Long-term variability of the Land Surface Dynamic vegetationLecture 13CLIM 714

Paul Dirmeyer

Time scales of variabilityLocally, any land surface state variable varies on a range of time scales:

Milankovich CyclesToday the Earth experiences about a 6% difference in the amount of solar radiation received in January compared to July. When the Earth's orbit is more elliptical, the amount of energy received would be vary much more between seasons, in the range of 20-30%. This inclination oscillates in a range of 21.8o and 24.4o. Precession oscillates between the two positions in a period of about 22,000 years. (The 22 000 year cycle is in fact a combination of a 19 000, and a 23 000, year cycle). The three cycles combine to produce variations in the amount of heating and the length of the seasons. The effect is most pronounced when the Earth is farthest from the Sun during the northern winter. The northern hemisphere is critical to the formation of large glaciers because most of the land is concentrated there. The glaciers grow not because of overall temperature decreases, but because there is not enough heating during the summer to melt the accumulated ice.

Long term change - past

Todays vegetation

8000 ybp

11,000 ybp

13,000 ybp

18,000 ybp

Europe: Present Potential Vegetation

Europe: Past vegetation

Africa: Current potential vegetation

Past African Climate

Can we predict future vegetation changes?

Has Vegetation Already Begun to Respond to Climate Change?

Long term change - FutureFossil fuelsGreenhouse gasesGlobal warmingAnthropogenic climate change

IPCC Intergovernmental Panel on Climate ChangeIPCC projects a number of different scenarios depending on the degree of mitigation of greenhouse gas emissions over the next centuryClimate models follow these scenarios to predict impacts

IPCC ProjectionsThese scenarios lead to various predictions of global warming.Notice the range of uncertainly in observations for for past and current climate, and among models for future climate.

IPCC Warming ScenariosPredictions for two scenarios show similar characteristics for warming patterns, but differing magnitudes. This can be used as input to global DVMs (and ocean biology models) to estimate the response of the biosphere to climate change.

IPCC Precipitation ProjectionsPrecipitation projections have larger inter-model spread than temperature, but certain features emerge in both scenarios.The so-called permanent El Nio over the Pacific is offset by drier conditions over much of the subtropics and lower mid-latitudes. These changes also affect vegetation distributions.

Effects on individual speciesPotential range changes of selected tree species in Yellowstone regionof the Rocky Mountains under a projected climate based on a doublingof atmospheric carbon dioxide.

A Climate Change Atlas for 80 Tree Species of the Eastern United States

Anantha M. Prasad and Louis R. Iverson

Predictions forLongleaf Pine

http://www.fs.fed.us/ne/delaware/atlas/

Overall warming in Eurasia. Less warming and even some cooling in North AmericaContinental Differences in Warming Land surface April-October temperature trends in C/18 yrs between 1982 and 1999 (NASA GISS Station Temperature data)

Large-Scale Effects on Vegetation.

Vegetated pixels between 30N-70NObjectives

minimize the effect of Solar Zenith Anglereduce background effects (snow, barren and sparsely vegetated areas)use data from the same pixels in the entire analysis.

Changes in Vegetation ActivityChanges in vegetation activity can be characterized through

changes in growing season

changes in seasonal NDVI magnitude Increases in NDVI magnitudeIncreases in growing season IncreaseNDVI

Longer Growing Seasons(Increased by 18 Days)(Increased by 12 Days)11.9 days/18 yrs (p

Increases in April-October NDVI Magnitudes(8 Percent Increase)(12 Percent Increase)8.4/18 yrs (p

Spatial Pattern of April-October NDVI Changes

Persistence index: an index for identifying regions where NDVI has increased consistentlyA persistent increase in NDVI is observed in Eurasia over a broad contiguous swath of land while North America shows a fragmented pattern of change.

April-October NDVI trend at the 5% significance levelPixels that show a statistically significant trend are also pixels with high persistence.

Consistency between April-October NDVI and Temperature R=0.79 (p

Carbon Cycle - Current UncertaintiesFossil FuelsTo AtmosphereTo Land/Ocean 5.50.3Land use 1.6 0.8changeOcean 2.0 0.6 Uptake Missing Sink 1.8 1.5 Current source and sink strengths are uncertain.

Prediction of future climate forcing is therefore uncertain as well.Peta (1015 ) grams of carbon/yearAtmospheric 3.3 0.2Carbon=+--Atmosphericstoragebiosphere uptakehuman input

Monitoring CO2 Variations Global CO2 monitoring allows for bulk estimation of sources and sinks over large areas.

North American Carbon SinkBulk calculations show a net terrestrial carbon sink over North America, partially offsetting the anthropogenic sources.Modeling studies (below) suggest the sink is a combination of forest regrowth in the east, and favorable climate trends over the last 50 years (earlier springs, wetter summers)

Dynamic Vegetation Models (DVMs)

Elements to model.The spatial and temporal dynamics of the biosphere have strong implications for the overall system

What drives long-term variability?

Succession models

The problem with succession modelsThey are linear.

DVMs and PFTs

Ecosystems are dynamic

In DVMs, autogenic succession is internal to the modelClimate and humans are external

DVM example 1:Boreal Forest

DVM Example 2:Tropical Forest

Summary of the first phase of the PILPS C-1 projectComparison of both biophysical and biogeochemical flux from different types of models with observations at one EUROFLUX site: Loobos, NetherlandsThe site:Temperate mature (100 years) coniferous forestClimate: 700 mm precipitation , 9.8 C mean temperature Planted on a sand no soil carbon at the beginning of the plantation Measured fluxes: NEE, LE,H, Rn Meteorological parameters: incoming SW rad., precipitation, temperature, wind speed, relative humidity, pressure-Period covered: 1997-1998Models: Including SVAT with and without carbon cycleSimulations:Free equilibrium simulations:Models are run until equilibrium of state variables using years 1997-1998 in loopFree 100 years run:simulation of realistic scenario: Beginning with a soil with no carbon, the models are run for 1906 (plantation of the forest) to 1998 using observed climate.www.pilpsc1.cnrs-gif.fr/

DVM Comparison: PILPS C1Models were not calibrated in advance. This is evident in the different trajectories of forest growth and soil carbon storage during the 100-year period.

DVM simulation of global vegetationOne case: NASA-CASA

DVM simulation of CO2 variability

DVM simulation of climate change responseReversal in trend is due to the release of soil carbon from high latitudes

Dynamic vegetation modeling