© 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 in the Development of Climate Scenarios PRECIS Workshop, MMD, KL, November 2012

© 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 1: 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. Socio- economic scenarios 2. Emissions scenarios 3. Atmospheric concentrations

© Crown copyright Met Office Impacts Climate scenarios Atmospheric concentration s Emissions scenarios Socio- economic scenarios SRES: Sequential approach to developing climate 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.

© Crown copyright Met Office RCPs: Parallel approach to generating climate scenarios 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

© Crown copyright Met Office Representative Concentration Pathways (RCPs)

© Crown copyright Met Office Uncertainties 2: Concentration Scenarios Uncertainties in the understanding of the processes and physics in the carbon cycle and chemistry models 2 major sets of developments in recent years which affect how we address this uncertainty: - Use of RCP scenarios - Development of models with interactive carbon cycle and atmospheric chemistry (ESMs) Older models with no interactive carbon cycle/chemistry use a single set of concentrations derived from 'offline' carbon cycle/chemistry models Many models now include coupled carbon cycle and atmospheric chemistry models, Allows feedbacks to be represented

© Crown copyright Met Office Carbon cycle model - HadCM3C Coupled to standard HadCM3 atmosphere, ocean and interactive sulphur cycle. Moses 2.1/ Triffid land surface scheme: Dynamic Vegetation newHadOCC: Ocean biology/carbon cycle model Prescribe CO 2 emissions Photosynthesis Respiration

© 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 3: 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 Atmosphere Land surface Ocean & sea-ice Sulphate aerosol Sulphate aerosol Sulphate aerosol Non-sulphate aerosol Non-sulphate aerosol 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 HADLEY CENTRE EARTH SYSTEM MODEL

© Crown copyright Met Office Uncertainties in climate model 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

Rainfall change: IPCC CMIP3 Combination of pattern and some sign differences lead to lack of consensus

© Crown copyright Met Office Temperature and precipitation changes Africa, A1B, 2090s, CMIP3 ensemble

© Crown copyright Met Office Uncertainties 4: 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

© Crown copyright Met Office Source of uncertainties

© Crown copyright Met 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

IPCC AR4 SPM mean precipitation change summary figure Model consensus does not imply reliability - understanding mechanisms provides basis for a prediction Lack of consensus implies no information - but assessed at grid-scale thus maybe misleading Many tropical and sub-tropical regions appear uncertain

…”But, what about the white bits?” Cannot distinguish between ‘all models show small changes around zero’ and ‘large changes of different sign Consensus is measured at GCM grid-box scale, whilst signal might only be evident at larger spatial scale % change misleading in regions/seasons where rainfall is close to zero 0 x x x x x x x x x x x x + -

Key land areas where message has changed: West Africa (DJF),South Asia (DJF), Australia (DJF) Southern South America (JJA), Northern Australia /SE Asia (JJA) Remaining areas of disconsensus: South America (DJF), North America (JJA), Northern/Central Africa (JJA)

© 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 The better we understand the uncertainties at each stage of the process, the better we are equipped to apply climate projections/scenarios appropriately. Confidence in climate model projections come from process understanding, not just model consensus

© Crown copyright Met Office Questions