3. Climate Change 3.1 Observations 3.2 Theory of Climate Change (last two lectures) 3.3 Climate Change Prediction (this lecture) 3.4 The IPCC Process (next lecture) Acknowledgement: Steve Arnold (University of Leeds, UK)
3.1 Climate Change Predictions • Climate Modeling • Detection and Attribution of Climate Change • Climate Change Predictions
(i) Climate Modeling: The Need for Climate Models • Test of understanding • Evaluation of response • Prediction of climate change • Attribution of causes of climate change
Elements of a Climate Model • Atmospheric and oceanic circulation • Equations of motion for a fluid (air or water). These represent Newton's laws, mass conservation for the fluid and some thermodynamic relationships • They take the form of nonlinear partial differential equations. • Atmospheric radiation budget • Radiation absorbed, transmitted, reflected and scattered by each level of the atmosphere, in each wavelength band • Sensitive to the composition of the atmosphere, which varies in time and position • Hydrology, and water phase changes • Cloud processes on scales of 10's to 1000's of km • Sea ice and snow cover • Chemical reactions in the atmosphere and ocean • Affect composition, which feeds back on the radiation balance and the biosphere. • Energy flow in rocks and soils • Critical in determining the surface temperature and magnitude of the OLR • Sensitive to the nature of the soil and the soil moisture, which is strongly varying in time and space • Biosphere • Responses of plant growth and ocean plankton development to climatic changes and changes in CO2
A ‘Hierarchy’ of Climate Models • AGCM • Atmospheric General Circulation Model • Simulates atmosphere but prescribes the oceans and land surface • OGCM • Ocean General Circulation • Simulates the ocean circulation, but with a simple atmosphere sufficient to provide surface wind stress and heat supply • AOGCM • Coupled Atmosphere-Ocean General Circulation Model • Used extensively in climate change experiments
Example Model Processes Relative humidity change Clouds Cloud formation Horizontal motion of air Number of cloud drops Meteorology Cooling of air Onset of rainfall Ascent of air Rainfall rate Buoyancy change of air Radiation Start Change in solar radiation reaching surface Change in temperature of surface air Rate of absorption of solar radiation Surface/vegetation Change in surface moisture Response of vegetation Evaporation rate
Model Structure Typical model resolution is 2o x 2o x 20 altitude levels, equivalent to 250 x 250 km x 1 km • Discretisation • Splitting continuous quantities up into discrete units that can be acted on by the driving processes • Necessary because a model can carry information only at a fixed number of points • Averaging over large ranges • Examples • Spatial (lat,lon,altitude) • Aerosol and cloud particles (usually just mass) • Wavelengths (wavelength bands)
Example of Discretisation • Simulation of a wind-blown cloud of pollution Real Discretised concentration distance Effect of wind Pollution blown by the wind Discretisation causes reduction in ‘resolution’ (detail) Changes of the discretised quantity are not the same as those of the real quantity Pollution blown by the wind as represented on the grid
Parameterisation • Simplification of processes in terms of simpler equations with physically or empirically derived parameters (which can be ‘tunable’) • Example for clouds: • Rainfall assumed to occur when the liquid water content of the cloud reaches a prescribed value • Reality is a highly complex interaction of different sized droplets, ice crystals, hail, etc. • Parameterisations capture the essence of real processes but they can be inaccurate and unreliable when used to make predictions under new conditions • Almost all processes are parameterised in climate models
Cloud Parameterisation RH Scheme: Assumes clouds form wherever the Relative Humidity is above a certain value CW Scheme: Treats Cloud Water as a ‘prognostic’ model variable and distinguishes ice and water clouds, and the different precipitation from them CWRP Scheme: As the CW scheme, but accounts for the change in cloud reflectivity with water content All schemes have adjustable parameters that can be tuned to reproduce climatological cloud cover However, in a double-CO2 experiment, the RH scheme produced a 5 K warming, CW produced 3 K and CWRP produced 2 K. This result shows the problem with key climate model parameterisations!
Model Spin-up • Models are ‘initialised’ with observed climate variables • The model climate then changes over time as the model physics adjusts to ‘equilibrium’ (i.e., model climate consistent with driving physics) • Adjustment time is about 5-50 years for the atmosphere and surface ocean • Longer term (century scale) climate ‘drift’ due to slow adjustment of deep ocean • presents problems in the interpretation of climate change
Climate Models vs. Weather Forecast Models • Can be the same model • The UK ‘Unified Model’ is used for Met Office weather forecasts and climate prediction • Climate models are mostly ‘free running’ • Day-to-day weather patterns not used, but average ‘climatic’ state should be OK • Weather forecast models are ‘nudged’ to match observations as much as possible • ‘Data assimilation’
Evolution of climate models (IPCC 2007) Grid size resolution
Evolution of climate models (IPCC 2007) How has model performance improved? IPCC (2007)
Two Major Problems with Early Climate Simulations • Ocean heat ‘flux adjustments’ • A non-physical ‘adjustment’ to ocean heat content to account for incomplete ocean physics (failure to resolve narrow ocean currents, such as found in the N Atlantic) • Cloud responses • Clouds remain one of the largest uncertainties in climate response simulations • Cloud feedbacks still responsible for a large part of inter-model differences – IPCC 2007
Model Comparisons With Observations • Models do not simulate the current weather, but only a climatological state consistent with the prescribed forcings (greenhouse gas content of the atmosphere, aerosols, etc.) • Need to evaluate models against average climate over, say, 1 year • Can also look at ‘typical’ seasonal cycle or typical El Nino variations, but not for any particular year
Climatological Temperature • Absolute error generally < 2oC • Slight general cold bias Labelled contours: climatological SST and surface air temp Colours: mean model error from several models IPCC (2007)
Climatological Precipitation General pattern very good Obs Dry bias: problems modelling monsoon Errors in Indo-Pacific warm pool affects ability of model to capture teleconnections (El Nino) Model IPCC (2007)
Summary of Climatological Experiments from AR4 • Confidence in model simulations has improved since previous IPCC (2001). • Increased confidence from models no longer needing ‘flux adjustments’ • These models are able to maintain stable climates over centuries • Some biases and long-term trends remain • Tropical precipitation a problem • Clouds remain a key uncertainty in models
20th Century Climate Variability 58 models driven by changes in natural and anthropogenic forcings Obs Mean of models IPCC (2007)
Simulation of ENSO • Climate models have substantially improved spatial representation of pattern of SST anomalies in S Pacific - Better physics - Increased resolution • Some even used to forecast ENSO • SST gradients in equatorial Pacific still not well captured - Thermoclines too diffuse • Most models produce ENSO variability on timescales faster than observed • Helped by further increases in model resolution?
Extreme Weather • Climate models are not weather forecast models, so they can’t simulate individual events during a long simulation (of perhaps 100 years) • We need to test the models’ variability • Temperature: Simulation of hot and cold extremes has improved, with large regional discrepancies. • Rainfall: Frequency of intense events and amount of precipitation during them are underestimated. • Extra-tropical storms: These are storms affecting mid-latitude regions, such as northern Europe. These are well captured by models – improved since 2001. • Tropical cyclones: Frequency and distribution captured well by some models – improvement since IPCC 2001
Further Reading • Chapter 8 of the IPCC 2007 Assessment explains what confidence scientists have in various model predictions
(ii) Detection and Attribution of Climate Change • Anthropogenic climate change occurs against a backdrop of natural climate variability • Internal variability • Climate variability not forced by external agents • All time-scales (weeks to centuries) • Externally forced variability • Natural (volcanic, solar) • Anthropogenic (greenhouse gases, aerosols) • not forgetting...Changes in natural variability • Detection of anthropogenic climate change within all this other climate variability is a statistical “signal-to-noise” problem
Definitions • Detection • Demonstrating that an observed change is significantly different (in a statistical sense) from that which can be explained by natural internal climate variability • Detection does not imply an understanding of the causes • Attribution • The isolation of cause and effect
Problems with Attribution • Ideal • Controlled experimentation with the climate system in which the agents of change are systematically varied – then measure the response • Limited scope (e.g., iron fertilisation expts, see http://en.wikipedia.org/wiki/Iron_fertilization) • Reality • Statistical analysis of observational record • Demonstrate that observed changes are: • unlikely to be due entirely to internal variability • consistent with estimated/anticipated responses (models) • inconsistent with alternative explanations (models) • Limited data and imperfect model • Proof of cause and effect (100% agreement) impossible • Relies on rejecting alternatives • Incomplete knowledge means that “new alternatives” are still emerging
Requirements for Successful Detection and Attribution • Good data • Sufficient coverage to identify main features of natural variability • So far, surface and upper air temperatures have been used • Other climate variables used for ‘qualitative’ assessment (changes broadly consistent)
climate quantity time Example of Need for Quality Data climate quantity time
The Need for Long Data Records climate quantity time
Beware of Correlations! • Temperatures have increased since 1700 to present • The number of pirates has decreased since 1700 to present • Does this mean lack of pirates is causing climate change??? The existence of a correlation does not indicate a causal mechanism
Quantifying Internal Climate Variability • From the instrumental record • Relatively short (compared to 30-50 year period of interest) • Coverage incomplete, and varies with time • Paleoclimatic data • Reconstructions of climate before anthropogenic perturbations • Poor resolution and global coverage • Contains unknown external forcings • GCM ‘control’ runs over long periods (1000 years)
The Magnitude of Modelled Natural Variability 3 climate models run with no external forcings. All variability is due to internal climate processes. These simulations are compared with observations in the right-hand panels. No evidence for model ‘natural variability’ anything like recent changes
A reminder of what we are dealing with:Estimated Forcings since pre-industrial times (IPCC 2007)
Carbon Dioxide • From fossil fuel burning • ~60% contribution to total radiative forcing • Atmospheric concentration increased from 280 ppm in 1750 to 380 ppm in 2005 (36%) • 1999 – 2005 CO2 fossil fuel / cement emissions increased by ~3% / yr • Today’s CO2 concentration has not been exceeded during the past 420,000 years and likely not during the past 20 million years. • The rate of increase over the past century is unprecedented, at least during the past 20,000 years
Methane Trends IPCC (2007) Factor 2.5 levelling off of upward trend not understood • No detectable trend in global average OH between 1979-2004 • Suggests global methane emissions not increasing?
Trends in Halocarbons Radiative forcing peaked in 2003 – now beginning to decline
Using Forcing-Response Relationships for Detection and Attribution • Use the temporal and spatial variation of the different forcings • Can separate natural and anthropogenic influences only if spatial and temporal responses are known • Climate record: Different responses are superimposed – impossible to separate • Climate model: Study responses to individual forcings
Two methods for attribution studies • ‘Forward model’ calculations - Use best estimates of temporal and spatial changes in forcings together with best model estimates of responses. - Compare model with observed response - Combine scaled ‘response’ patterns from individual forcings to match observations – assumes spatial pattern of response correct but not magnitude • Inverse model calculations - Magnitude of uncertain parameters (e.g. external forcings) varied until model matches observations
Example: Aerosol forcing • ‘Forward model’ calculations - use estimates of emissions changes, aerosol chemistry models - directly resolve separate contributions from different aerosol components and processes • Inverse model calculations - vary aerosol forcing until model simulation matches observations - yields ‘net’ forcing, including all contributions to forcing that project onto ‘fingerprint’ of forcing being estimated. - useful for very uncertain forcings.
Optimal Detection Using Fixed Signal Patterns • Determine the model response to each forcing as a function of time (or space) • Determine the best combination of all responses to fit the temperature record Patterns of variation Patterns scaled to best explain observations Pattern of observed T Aerosol effect becoming increasingly negative + _ Steadily increasing greenhouse gases 1900 1950 2000 1900 1950 2000
Forcing-Response Patterns • Pattern of response can look very different to the pattern of forcing because of: • Feedbacks • Atmospheric/ocean circulation smooth out temperature gradients • Model response will depend on its representation of feedbacks
Example of model Forcing-Response Patterns Solar Volcanic Well mixed GHGs Ozone Direct sulfate Total Temp change 1890-1999 (oC / century)
Can natural forcings explain Global Warming? • A climate model including only natural forcings (solar + volcanic aerosol) does not explain the temporal change in global mean temperature IPCC (2007)
Can natural forcings explain Global Warming? Models with both natural and anthropogenic forcings do far better IPCC (2007)
Detection of natural and anthropogenic signals Contribution from GHGs, other anthropogenic and natural foircings to temperature changes between 1990s and 1900s.
Conclusions • It is extremely unlikely (<5%) that the global pattern of warming during last 50 years can be explained without external forcing. • Greenhouse gas forcing has very likely caused most of warming over last 50 years. • It islikelythat there has been a substantial anthropogenic contribution to surface temperature increases in every continent except Antarctica since the middle of the 20th century. • Recommend read summary and conclusions to Chapter 9 IPCC AR4.
(iii) Climate Change Predictions • Climate model experiments • Projections of future climate (IPCC AR4) • Abrupt changes – ‘tipping points’ • ‘Geo-engineering’ Reading: IPCC AR4 - Chapters 10 and 11