THEME – 1 Anticipated dryland expansion in scenarios of global warming
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The document summarizes projections of dryland expansion under scenarios of global warming based on analysis of climate model simulations. It finds that precipitation is expected to decrease in many subtropical regions due to warming-induced increases in potential evapotranspiration outpacing any increases in precipitation. This would result in expansion of arid and semi-arid lands in regions like North Africa, the Mediterranean, western US and southern Africa. The confidence in projections is higher for drying trends than wetting trends in some regions.
THEME – 1 Anticipated dryland expansion in scenarios of global warming
- 1. Anticipated dryland expansion in scenarios of global warming ICARDA Workshop Christopher T. Simmons H. Damon Matthews Concordia University, Montréal, QC, Canada
- 2. Outline • Introduction – Observed regional changes in precipitation and temperature in the last century – Representative Climate Pathways (future scenarios) – Climate models and uncertainties • Detailed Results from CMIP5 on Dryland Changes – Precipitation changes – Projected P/PET and Aridity Classifications – Shifts in the Köppen-Trewartha Classification of Climate Regimes • Conclusions
- 3. Observed Regional Precipitation Trends • Globally, little evidence of major precipitation changes or precipitation extremes (floods/droughts) • Certain regions show significant trends toward drier or wetter conditions (i.e., drying in W. Africa and parts of the Mediterranean Basin. + = significant (90% confidence) Adapted from IPCC Fifth Assessment Report, WG1, Physical Science Basis, Pg. 44 1901-2010 1951-2010 White space represents missing data
- 4. Observed Regional Temperature Trends • Net observed warming in datasets is already evident across many portions of the globe. • High latitude warming is accentuated by decreases in seasonal ice and snow coverage Adapted from IPCC Fifth Assessment Report, WG1, Physical Science Basis, Pg. 39 + = significant (90% confidence), white space is missing data
- 5. Representative Concentration Pathways (RCPs) • Scenarios used by the Intergovernmental Panel on Climate Change (IPCC) • Each pathway represents different increases in anthropogenic radiative forcing, accounting both for greater GHG concentrations (warming effect) and estimated aerosols (cooling effect). • Each pathway is named after the quantity of radiative forcing increase between 1750 and 2100 (i.e., RCP8.5 is associated with a +8.5 Watts/m2 increase in radiative forcing since 1750. Adapted from IPCC Fifth Assessment Report, WG1, Physical Science Basis, Pg. 94
- 6. Representative Concentration Pathways (RCPs) • With ~2.3 Watts/m2 already obtained, RCP2.6 optimistically assumes a near-term and consistent decrease in fossil fuel emissions. • RCP4.5, the ‘stabilization scenario,’ assumes a gradual increase in emissions to mid- century, followed by a decrease • RCP6.0 assumes a longer gradual increase in emissions, followed by a decrease in the last quarter of the century • RCP8.5 is the ‘business-as- usual’ scenario, with continued increasing emissions accompanying the projected rise in global population Adapted from IPCC Fifth Assessment Report, WG1, Physical Science Basis, Pg. 94
- 7. Climate Models • Simplified mathematical representations of the Earth’s Climate System – Usual contain a three dimensional representation of the atmosphere and/or the oceans, land-surface processes, land/sea ice, and vegetation • Validated on how well they reproduce the climate of the second half of the 20th century (when meteorological observations are more available and abundant globally) • Future climate projections are obtained by running climate models, starting at the present climate, with estimates of how future greenhouse gas (GHG) and aerosol concentrations will evolve (RCP scenarios)
- 8. Model Uncertainties Fig. adapted from Knutti et al. (2013), Geophys. Res. Letters, 40, 1194-1199 • Model Performance: Different models reproduce historical (observed) precipitation and temperatures with different accuracies • Parameterizations (mathematical simplifications of complex processes): Models have difficulty capturing and accurately reproducing sub-scale processes related to convective precipitation and tropical cyclones (IPCC: low confidence), both of which are important for subtropical hydrology
- 9. Model Uncertainties Fig. adapted from Hawkins and Sutton (2010), Clim. Dyn., doi: 10.1007/s00382-010-0810-6 • Internal Variability: very similar initial conditions or forcing leads to different results for a single climate model • Model Uncertainty: different models yield different results for the same emissions scenario. • Scenario Uncertainty: spread of model solutions created by using different RCP scenarios, related to our lack of knowledge on how future anthropogenic emissions will evolve.
- 10. Model Uncertainties Fig. adapted from Hawkins and Sutton (2010), Clim. Dyn., doi: 10.1007/s00382-010-0810-6 • For different parameters (temperature and precipitation), different uncertainties are important • Model uncertainty is most important in global decadal mean precipitation for short and long timescales, whereas internal variability is most important in the first 20 years of the simulation • Scenario uncertainty is dominant for long timescales in temperature projections
- 11. Regional Uncertainties (Precipitation) • For different regions, sources of uncertainty are different. • Uncertainty in Sahel summer precipitation is mostly tied to internal variability, with virtually no influence of scenario uncertainty Figs. adapted from Hawkins and Sutton (2010), Clim. Dyn., doi: 10.1007/s00382-010-0810-6
- 12. Detailed Results Obtained from CMIP5 on Land Surface Changes
- 13. CMIP5 Table adapted from Feng & Fu (2013), Atmos. Chem. Phys., 13, 10081-10094. • Climate Model Inter-Comparison Project, Phase 5 (CMIP5) • 27 Climate Models are run with the same RCP scenarios • The results of these simulations are averaged together to obtain the multi- model average (or ensemble mean) • Caveat: Not all climate models are independent of each other
- 14. Projected Precipitation Trends Fig. adapted from IPCC Fifth Assessment Report, WG1, Physical Science Basis, Pg. 91 Dots indicate 90% model agreement on sign of change and change is more than two standard deviations away from the models’ internal variability CMIP5 projected change from 1986-2005 average greater warming = more substantial precipitation decreases in the Subtropics Greater model confidence of precipitation decreases in drying regions for more extreme warming scenarios
- 15. Annual Climate Changes for RCP8.5 °C % precip. change Precipitation Changes (projected change for 2071-2100 from 1961-1990 average) Temperature Changes (projected change for 2071-2100 from 1961-1990 average) From Feng et al. (2014), Glob. Planet. Change, 112, 41-52
- 16. Seasonal Changes in Precipitation for RCP8.5 Precipitation Changes (projected change for 2071-2100 from 1961-1990 average) Adapted from Feng et al. (2014), Glob. Planet. Change, 112, 41-52 • Both winter (wet season) and summer drying in Mediterranean Basin • Poleward expansion of Hadley Cell and longer Monsoon season leads to greater precipitation in Tropical Africa and the Indian Subcontinent, esp. during the Boreal Summer. • Very small increases in precipitation result in changes in precipitation percentage in regions with relatively little total precipitation (ex. Sahara, Sahel, Arabian Peninsula) % precip. change
- 17. Precipitation Changes related to Monsoons • Monsoon winds are driven by broad-scale land-sea temperature contrast • Models show a weakening of monsoon winds, but overall wet season lengthens due to greater heating of land than water (IPCC, likely) • This leads to increased precipitation on the Indian Subcontinent and the Tibetan Plateau, particularly during the Northern Hemisphere Summer
- 18. • Poleward expansion of the descending branch of the Hadley Cell suppresses precipitation in regions experiencing greater subsidence, leading to greater drying poleward from traditional subtropical desert zones • The Hadley Cell is expected to broaden, leading to a wider tropical zones and wider subtropical drylands, but it might also weaken in intensity (IPCC Report) Precipitation Changes related to the Hadley Circulation Fig. adapted from Yongyun et al. (2013), Adv. Atmos. Sci., 30, 790-795.
- 19. P/PET Dryland Classification P/PET Dry Subhumid 0.5-0.65 Semiarid 0.2-0.05 Arid 0.05-0.2 Hyper-arid 0-0.05 Fig. adapted from Feng & Fu (2013), Atmos. Chem. Phys., 13, 10081-10094. • Ratio of Precipitation to Potential Evapotranspiration (PET) • PET = evaporation one would get (for a given air temperature and wind speed) over a completely wet surface • Natural values range from 0 (desert) to slightly greater than 1 (very moist regions)
- 20. Annual P/PET and Relative Humidity in RCP8.5 • Greater warming over land decreases relative humidity • This casues more evaporation from the land surface and increases PET for much of the globe • P/PET ratio decreases in many regions % RH change CMIP5 Projected change for 2071-2100 from 1961-1990 average Fig. adapted from Sherwood and Fu (2014), Science, 343, 737-738.
- 21. • Precipitation does not increase enough in most regions to counter the effect of decreased RH with increased T • Gradual drying of many land surfaces despite increased precipitation • Regions influenced by the stronger monsoon under greater GHG forcing experience moistening (equatorial Africa, Indian subcontinent) Annual P/PET vs. Precipitation for RCP8.5 Fig. adapted from Feng & Fu (2013), Atmos. Chem. Phys., 13, 10081-10094. CMIP5 Projected change for 2071-2100 from 1961-1990 average
- 22. Dryland Classification Changes for RCP8.5 Becoming WetterBecoming Drier Regions with Strong Multi-Model Agreement (80% consensus) on Drying Trend CMIP5 Projected change for 2071-2100 from 1961-1990 average • Expansion arid lands on the northern coast of Africa to Anatolia, southern Africa, Australia, southwestern North America. • Hyper-arid lands expand in northern Africa and Iraq. • Expansion of semiarid lands to the northern coast of the Mediterranean, western Africa, Black Sea region, North American Prairies, southern Africa, western Africa, and South America. • Model consensus is much better for regions experiencing a drying trend (see Fig. to left) than for regions becoming wetter (not shown).Figs. adapted from Feng & Fu (2013), Atmos. Chem. Phys., 13, 10081-10094.
- 23. Global Trends CMIP5 Projected change for 2071-2100 from 1961-1990 average • Models have historically underestimated dryland expansion. According to data, drylands have increased in size by 4% since the 1950s. • Greatest rate of expansion of drylands occur in next 50 years in both RCP scenarios. Little net change thereafter in RCP4.5 • Greater warming (RCP8.5) leads to more drylands (thermodynamic mechanism) Figs. adapted from Feng & Fu (2013), Atmos. Chem. Phys., 13, 10081-10094.
- 24. Model Uncertainties • Averaging between models eliminates some of the original climate signal • In this example, spread of precipitation for multi-model ensemble mean is nearly 30% less than for the individual simulations. • Drying of more than 15-30% per degree Celcius (or Kelvin,K) change in temperature, as seen in the individual simulations, is not captured in multi-model mean (multi-model mean calculated for each grid cell) Fig. adapted from Knutti et al. (2010), Journal of Clim., 23, 2739-2758
- 25. Köppen-Trewartha Climate Classification Figs. adapted from Feng et al. (2014), Glob. Planet. Change, 112, 41-52
- 26. End-of-Century Climate Shifts for RCP8.5 Figs. adapted from Feng et al. (2014), Glob. Planet. Change, 112, 41-52
- 27. Global Climate Regime Shifts • 46 % of Earth’s land surface undergoes a climate regime shift • Conversion to dryland classification in many parts of presently subtropical northern Africa and southern Spain, Australia, central Asia, southern Africa, and southwestern North America • Mid-century expansion of drylands by ~6-9%, end of century by ~8- 16% CMIP5 Projected change for 2071-2100 from 1961-1990 average for RCP.8.5 Table and Fig. adapted from Feng et al. (2014), Glob. Planet. Change, 112, 41-52
- 28. Global Climate Regime Shifts to More Arid Conditions Fig. adapted from Feng et al. (2014), Glob. Planet. Change, 112, 41-52 • Semiarid and arid climate regimes increase, largely at the expense of subtropical climate regime categories • Greater GHG forcing leads to greater warming and more global aridification • Models underestimated more recent climate regime shifts to more arid climates (1990s)
- 29. Main Conclusions • Climate models participating in CMIP5 produce a strong consensus for drying in subtropical regions for scenarios with greater global warming, due in part to a broadening of the Hadley Circulation • Specific regions which show the most consistent trends toward drying in future scenarios are the Mediterranean and Black Sea basins, southern Africa, southern Australia, and southwestern North America • Even where increases in precipitation in many mid-latitude regions, a decrease in relative humidity is also projected for many of these land surfaces due to greater heating • Model and scenario uncertainties have an important influence on the projected expansion of drylands and must be taken into account for a realistic perspective on future climate regime and vegetation changes Slides Available at www.esmg.mcgill.ca/ICARDA.pdf
- 31. Contributors to Warming Climate • Carbon dioxide, methane, halocarbons and nitrous oxide (greenhouse gases) trap the radiation absorbed and re-emitted by the Earth’s surface, contributing to warming. • Emissions of aerosols and cloud condensation nuclei lead to less solar radiation received at Earth’s surface, and replacement of forests with croplands leads to less terrestrial absorption of solar radiation, contributing to cooling effect. • Estimated total radiative forcing: ~2.29 W m2 contribution of warming from anthropogenic sources Adapted from IPCC Fifth Assessment Report, WG1, Physical Science Basis, Pg. 14
- 32. Model Uncertainties • For different parameters (temperature and precipitation), different uncertainties are important • Model uncertainty is most important in global decadal mean precipitation for short and long timescales, whereas internal variability is more important in the first 20 years of the simulation • Scenario uncertainty is dominant for long timescales in temperature projections Figs. adapted from Hawkins and Sutton (2010), Clim. Dyn., doi: 10.1007/s00382-010-0810-6
- 33. Model Uncertainties • Bias projection to the ensemble mean from nearly identical models – Difference in results between models developed by the same institution are often a factor of 3- 10 times smaller than difference with other models Fig. adapted from Knutti et al. (2013), Geophys. Res. Letters, 40, 1194-1199 Climate Model Family Tree, closeness based on similar behaviour (correlations) of temperature and precipitation between the individual models, often a symptom of code sharing between models
- 34. Global Climate Regime Shifts Table adapted from Feng et al. (2014), Glob. Planet. Change, 112, 41-52 • Perspective of broad-scale Köppen-Trewartha Climate Classification, used to diagnose vegetation changes • 5 classes, B represents drylands (arid and semi-arid)
- 35. Other Hydrological Parameters Dots indicate 90% model agreement on sign of change and change is more than two standard deviations away from the models’ internal variability Fig. adapted from IPCC Fifth Assessment Report, WG1, Physical Science Basis, Pg. 45
- 36. Thermodynamic Mechanism Increasing Global Aridity • Land warms an average of 50% more than ocean surfaces; increased land warming from GHG forcing decreases relative humidity. • Relatively robust mechanism and seen in most models of global warming scenarios e___ es Clausius-Clapeyron Relationship Relative Humidity= Water Vapour Pressure (content) of atmosphere Saturation Vapour Pressure at a given temperature