* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
Download Climate scenarios
Fred Singer wikipedia , lookup
Michael E. Mann wikipedia , lookup
Effects of global warming on human health wikipedia , lookup
Climate change adaptation wikipedia , lookup
Mitigation of global warming in Australia wikipedia , lookup
Climate change in Tuvalu wikipedia , lookup
Low-carbon economy wikipedia , lookup
German Climate Action Plan 2050 wikipedia , lookup
Economics of climate change mitigation wikipedia , lookup
Instrumental temperature record wikipedia , lookup
2009 United Nations Climate Change Conference wikipedia , lookup
Media coverage of global warming wikipedia , lookup
Numerical weather prediction wikipedia , lookup
Public opinion on global warming wikipedia , lookup
Climate change and agriculture wikipedia , lookup
Scientific opinion on climate change wikipedia , lookup
Global warming wikipedia , lookup
United Nations Framework Convention on Climate Change wikipedia , lookup
Politics of global warming wikipedia , lookup
Effects of global warming wikipedia , lookup
Atmospheric model wikipedia , lookup
Effects of global warming on humans wikipedia , lookup
Climate change, industry and society wikipedia , lookup
Climate change in Canada wikipedia , lookup
Climate engineering wikipedia , lookup
Surveys of scientists' views on climate change wikipedia , lookup
Climate governance wikipedia , lookup
Attribution of recent climate change wikipedia , lookup
Climate change in the United States wikipedia , lookup
Climate sensitivity wikipedia , lookup
Effects of global warming on Australia wikipedia , lookup
Climate change and poverty wikipedia , lookup
Global Energy and Water Cycle Experiment wikipedia , lookup
Solar radiation management wikipedia , lookup
Citizens' Climate Lobby wikipedia , lookup
Economics of global warming wikipedia , lookup
Carbon Pollution Reduction Scheme wikipedia , lookup
Business action on climate change wikipedia , lookup
Climate change feedback wikipedia , lookup
Uncertainties in the Development of Climate Scenarios PRECIS Workshop, MMD, KL, November 2012 © Crown copyright Met Office Uncertainties in Climate Scenarios • Goal of this session: • understanding the cascade of uncertainties • provide detail on the uncertainties in emissions scenarios • provide detail on the uncertainties in regional climate change predictions © Crown copyright Met Office Uncertainties • Emissions • Concentration • GCMs • Regional modelling • Climate scenario construction • Impacts Stages required to provide climate scenarios © Crown copyright Met Office Uncertainties: Emission Scenarios • Uncertainties in the key assumptions and relationship about future population, socio-economic development and technical changes. • The consequent uncertainties are unquantifiable as IPCC does not assign probabilities to any of choices of the key assumptions involved • We are currently working with 2 sets of scenarios: SRES (used for CMIP3/IPCC AR4) and RCPs (used for CMIP5/AR5) © Crown copyright Met Office SRES Emissions Scenarios 1. Socioeconomic scenarios 3. Atmospheric concentrations 2. Emissions scenarios © Crown copyright Met Office SRES: Sequential approach to developing climate scenarios Socioeconomic scenarios Emissions scenarios Atmospheric concentration s Climate scenarios Impacts • 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. © Crown copyright Met Office Representative Concentration Pathways (RCPs) © Crown copyright Met Office RCPs: Parallel approach to generating climate scenarios Atmospheric concentrations (‘Representative Concentration Pathway’, RCPs) Emissions scenarios Socio-economics Policy Intervention (mitigation or adaptation) Integrated assessment modellers and climate modellers work simultaneously and collaboratively Carbon cycle and atmospheric chemistry Impacts © Crown copyright Met Office Climate scenarios Uncertainties: Concentration Scenarios • Uncertainties in the understanding of the processes and physics in the carbon cycle and chemistry models • Models currently use a single set of concentrations derived from carbon cycle/chemistry models • Experiments to date indicate the uncertainties may be large • Coupling a carbon-cycle model into one AOGCM shows a large positive feedback • Coupling an atmospheric chemistry model into one AOGCM shows a small negative feedback © Crown copyright Met Office Carbon cycle model Coupled to standard HadCM3 atmosphere, ocean and interactive sulphur cycle. Prescribe CO2 emissions Photosynthesis Respiration (not atmospheric concentration) Moses 2.1/ Triffid newHadOCC: land surface scheme: Ocean biology/carbon cycle model Dynamic Vegetation © Crown copyright Met Office Impact of perturbations on the atmospheric CO2 17 member ensemble of HadCM3C Historical and A1B SRES future scenario CO2 concentration (ppm) © Crown copyright Met Office Impact of perturbations on global mean temperature. Relative impact of uncertainties in the terrestrial carbon cycle (green) and atmospheric feedbacks (blue) © Crown copyright Met Office Uncertainties: Climate models • Incorrect, incomplete or missing description of key processes and feedbacks in the climate system e.g. • Representation of clouds • Complexity of sea-ice model • Feedback from land-use change • Internal (natural) variability of the climate system • Decadal variability means that 30-year samples of a climate state may differ substantially © Crown copyright Met Office Climate model formulation © Crown copyright Met Office HADLEY CENTRE EARTH SYSTEM MODEL Atmosphere 1985 1992 1997 Atmosphere Atmosphere Atmosphere Atmosphere Atmosphere Land surface Land surface Land surface Land surface Land surface Ocean & sea-ice Ocean & sea-ice Ocean & sea-ice Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Sulphate aerosol Non-sulphate aerosol Carbon cycle Carbon cycle Ocean & sea-ice Atmospheric chemistry Ocean & sea-ice Off-line model model development Strengthening colours denote improvements in models © Crown copyright Met Office Sulphur cycle model Land carbon cycle model Ocean carbon cycle model Atmospheric chemistry Non-sulphate aerosols Carbon cycle model Atmospheri c chemistry Uncertainties in climate model Boundary layer Turbulent mixing coefficients: stabilitydependence, neutral mixing length Large Scale Cloud Ice fall speed Roughness length over sea: Charnock constant, free convective value Critical relative humidity for formation Dynamics Cloud droplet to rain: conversion rate and threshold Diffusion: order and e-folding time Cloud fraction calculation Gravity wave drag: surface and trapped lee wave constants Convection Entrainment rate Gravity wave drag start level Land surface processes Intensity of mass flux Root depths Shape of cloud (anvils) (*) Forest roughness lengths Cloud water seen by radiation (*) Surface-canopy coupling Radiation Ice particle size/shape CO2 dependence of stomatal conductance (*) Sea ice Cloud overlap assumptions Albedo dependence on temperature Water vapour continuum absorption (*) Ocean-ice heat transfer © Crown copyright Met Office Change (%) in South Asian monsoon rainfall: A1B, 2090s, CMIP3 ensemble © Crown copyright Met Office Temperature and precipitation changes Africa, A1B, 2090s, CMIP3 ensemble Figure 11.2 © Crown copyright Met Office Perturbed physics approach • The perturbed physics approach allows uncertainties in various components of the model to be systematically explored. • This is done by: • Identifying parameters in the model which are both uncertain and important for the model response • Using an ensemble of models to explore the implications of these parameter uncertainties © Crown copyright Met 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 wellsimulated © Crown copyright Met Office Source of uncertainties Source of Uncertainty Alternative emission scenarios Emissions to concentrations Represented in Climate Ways to address it Scenarios? Yes Scale GCM patterns by the ratio of the radiative forcing Beginning Use GCMs that include interactive chemistry Modelling the climate response Different responses by different GCMs for the same forcing. Signal (response)/noise (internal climate variability) Yes Not normally Use a range of GCMs 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 © Crown copyright Met Office Main Sources of Uncertainty Socio- Economic Uncertainty Natural annualdecadal variability (‘Internal variability’) © Crown copyright Met Office Uncertainty in the model representation of physical processes Q: Which are the most important sources of uncertainty? 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 A: That depends on the timescale that we are looking at… © Crown copyright Met Office To summarise • There are many uncertainties which need to be taken into account when assessing climate change (and its impact) over a region • Some account may currently be taken for most (BUT NOT ALL) uncertainties • Even those uncertainties that can be accounted for are currently not well described • There is a lot more work for us all to do! © Crown copyright Met Office Questions © Crown copyright Met Office