that can absorb light from all directions and an exaggerated root collar to deal with the bending stress of the large moving crown. Growth is concentrated on crown expansion and stem/root stability, rather than height growth. In contrast, trees grown in tight, fully stocked stands must deal with very different conditions. In this case, there is relatively little stem bending stress, but there is strong vertical competition for light, which results in tall, relatively thin trees.

Understanding these growth patterns can be used to increase both the windfirmness and economic value of trees. These will be explored in Part 2 next month.

Bibliography, references and credits

· Dr. William Ostrofsky – Maine State forest pathologist – personal correspondence

· Greg Adams – JD Irving , Limited – Manager of research and development – personal correspondence

· Tony Filauro – Retired Great Northern Silviculturalist – personal correspondence

· Mike Dann – Retired Seven Islands Chief Forester – personal correspondence

· Dr. Michael Greenwood – Univ. of Maine – personal correspondence

· Dr. Frank Telewski – Univ. of Michigan – personal correspondence

· Tim Scott – USFS – Forest Products Lab – Madison Wisconsin – personal correspondence

· Prof. Dr. Claus Mattheck – Germany – tree biomechanics – websitehttp://www.mattheck.de/english/english2.htm

· Julia Schofield, CISR, Outdoor Underwriters, Inc, 140 Stoneridge Drive, Suite 265,, Columbia, South Carolina 29210 – personal correspondence

· Living with Storm Damage to Forests – What science can tell us – European Forest Institute – 2013 – Barry Gardiner, Andreas Schuck, Mart-Jan Schelhass, Chistophe Orazio, Kristina Blennow, Bruce Nicoll

· Predicting Stem Windthrow Probability in a Northern Hardwood Forest Using a Wind Intensity Bio-Indicator Approach, Philippe Nolet1,2, Frédérik Doyon1,2, Daniel Bouffard3 1Insitut des Sciences de la Forêt tempérée, Ripon, Canada 2Université du Québec en Outaouais, Gatineau, Canada 3Insitut Québécois d’Aménagement de la Forêt Feuillue, Ripon, Canada, – Open Journal of Forestry 2012. Vol.2, No.2, 77-87 Published Online April 2012 in SciRes (http://www.SciRP.org/journal/ojf)

· Tree Survival 15 Years after the Ice Storm of January 1998, USFS, Northern Research Station, Research paper NRS-25, February 2014. Shortle, Smith and Dudzik.

· Overview of techniques and procedures for assessing the probability of tree failure – David Lonsdale, 33 Kings Road, Alton, Hampshire GU34 1PX, UK

· wind and trees:lesson learned from hurricanes – chapter 5 – Publication No FOR 118 – Mary Duryea & Eliana Kampf – University of Florida, IFAS extension

· Are Irregular stands more windfirm? – W.L. Mason – Forest Research, Northern Research Station, Roslin, Midlothian, EH25 9SY, Scotland

· A mechanistic model for calculating windthrow and stem breakage in Scots pine at stand edge; by H Peltola – ‎1993 – Silva Fennica. 1993, Vol. 27 N20 2: 99-111.

· Crown structure and wood properties: Influence on tree sway and response to high winds – 2009 Damien Sellier SCION, 49 Sala Street, Rotorua 3010, New Zealand and

Thierry Fourcaud CIRAD, UMR AMAP, TA-A51/PS2, Boulevard de la Lironde 34398 Montpellier Cedex 5, France 10.3732/ajb.0800226Am. J. Bot. May 2009 vol. 96 no. 5 885-896

· Size- and Age-Related Changes in Tree Structure and Function: Size- and Age-Related Changes In Tree Structure and Function  – Frederick C. Meinzer, Barbara Lachenbruch, Todd E. Dawson Springer, Jun 29, 2011

· Should Newly Planted Trees Be Staked and Tied? By William R. Chaney, Professor of Tree Physiology – Purdue – The Department of FNR-FAQ-6 FORESTRY AND NATURAL RESOURCES

· http://www.uky.edu/~jmlhot2/Resources/The%20Practice%20of%20Silviculture-Smith-ch.2.pdf The response of trees to individual thinning and pruning.

Stream Crossings and Climate Change (Part 2)

By Si Balch and Eric Walberg

(click to download a one-page synopsis of this article)

The August 2014 Climate Smart Land Network Bulletin provided an introduction to stream crossings and climate change. The September Bulletin provides additional detail on using watershed and stream corridor characteristics to design and size stream crossings that perform well in a changing climate. Stream crossings that maintain the slope, structure and dimensions of the natural streambed are proving to be robust in large flood events and have the added benefit of maintaining full ecological function and connectivity of streams. Figure 1 shows a hierarchy of stream connectivity that occurs as increasingly large portion of the stream channel and surrounding floodplain are spanned by a crossing.1 Moving up this hierarchy also increases the size of storm event that can be accommodated without damage to the structure.


Figure 1. Crossing Width and Stream Connectivity1

Determine the Minimum Opening Size

Stream channels are a manifestation of the water flow volumes and frequencies in the surrounding watershed. Identification of the normal high water mark provides a reference for identifying the minimum opening size needed to accommodate a given design storm. The normal high water mark is evidence of a frequent surface water elevation below bankfull that can usually be identified by physical scarring along the bank. Bankfull is typically defined as the point at which the stream channel transitions into the floodplain. Indicators of normal high water may include erosion, shelving, changes in soil characteristics, destruction of terrestrial vegetation, the presence of litter or debris, or other distinctive physical characteristics.1 The 10-year peak is approximately 2.5 times larger than the normal high water flow, the 25 year is approximately 3.5 times larger and the 50 year peak is approximately 4.5 times larger.

The first step in determining the opening size needed to accommodate a selected design storm is calculating the area of the stream bed (stream width in feet at the normal high water mark X average depth in feet). Multiply the area by 2.5 if the goal is to accommodate a 10-year storm, 3.5 if the goal is to accommodate a 25 year storm, and 4.5 if the goal is to accommodate a 50-year storm. This calculation will result in the opening size in square feet. If a culvert is to be used for the crossing it can be selected from the chart below. Note that if peak flows increase to 15% larger than historic norms going up one culvert size will address that. Embedding a culvert to match stream elevation and allow for inclusion of natural substrate may reduce volume by up to 35% and require sizing up to maintain desired flow capacity. The green section of the chart highlights the most commonly used culvert sizes. For opening sizes above 20 square feet structures other than culverts are typically used. If a pipe arch is to be used, double the opening size from the previous calculation and select the corresponding diameter from the chart.


Design the Crossing

As precipitation patterns continue to change in response to a warming climate stream crossings will be subjected to an increase in heavy downpours. The linkage between the increase in heavy downpours and flooding will be most direct in those watersheds that are vulnerable to flash flooding. Taking the regional trends in both extreme and total precipitation into account in designing and installing crossings will minimize the likelihood of having to spend time and money repairing or replacing the structures. The USGS StreamStats Program (http://water.usgs.gov/osw/streamstats/) provides a GIS-based interface to stream gage data and includes tools to estimate flows at non-gaged locations.

Temporary Stream Crossings: Temporary crossings have several advantages over permanent crossings including limiting exposure of the structure to flood events, limiting unauthorized property access and avoiding permanent stream corridor alteration. Temporary bridges have the added advantage of accommodating a larger design storm as compared to typically sized temporary culverts. In addition, a recent evaluation of cost benefit tradeoffs of temporary bridges ranked them low on cost and high on protection of water quality.2 Temporary bridges perform best when installed on abutments on both sides of a stream. This practice will both stabilize the bridge and facilitate removal in frozen conditions.3

Temporary culverts are typically sized to accommodate at 10-year storm if they will be in place during spring runoff or located in watersheds that are prone to flash flooding. Recommended minimum diameter is 12 inches as smaller sizes difficult to clear if they become clogged.3

Permanent Stream Crossings: Permanent crossings merit detailed design due to cost, greater potential for exposure to large flood events and potential for long-term stream impacts. Matching the width, slope and materials of a similar reference stream reach will improve the resiliency of the crossing to large storm events, maximize stream connectivity and preserve habitat value of the stream. Rules of thumb for a robust permanent crossing include:

· Locate a reference stream reach that is similar to the stream segment to be spanned and pattern the reconstructed stream segment on the reference segment,

· Identify stable endpoints for the crossing structure and span the bankfull width of the channel,

· Match the slope and elevation of the stream, embed culverts or use open bottom structures to maximize stream connectivity,

· Use substrate in the crossing that matches upstream and downstream reaches.

Installing crossings that maintain stream width and substrate may require openings that are larger than the size needed to handle stream volume. An 18” culvert may handle the estimate water flow, but to maintain stream width it might take a 24” culvert. For this reason many crossings are now being installed as arches or bridges, because they are less expensive and easier to install than huge culverts. Building crossings that meet these additional considerations normally result in openings that meet the 50 or more year peak flow estimates.

Recommendations for Permanent Crossings: Calculate the area of the stream bed as previously described. Multiple by 3.5 to calculate the minimum opening size needed to accommodate a 25-year storm. Increase the structure opening size as needed to ensure that the bankfull width of the stream channel is spanned.1 Install a structure that maintains stream width, slope and substrate and you will have a crossing that will likely withstand all but the most extreme flows.

Note on Regulatory Compliance: Regulatory requirements for stream crossings vary by location and are beyond the scope of this Bulletin. Please be sure you are familiar with the regulatory requirements for the location where you are working.

1. Clarkin, K. Stream Simulation: An Ecological Approach to Providing Passage for Aquatic Organisms at Road-Stream Crossings. (2008). at <http://www.stream.fs.fed.us/fishxing/publications/PDFs/AOP_PDFs/Cover_TOC.pdf>

2. Wilkerson, E. & Gunn, J. Quantifying Benefits and Costs of Applying Improved Forest Management Practices for Protecting Water Quality in the Northeast U.S. (Manomet Center for Conservation Sciences, 2012).

3. Blinn, C., Dahlman, R., Hislop, L. & Thompson, M. Temporary Stream and Wetland Crossing Options for Forest Management. (U.D. Forest Service, North Central Research Station, 1998).

Additional Sources:

Keith Kanoti – Maine Forest Service – Personal correspondence

John Magee – NH Fish & Game – Personal correspondence

Scott Olson – USGS – NH – Personal correspondence

Charles Hebson – Maine DOT – Chief Hydrologist – Personal correspondence

Adam Cates – Dirigo Timberlands – Personal correspondence

Stream Smart Crossing Principles – Maine Forest Service – 2013

Maine Audubon – http://maineaudubon.org/wp-content/uploads/2012/04/StreamSmart-How-To-TechnicalGuidance.pdf

Modeled Future Peak Streamflows in Four Maine Coastal Rivers – Hopkins & Dudley – USGS 2013

Urban Hydrology for Small Watersheds – TR55 – 1986

Culvert Material Cost Comparison – New England Environmental Finance Center for the Maine Dept. of Transportation Office of Environmental Planning – 2010

Trends in Extreme Precipitation Events for the Northeastern United States – 1948 – 2007 – Spierre and Wake – Univ of NH – 2010

Estimation of Flood Discharge at Selected Recurrence Intervals for Streams in New Hampshire – Olson – 2010 – USGS Scientific Investigations Report 2008-5206

The USDA Soil Conservation Service (SCS) Methods; specifically: “Urban Hydrology for Small Watersheds,” June 1986 Soil Conservation Service Technical Release #55.
The United States Geological Survey (USGS) Methods; specifically: U.S. Geological Survey. 1975. “A Technique for Estimating the Magnitude and Frequency of Floods in Maine.” Open- file Report 75-292.

Stream Crossings and Climate Change (Part 1)

By Eric Walberg and Si Balch

(click to download a pdf of this article or a one-page synopsis of this article)

Several aspects of climate change should be considered in the design and construction of stream crossings for forest access. These factors include both the need to plan for resilient structures against the backdrop of continued change in precipitation patterns and the need to minimize fragmentation of streams as climate warms and aquatic species move in search of suitable habitat. As the atmosphere warms an increasing percentage of precipitation is coming in heavy precipitation events. Warming ocean temperatures provide a conducive environment for larger, more powerful coastal storms. Both of these trends are increasing the potential for extreme precipitation events that can increase erosion and sedimentation and damage improperly designed stream crossings.

Stream crossings can play a significant role in limiting movement of fish and other aquatic species along stream corridors if not properly designed. As stream temperatures warm the opportunity for cold water fish to move to refuge areas with sufficiently cool waters is increasingly important to their viability. Emerging approaches to linking infrastructure and ecological resiliency through the design of structures that take stream function and form into account hold the promise of simultaneously addressing both changing precipitation patterns and ecosystem stressors.

Changing Precipitation Patterns

Precipitation patterns are changing as the climate warms. The warming of the atmosphere and the oceans have combined impacts that are causing long term changes in both global precipitation intensity and regional precipitation totals.

  • Warming atmosphere:
    • As the atmosphere warms it can hold more water vapor (7% increase for every 1 degree C increase in atmospheric temperature).1 This is a key factor in the global increase in the percentage of precipitation coming in heavy precipitation events. As the climate continues to warm this trend is projected to continue with a diminishment in light and moderate precipitation and continued increase in heavy precipitation.
    • As the atmosphere warms evaporation rates are increasing. In geographic areas with ample surface water this leads to increasingly high levels of water vapor in the atmosphere. In areas with limited surface water this phenomenon can exacerbate drought.1 In a general sense wet regions of the planet are getting wetter and dry regions are getting dryer.
  • Warming oceans:
    • As sea surface temperatures warm evaporation rates are increasing.
    • As sea surface temperatures warm more energy is available to fuel tropical storms, hurricanes and extra-tropical coastal storms. This trend is creating background conditions that are conducive for larger, more powerful storms with both stronger winds and more precipitation.2 The influence of climate change on the frequency of ocean storms is uncertain leading to differing projections of future storm frequency.

As a result of these trends, the percentage of total precipitation coming in heavy precipitation events is increasing across all of the U.S. and Canada. Total annual precipitation is also changing with some regions such as the northeastern U.S. and Canada getting more total precipitation over time and other regions such as the southwestern U.S. receiving less. These long-term, climate change-driven trends in precipitation interact with cyclical phenomenon such as the El Nino/Southern Oscillation and the Atlantic Multi-decadal Oscillation. Depending on the phase of these cyclical patterns they can enhance or retard the precipitation changes associated with longer-term climate trends.

Statistical analysis of regionally-specific precipitation rates and frequencies provides an important benchmark for sizing of stream crossings. However, as climate continues to warm it is likely that historic statistics will become a less reliable predictor of future precipitation patterns. Two of the primary sources of precipitation frequency data for the U.S. are Technical Paper No. 40: Rainfall Frequency Atlas of the United States, 1961 (http://www.nws.noaa.gov/oh/hdsc/PF_documents/TechnicalPaper_No40.pdf) and the NOAA Precipitation Frequency Data Server (http://dipper.nws.noaa.gov/hdsc/pfds/). Both of these sources are based on the notion of a static climate. The NOAA Precipitation Frequency Data Server is intended as an update to Technical Paper 40 but due to funding limitations the project has not been completed for all of the U.S. To address this gap and provide insight on changes in extreme precipitation, the Northeast Regional Climate Center created a web-based tool titled Extreme Precipitation in New York and New England (http://precip.eas.cornell.edu/). These more recent data indicate that what was considered a 100-year storm event in the northeast in 1950 is now likely to occur twice as often.

The Role of Watershed Characteristics and Conditions

Watershed conditions such as soil moisture, topography and ground cover determine how a given watershed responds to changing precipitation inputs. It is important to differentiate between watersheds that are vulnerable to flash floods and those that are not. Watersheds that are conducive to flash flood events due to steep slopes and stream channels that rapidly concentrate flow are exhibiting increases in flooding as climate warms and heavy downpours become more prevalent. Flood vulnerability in watersheds that are less conducive to flash floods is dependent on precursor conditions such as soil moisture, snow pack and ground cover. Trends in flood magnitude differ by region with generally increasing trends in the Northeast and Midwest and decreasing trends in the southwest.3 Significant variation in flood trends occurs within regions based on differing watershed characteristics and differing local precipitation patterns.


Linking Infrastructure and Ecosystem Resiliency

Consideration of the geomorphic and ecological processes that a healthy stream supports is an important element of intelligent stream crossing design. A properly designed crossing maintains connectivity for upstream and downstream movement of fish and other aquatic organisms, maintains natural flow regimes, and supports the transport of organic and inorganic materials. Crossings that match the slope of the stream, span the full width of the stream, and have natural stream bed materials that continue through the structure are likely to address these concerns.4

Inclusion of these elements in stream crossing design will often result in a wider opening width than would be selected based purely on hydrologic concerns and will bias selection towards the use of open-bottom structures such as pipe arches or bridges as opposed to culverts. Both of these decisions often have the added benefit of resulting in a structures that will accommodate a larger design storm.

Managing Risk

As the atmosphere and oceans continue to warm the likelihood of extreme precipitation events will continue to increase. The risk for increased flooding will be highest in those watersheds that are conducive to flash flooding and in those regions with increasing total precipitation. Minimizing risk and costs associated with stream crossings in this changing environment should include consideration of the following:

  • Minimize the number of new stream crossings: Planning road and trail systems to minimize the number of new stream crossings is a sure-fire method of reducing cost and risk.
  • Use of temporary structures where feasible: Temporary stream crossings reduce exposure to flooding due to the limited time that they are in place and completely eliminate long-term maintenance headaches and long-term stream impacts. In addition, some states offer loaner bridges and cost share programs.
  • Link infrastructure and ecosystem resiliency in design and construction: Designing new structures and upgrading old structures to address both changing precipitation patterns and ecosystem function will minimize failure of structures and the need to rebuild prematurely.

A synopsis of a recent Manomet evaluation of the costs and benefits of stream crossing best management practices is available on page 44, Table 5 of the following publication: http://www.wri.org/sites/default/files/WRI13_Report_4c_NaturalInfrastructure_v2.pdf

(Part 2 of Stream Crossings and Climate Change will provide practical, on the ground advice for sizing stream crossings. To be posted in September 2014.)



1. Climate Change 2007: Working Group 1: The Physical Science Basis. (Intergovernmental Panel on Climate Change, 2007). at <http://www.ipcc.ch/publications_and_data/ar4/wg1/en/faq-3-2.html>

2. U.S. Global Change Research Program. Climate change impacts in the United States: U.S. national climate assessment. (2014). at <http://purl.fdlp.gov/GPO/gpo48682>

3. Peterson, T. Monitoring and understanding changes in heatwaves, cold waves, floods and droughts in the United States: State of knowledge. Bull. Am. Meteorology Soc. 94, 821–834 (2013).

4. New Hampshire Stream Crossing Guidelines. (University of New Hampshire, 2009). at <http://www.streamcontinuity.org/pdf_files/nh_stream_crossing_guidelines_unh_web_rev_2.pdf>