Global climate change

This graph, based on the comparison of atmospheric samples contained in ice cores and more recent direct measurements, provides evidence that atmospheric CO2 has increased since the Industrial Revolution. (Source: NOAA)

Much of the information from this presentation comes from:http://climate.jpl.nasa.gov/

                                          

                                          

http://climate.jpl.nasa.gov/keyIndicators/

The chart on the left shows historical sea level data derived from 23 tide-gauge measurements. The chart on the right shows the average sea level since 1993 derived from global satellite measurements, updated here monthly. Sea level rise is associated with the thermal expansion of sea water due to climate warming and widespread melting of land ice.

This illustration on the left shows areas where ice melt occurred for more than three days over Greenland during 2007. Areas in which melt occurred for longer time periods are shown in a darker red while those areas melted for fewer days are shown in lighter red.

                                  

             

                                          

The time series shows the combined global land and marine surface temperature record from1850 to 2007. The year 2007 was eighth warmest on record, exceeded by 1998, 2005, 2003, 2002, 2004, 2006 and 2001.

Between 1850 and 2007the eight warmest years on

record were 1998, 2001, 2002, 2003, 2004, 2005, 2006 and

2007

The main cause of current global warming is an increased

greenhouse effect caused by human activities

http://climate.jpl.nasa.gov/causes/

The greenhouse effect results from heat energy being trapped in Earth’s atmosphere. In the past, much of this heat energy escaped Earth’s atmosphere. Today, increased greenhouse gases in the atmosphere don’t allow this energy to escape.

    

http://climate.jpl.nasa.gov/causes/

Greenhouse gases

• Water vapor

• Carbon dioxide (CO2)

• Methane

• Nitrous oxide

• Chlorofluorocabrons

Intergovernmental Panel on Climate Change, Fourth

Assessment Report

Greater than 90% chance that CO2, methane, and nitrous oxide increases in atmosphere as a result of human activities are responsible for warming patterns in the last 50 years.

Why have these gases increased in Earth’s atmosphere?

Burning of coal and oil to fuel human civilization results in carbon and oxygen combining in the atmosphere to creat CO2

Clearing of forests also results in increased CO2 because forests typically take up CO2 so photosynthesis can take place

Could climate change be a result of changes in the amount of the sun’s energy that reaches Earth?

Probably not

Evidence that changes in sun energy haven’t been responsible

for climate changeSince 1750, solar energy reaching Earth has

been constant or increased slightly (based on sunspot and tree ring data)

Warming has only occurred in Earth’s lower atmosphere, where greenhouse gases accumulate

Climate models suggest solar energy changes can’t account for warming patterns of last century

Climate change drivers are categorized into

Forcings—the ultimate drivers of climate change

Feedback—processes that further influence climate change after being set in motion by forcings

Forcings

Solar energy changes over time influence climate

Particulate matter like aerosols, dust, or soot in atmosphere can result in cooling or warming

Feedbacks

Ice-albedo feedback—ice reflects solar radiation. If ice melts because temperatures are rising, there is less ice to reflect solar radiation and more water to absorb it. More and warmer ocean waters on Earth will cause Earth to warm more.

Other important feedback phenomena

Clouds

Carbon cycle

Ocean circulation

Precipitation

Global climate change is predicted to have important effects on

species’ distributions

Methods for studying climate change effects on distributions

Comparisons over time

Comparisons over space to determine if same responses are occurring in different parts of the world

Modelling

Lenoir et al. 2008, plants in western Europe

Question— Do species shift their optimal elevational range upward when the climate is warming?

Question 2—Do species with particular life history traits have greater responses to climate change?

Studied forest plants in six mountain ranges in Europe, from sea level to 2600 m

Used data from surveys conducted from 1905 through 1985 (early surveys) and compared these data to data from surveys conducted between 1986 and 2005 (late surveys)

1986 was chosen as the “break” year because at that year the average annual temperature shifted upward

                                               

Fig. 1. Climatic trends from 1965 to 2006. ( A) Yearly mean surface temperature anomalies (using overall mean temperature as baseline) and ( B) annual precipitation anomalies (using overall mean annual precipitation as baseline) averaged for 73 elevation sites in the French mountains ranging in altitude from 10 to 2010 m above sea level. Solid gray bars refer to positive anomalies, whereas open bars refer to negative ones. The solid curve is the smoothed average with use of a 10-year filter. The vertical dotted lines mark the split between the two studied periods. Data have been gathered from the French National Climatic Network (Météo-France). [View Larger Version of this Image (114K JPEG file)]

Fig. 1. Climatic trends from 1965 to 2006. (A) Yearly mean surface temperature anomalies (using overall mean temperature as baseline) and (B) annual precipitation anomalies (using overall mean annual precipitation as baseline) averaged for 73 elevation sites in the French mountains ranging in altitude from 10 to 2010 m above sea level. Solid gray bars refer to positive anomalies, whereas open bars refer to negative ones. The solid curve is the smoothed average with use of a 10-year filter. The vertical dotted lines mark the split between the two studied periods. Data have been gathered from the French National Climatic Network (Météo-France).

• Based on how many times each plant species was detected at particular elevations during the early surveys and the late surveys, scientists calculated the “optimal elevation” for the earlier and later time periods

There was a significant upward shift in optimal elevation for 2/3 of the plant species. The mean shift was 64.8 meters between the two time periods.

                                                                  

Fig. 2. Scatter diagram of forest plant species ( n = 171) optimum elevation (i.e., altitude value at maximum probability of presence) for the periods 1905–1985 and 1986–2005. Each point represents one species: Species showing nonoverlapping 95% CIs around the optimum elevation between periods are displayed as solid triangles (                         ) (n = 46), whereas species with overlapping 95% CIs are displayed as solid circles (                               ) (n = 125) (see tables S1 and S2 for details) ( 23). (Inset) The distribution of the species differences in optimum elevation between periods. The vertical dotted line marks zero shift, and the vertical solid line marks the median shift. The arrow describes the direction of the shift.

Fig. 2. Scatter diagram of forest plant species (n = 171) optimum elevation (i.e., altitude value at maximum probability of presence) for the periods 1905–1985 and 1986–2005. Each point represents one species: Species showing nonoverlapping 95% CIs around the optimum elevation between periods are displayed as solid triangles ( ) (n = 46), whereas species with overlapping 95% CIs are displayed as solid circles ( ) (n = 125) (see tables S1 and S2 for details) (23). (Inset) The distribution of the species differences in optimum elevation between periods. The vertical dotted line marks zero shift, and the vertical solid line marks the median shift. The arrow describes the direction of the shift.

Authors also found that species of higher elevation (mountainous areas) and species with shorter life cycles (i.e. faster rates of reproduction) showed the greatest upward shifts

                                                                 

Fig. 4. Magnitude of optimum elevation shifts for plant species in relation to their ecological and life history traits ( 23). Shifts in mean optimum elevation according to geographic distribution pattern (solid line and symbols) correspond to ubiquitous ( n = 104), and mountainous species ( n = 67). Shifts in mean optimum elevation according to life form (dotted line and open symbols) correspond to woody ( n = 56) and grassy species (n = 115). Means are shown with standard errors. Significance of the magnitude of mean shift from the null hypothesis of zero shift is displayed for each trait (n.s. indicates nonsignificant, * P < 0.05, **P < 0.01, ***P < 0.001; Student's paired sample t test).

Fig. 4. Magnitude of optimum elevation shifts for plant species in relation to their ecological and life history traits (23). Shifts in mean optimum elevation according to geographic distribution pattern (solid line and symbols) correspond to ubiquitous (n = 104), and mountainous species (n = 67). Shifts in mean optimum elevation according to life form (dotted line and open symbols) correspond to woody (n = 56) and grassy species (n = 115). Means are shown with standard errors. Significance of the magnitude of mean shift from the null hypothesis of zero shift is displayed for each trait (n.s. indicates nonsignificant,

*P < 0.05, **P < 0.01, ***P < 0.001; Student's paired sample t test).

Results demonstrate upward shifts in optimal elevational range and that the magnitude of these shifts varies with species’ life history traits.

Still unknown are how temperature shifts might interact with other changes like land-cover changes and the presence of invasive species to influence species’ distributions.

Hitch & Leberg 2006, North American birds

Are distributions of North American birds moving north?

Are distributional shifts similar to those seen in a study from Great Britain (similar results suggest a global cause may be responsible)

Methods

Used Breeding Bird Survey (BBS) data

Breeding Bird Survey routes

Volunteers drive routes each year during the peak of the breeding season in North America when bird singing should be at a peak

Volunteers record all birds seen and heard during 3-minute stops each half-mile for 24.5 miles

The data are available online

BBS has been going on since 1966

(Now it has expanded into other countries)

Investigators used data from east of the Rocky Mountains to exclude potentially confounding influences of geography

Most bird species were used in analyses, except for game birds (because of human influences on distributions) and aquatic birds (because they may not be sampled well by BBS methods)

• Birds with distributions not more north than 44° North were considered to have a southern distribution

• Birds with distributions not more south than 34° North were considered to have a northern distribution

                                                            

Figure 1. Sampling area (dark, solid line) for breeding bird distributions in North America.

Figure 1. Sampling area (dark, solid line) for breeding bird distributions in North America.

Authors calculated whether birds with southern distribution had ranges that were shifting north and whether birds with northern distributions had ranges that were shifting south.

It was necessary to do both types of calculations to exclude the possibility that all birds were experiencing range expansions for reasons not necessarily associated with climate change, like land-cover change for example

Authors use data from between 1967 and 1971 to compare to data between 1998 and 2002

Authors determined whether mean northern latitude (mean latitude determined from the 10 northernmost routes where the species was detected) had changed between the two time periods

Analyses were conducted for all species simultaneously and for individual species

Results

Birds with southern distributions showed a significant northward shift, averaging 2.35 km per year

Birds with northern distributions did not show a significant southward shift

Species varied in their individual responses but more species with southern distributions shifted north than shifted south

Results

Results are similar to those of a study of birds from Great Britain, suggesting that a global force, likely climate change, is driving the shifts

Modelling approaches

What are the advantages of modelling, as compared to field studies, to predict species’ responses to climate change?

Pearson and Dawson 2003 evaluate the bioclimate envelope modelling approach

Use information that is known about how a species’ distribution relates to climate variables to describe the species’ “climate envelope”

Then examine how future scenarios of climate change would affect species’ distributions, given their climate envelopes

Fig. 1 Simulated redistribution of suitable climate space for stiff sedge (Carex bigelowii) under future climate scenarios in Great Britain and Ireland as predicted by the SPECIES model (Pearson et al., 2002). Climate change scenarios are those of Hulme & Jenkins (1998). Suitable climate space is expected to be lost, with a general migration northwards as the climate changes.

Limitations of bioclimate envelope modelling

Potential effects of biotic interactions on distributions, as well as other environmental characteristics like land-cover type, are not considered

Remember Connell’s work on impact of species interactions on distributions of

barnaclesHowever, at large scale, climate tends to be

a dominant influence on distributions at large scales, so bioclimate envelope modelling that looks at distributions at large scales may reduce errors that stem from ignoring species interactions

Fig. 2 Observed European distribution of hard-fern (Blechnum spicant) alongside the distribution as simulated by the SPECIES model (Pearson et al., 2002). Presented as an example of the good agreement achievable between observed and simulated European-scale distributions using a bioclimatic model.

Fig. 3 Observed European distribution of yew (Taxus baccata) alongside the distribution as simulated by the SPECIES model (Pearson et al., 2002). Although the broad distribution trends are identified in the simulated distribution, the finer details of the distribution are not captured.

Limitations of bioclimate envelope modelling

Evolutionary change is ignored. Several studies have show that populations can often change rapidly

Example, Thomas et al. 2001—Bush crickets in new populations (founded within the last 20 years) in new areas in Britain had more longer-winged individuals than older populations, indicating that evolution can occur rapidly

Thus bioclimate models would be best for species that are unlikely to undergo rapid evolutionary, for example long-lived species and species that don’t disperse well

Limitations of bioclimate envelope modelling

Predictions of future ranges based on bioclimate envelope models assume species will be able to migrate into the new areas that, in the future, have their preferred climates. If a species is a poor disperser, or if it inhabits a region with many barriers to dispersal, this may not be a good assumption.

Thus bioclimate models are probably most appropriate for species that are good dispersers although there is evidence that even poor dispersers are able to expand their ranges through rare, long-distance dispersal events

Conclusions

Bioclimate envelope models can be a useful first step in predicting species’ potential range changes in the face of climate change

It is necessary to bear in mind their limitations and to use the models with species that are most likely to meet the assumptions of the models. They will be more useful at larger scales.

Fig. 5 Schematic example of how different factors may affect the distribution of species across varying spatial scales. Characteristic 'scale domains' are proposed within which certain variables can be identified as having a dominant control over species distributions. Approximate spatial extents have been assigned to categories of scale based in part on Willis & Whittaker (2002). It is assumed that large spatial extents are associated with coarse data resolutions, and small extents with fine data resolutions.

Solutions

“It is not NASA's role to develop solutions or public policies related to global climate change. Instead, the agency's mission is to provide the scientific data needed to understand climate change and to evaluate the impact of efforts to control it.”

http://climate.jpl.nasa.gov/solutions/

Individual action

A guide to reducing personal greenhouse gas emissions from U.S. Environmental Protection Agency http://www.epa.gov/climatechange/wycd/index.html

Personal Emissions CalculatorA tool from the U.S. E.P.A. to estimate your greenhouse gas emissions and explore how to reduce them.http://www.epa.gov/climatechange/wycd/calculator/ind_calculator.html

We need corporate action as well as individual action. Get involved

in the political process.

Vote, write to newspapers and politicians, blog, support appropriate organizations