Long-term variability of the Land Surface – Dynamic vegetation

1. Long-term variability of the Land Surface – Dynamic vegetation Lecture 13 CLIM 714 Paul Dirmeyer

2. Ice Ages Climate Change (T) Time scales of variability Locally, any land surface state variable varies on a range of time scales:

3. Milankovich Cycles Today 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.

4. Long term change - past

5. Today’s vegetation

6. 8000 ybp

7. 11,000 ybp

8. 13,000 ybp

9. 18,000 ybp

10. Europe: Present Potential Vegetation

11. Europe: Past vegetation

12. Africa: Current potential vegetation

13. Past African Climate

14. Can we predict future vegetation changes?

15. Changes in Climate Changes in Vegetation Pronounced warming in northern high latitudes Earlier disappearance of snow in spring Increased precipitation in northern high latitudes Increased concentration of atmospheric CO2 Increased productivity through: - enhanced photosynthesis - enhanced nutrient availability Has Vegetation Already Begun to Respond to Climate Change?

16. Long term change - Future Fossil fuels Greenhouse gases Global warming Anthropogenic climate change

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

18. IPCC Projections These 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.

19. IPCC Warming Scenarios Predictions 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.

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

21. Effects on individual species Potential range changes of selected tree species in Yellowstone region of the Rocky Mountains under a projected climate based on a doubling of atmospheric carbon dioxide.

22. A Climate Change Atlas for 80 Tree Species of the Eastern United States Anantha M. Prasad and Louis R. Iverson Predictions for Longleaf Pine http://www.fs.fed.us/ne/delaware/atlas/

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

24. Large-Scale Effects on Vegetation. • Vegetated pixels between 30N-70N • Objectives • minimize the effect of Solar Zenith Angle • reduce background effects (snow, barren and sparsely vegetated areas) • use data from the same pixels in the entire analysis.

25. delayed fall earlier spring Jan Jul Aug Dec NDVI Jan Jul Aug Dec Changes in Vegetation Activity • Changes in vegetation activity can be characterized through • changes in growing season • changes in seasonal NDVI magnitude Increases in growing season Increases in NDVI magnitude Increase NDVI

26. Longer Growing Seasons (Increased by 12 Days) 11.9 days/18 yrs (p<0.05) (Increased by 18 Days) 17.5 days/18 yrs (p<0.05)

27. Increases in April-October NDVI Magnitudes (8 Percent Increase) 8.4/18 yrs (p<0.05) (12 Percent Increase) 12.4/18 yrs (p<0.05)

28. Spatial Pattern of April-October NDVI Changes Persistence index: an index for identifying regions where NDVI has increased consistently A persistent increase in NDVI is observed in Eurasia over a broad contiguous swath of land while North America shows a fragmented pattern of change.

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

30. Consistency between April-October NDVI and Temperature R=0.79 (p<0.01) R=0.72 (p<0.01) Year-to-year changes in growing season NDVI are tightly linked to year-to-year changes in temperature.

31. Carbon Cycle - Current Uncertainties 5.5±0.3 • Current source and sink strengths are uncertain. • Prediction of future climate forcing is therefore uncertain as well. To Atmosphere Atmospheric 3.3 ± 0.2 Carbon “Missing”Sink1.8 ± 1.5 Land use 1.6 ± 0.8 change Ocean2.0 ± 0.6Uptake Fossil Fuels = + - - To Land/Ocean Atmospheric storage human input biosphere uptake Peta (1015 ) grams of carbon/year

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

33. North American Carbon Sink Bulk 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)

35. Dynamic Vegetation Models (DVMs)

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

37. What drives long-term variability?

38. Succession models

39. The problem with succession models They are linear.

40. DVMs and PFTs

41. Ecosystems are dynamic

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

43. DVM example 1:Boreal Forest

44. DVM Example 2:Tropical Forest

45. Summary of the first phase of the PILPS C-1 project Comparison of both « biophysical » and « biogeochemical » flux from different types of models with observations at one EUROFLUX site: Loobos, Netherlands • The site: • Temperate – mature (100 years) – coniferous forest • Climate: 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: 1997-1998 Models: Including SVAT with and without carbon cycle www.pilpsc1.cnrs-gif.fr/ • Simulations: • Free equilibrium simulations: • Models are run until equilibrium of state variables using years 1997-1998 « in loop » • Free 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.

46. Total living biomass (Kg C/m-2) CLASS-MCM CLASS-UA ORCHIDEE-1 ORCHIDEE-2 SWAP VISA IBIS MC Total Soil carbon (Kg C/m-2) AVIM CLASS-MCM CLASS-UA ORCHIDEE-1 ORCHIDEE-2 SWAP VISA MC IBIS AVIM DVM Comparison: PILPS C1 Models were not calibrated in advance. This is evident in the different trajectories of forest growth and soil carbon storage during the 100-year period.

47. DVM simulation of global vegetation One case: NASA-CASA

48. DVM simulation of CO2 variability

49. DVM simulation of climate change response Reversal in trend is due to the release of soil carbon from high latitudes

50. Dynamic vegetation modeling