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Climate scenarios
The document discusses climate change, focusing on temperature, rainfall, and extreme events while highlighting the importance of effective climate risk management. It emphasizes the role of General Circulation Models (GCMs) and Representative Concentration Pathways (RCPs) in understanding climate scenarios and the associated uncertainties. Furthermore, it outlines the need for calibration approaches to improve climate projections for impact assessments.
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Climate scenarios
- 1. Climate Change, Climatescenarios (RCPs, GCM) and Uncertainties Mukhtar Ahmed mukhtar.ahmed@slu.se ahmadmukhtar@uaar.edu.pk 1
- 2. Outline………….. • Climate Change •Temperature • Rainfall • Solar radiation • Elevated CO2 • Extreme events • Effective Climate Risk Management requires ? • IPCC • Representative Concentration Pathways (RCPs) • RCP2.6 • RCP4.5 • RCP6 • RCP8.5 • GCM • Uncertainties in Climate Model • Calibration approaches • Summary 2
- 3. GlobalTemperature 3 LATEST ANNUAL AVERAGEANOMALY: 2018 0.8 °C
- 4. 2018 4th warmestyear in continued warming trend, according to NASA, NOAA 4
- 5. Rainfall NASA data suggestfuture may be rainier than expected 5
- 6. Global precipitation trends 6 Annualmean precipitation in mm Pixel-based precipitation trends from 1983 to 2015 from PERSIANN-CDR Source:https://journals.ametsoc.org/doi/10.1175/BAMS-D-17-0065.1
- 7. Trend in climatezones. Precipitation trends from 1983 to 2015 over climate zone–continent groups (60°N–60°S). 7 S statistics to identify the sign (negative for decreasing and positive for increasing) Source:https://journals.ametsoc.org/doi/10.1175/BAMS-D-17-0065.1
- 8. Solar Radiation One SolarCycle-11 years (2019-2030) 2008-2018 Solar Dimming
- 9. Little ice age(1645-1715):RiverThames Frozen
- 10. Little Ice-age (Europein 1682)
- 11. Mini ice age:Dalton Minimum
- 12. Elevated CO2 12 Direct measurement2005-present Source: NOAA Indirect measurements Source: NOAA Source: Atmospheric Infrared Sounder (AIRS) NASA
- 13. Sea Level Change 13 Datasource: Coastal tide gauge records. Credit: CSIRO
- 14. First global rainfalland snowfall map from new Earth mission released 14
- 15. Extreme events • HeatWaves •Drought • Heavy Downpours • Floods • Hurricanes 15
- 16. Effective Climate RiskManagement requires An understanding of management options in response to climate information, 16
- 17. Understanding Climate Variability Driversof Climate variability Sea Surface Temperature (SST) and Pressure • SOI (Southern Oscillation Index) (The SOI is calculated using the pressure differences between Tahiti and Darwin) • El Nino-Southern Oscillation (ENSO) • Nino SST Indices (Nino 1+2, 3, 3.4, 4; ONI and TNI) • IOD (Indian Ocean dipole) 17
- 18. Southern Oscillation Index(SOI) 18
- 19. El Niño SouthernOscillation (ENSO) • El Niño Southern Oscillation (ENSO), a natural cycle that originates in the Pacific Ocean, is one of the most important modes of variability impacting the global climate • ENSO is a complex interaction of oceanic and atmospheric processes and predicting its variability is challenging. 19
- 20. The Intergovernmental Panelon Climate Change (IPCC) • United Nations body for assessing the science related to climate change. • Provide policymakers with regular scientific assessments on climate change, its implications and potential future risks, as well as to put forward adaptation and mitigation options • Working Groups and Task Force: Working Group I (The Physical Science Basis), Working Group II (Impacts, Adaptation and Vulnerability), and Working Group III (Mitigation of Climate Change). 20
- 21. Climate scenarios 21 A climatescenario is a combination of an emission or radiation scenario, a global climate model, a regional climate model and the modelled time period.
- 22. Stages required toprovide climate scenarios 22 1. Emissions 2. Concentration 3. GCMs 4. Regional modelling 5. Climate scenario construction 6. Impacts
- 23. SRES Emissions Scenarios 23 1.Socio-economic scenarios 2. Emissions scenarios 3. Atmospheric concentrations
- 24. SRES: Sequential approachto developing climate scenarios 24 Impacts Climate scenarios Atmospheric concentrations Emissions scenarios Socio- economic scenarios • Climate modellers await results from socio-economic modellers • Emissions scenarios chosen early on are restrictive.. E.g. no exploration of deliberate mitigation strategies, difficult to explore uncertainties in carbon cycle feedbacks.
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- 26. Representative Concentration Pathways (RCPs) FourRCPs defined by their total radiative forcing (cumulative measure of human emissions of GHGs from all sources expressed inWatts per square meter) pathway and level by 2100. 26
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- 28. RCPs: Parallel approachto generating climate scenarios 28 Impacts Emissions scenarios Atmospheric concentrations (‘Representative Concentration Pathway’, RCPs) Climate scenarios Integrated assessment modellers and climate modellers work simultaneously and collaboratively Socio-economics Policy Intervention (mitigation or adaptation) Carbon cycle and atmospheric chemistry
- 29. General Circulation Model •General circulation models (GCMs) are valuable tools for developing a quantitative understanding of climate dynamics and climate change • Essential tools for climate studies. • GCM projections are translated for regional impact assessment using either statistical or dynamic downscaling. • Regional Climate Models 29
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- 31. © Crown copyrightMet Office Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Land surfaceLand surfaceLand surfaceLand surfaceLand surface Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Sulphate aerosol Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Non-sulphate aerosol Carbon cycle Carbon cycle Atmospheric chemistry Ocean & sea-ice model Sulphur cycle model Non-sulphate aerosols Carbon cycle model Land carbon cycle model Ocean carbon cycle model Atmospheric chemistry Atmospheric chemistry Off-line model development Strengthening colours denote improvements in models HADHADLEY CENTRE EARTH SYSTEM MODEL
- 32. List of theGCM in IPCC AR5 (Coupled Model Intercomparison Project 5, CMIP5) 32 Source: https://www.sciencedirect.com/topics/earth-and-planetary-sciences/general-circulation-model
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- 38. Uncertainties in climatemodel Large Scale Cloud Ice fall speed Critical relative humidity for formation Cloud droplet to rain: conversion rate and threshold Cloud fraction calculation Convection Entrainment rate Intensity of mass flux Shape of cloud (anvils) (*) Cloud water seen by radiation (*) Radiation Ice particle size/shape Cloud overlap assumptions Water vapour continuum absorption (*) Boundary layer Turbulent mixing coefficients: stability-dependence, neutral mixing length Roughness length over sea: Charnock constant, free convective value Dynamics Diffusion: order and e-folding time Gravity wave drag: surface and trapped lee wave constants Gravity wave drag start level Land surface processes Root depths Forest roughness lengths Surface-canopy coupling CO2 dependence of stomatal conductance (*) Sea ice Albedo dependence on temperature Ocean-ice heat transfer
- 39. © Crown copyrightMet Office Change (%) in South Asian monsoon rainfall: A1B, 2090s, CMIP3 ensemble Change (%) in South Asian monsoon rainfall: A1B, 2090s, CMIP3 ensemble Source: MMD, KL, PRECIS Workshop
- 40. 40 Temperature and precipitationchanges Africa,A1B, 2090s, CMIP3 ensemble Source: MMD, KL, PRECIS Workshop
- 41. © Crown copyrightMet Office Uncertainties: Climate change scenarios and impacts • Climate change scenarios for impacts studies can be derived by: • Combining climate model and observed data • Using climate model data directly • Choices are often required when considering: • How to provide information at fine scales • How to apply changes in the mean climate or climate variability • As with climate modelling, the physical processes involved in studying climate impacts are often not well understood or well-simulated
- 42. © Crown copyrightMet Office Source of uncertainties Source of Uncertainty Represented in Climate Scenarios? Ways to address it Alternative emission scenarios Yes Scale GCM patterns by the ratio of the radiative forcing Emissions to concentrations Beginning Use GCMs that include interactive chemistry Modelling the climate response • Different responses by different GCMs for the same forcing. Yes Use a range of GCMs • Signal (response)/noise (internal climate variability) Not normally Use ensemble simulations Providing regional climate scenarios • Baseline and future climates Yes Use observed or model baseline and different methods for changes • Adding high resolution detail Yes Use of a range of dynamical and statistical techniques
- 43. © Crown copyrightMet Office Main Sources of Uncertainty Socio- Economic Uncertainty Uncertainty in the model representation of physical processes Natural annual- decadal variability (‘Internal variability’)
- 44. © Crown copyrightMet Office Q:Which are the most important sources of uncertainty? A: That depends on the timescale that we are looking at… Natural variability most important on timescales 0- 20 years, small by 100 years Emissions scenario important on timescales 40 years + Model uncertainty important at all timescales
- 45. Calibration approaches • GlobalClimate Models (GCMs) have been the primary source of information for constructing climate scenarios, and they provide the basis for climate change impacts assessments of climate change at all scales, from local to global. • Impact studies rarely use GCM outputs directly because climate models exhibit systematic error (biases) due to the limited spatial resolution, simplified physics and thermodynamic processes, numerical schemes or incomplete knowledge of climate system processes . Errors in GCM simulations relative to historical observations are large (Ramirez-Villegas et al. 2013). • Important to bias-correct the raw climate model outputs in order to produce climate projections that are better fit for agricultural modeling. 45
- 46. Calibration approaches 46 OREF =observations in the historical reference period TREF = GCM output from the historical reference period TRAW = raw GCM output for the historical or future period TBC = bias-corrected GCM output.) Bias correction (or nudging)
- 47. Change Factor • Inthe Change Factor (CF) approach the raw GCM outputs current values are subtracted from the future simulated values, resulting in “climate anomalies” which are then added to the present day observational dataset (Tabor & Williams, 2010). 47
- 48. Quantile Mapping • Theabove-described methods work well for more non-stochastic variables (i.e. temperature). A more sophisticated approach for bias- correcting more stochastic variables (e.g. precipitation and solar radiation) is needed. • GCM outputs are known to have a "drizzle problem", that is, too many low-magnitude rain events as compared to observations • Quantile Mapping (QM) approach with the qmap library written for R statistical software (Gudmundsson, 2014; Gudmundsson et al., 2012). 48
- 49. Observational Datasets forCalibration 49
- 50. © Crown copyrightMet Office To summarise •Understanding of climate variability is utmost important for designing adaptation and mitigation strategies •GCMs are best option but ensemble approach should be used •There are many uncertainties which need to be taken into account when assessing climate change (and its impact) over a region
- 51. 51 This Photo byUnknown Author is licensed under CC BY-SA-NC