Key Factor Climate Sensitivity

Transcription

1 Reducing Uncertainty in Climate Sensitivity SOLAS Summer School 14 th of August 2009 Thomas Schneider von Deimling, Potsdam Institute for Climate Impact Research Key Factor Climate Sensitivity CO 2 Emissions Atmospheric CO 2 Concentration CLIMATE SENSITIVITY Global Mean Temperature Larger & more Frequent Impacts of Global Warming 1

2 Why does it matter if uncertainty in Climate Sensitivity is large? Lenton, Held et al. (2008) Why does it matter if uncertainty in Climate sensitivity is large? Five Reasons for Concern (Societal, Economic, Natural Damage) Stephen Schneider, Nature 458, (April 2009) 2

3 Defining Policy Targets Probability of Staying below 2 C Warming malte.meinshausen@pik-potsdam.de, see Meinshausen (2006)< History of Climate Sensitivity estimates Arrenhius (1896) 4 to 6 C... Charney (1979) 1.5 to 4.5 C IPCC CS Range ( C) IPCC-4: CS likely to be in the range 2 to 4.5 C wit h a best estimate of about 3 C In successive IPCC reports, the range of CS from models available at the time has changed little, despite the fact that their present-day climate simulations have greatly improved! 3

4 Approaches of Reducing the Uncertainty in CS 1) Bottom-up Approach (based on a quantitative understanding of the physical mechanisms) -> Simulation of the climate system response by complex climate models 2) Top-down Approach Analyse how climate has changed in the past when GHG concentrations (or other boundary conditions) have changed Increase of computer power allows for: 3) Perturbed-Physics Approach Ensemble of model versions are run to systematically account for parameter uncertainties. Models then are weighted due to their performance of reproducing past climate changes (or present-day climatology). Approaches of Reducing the Uncertainty in CS 1) Bottom-up Approach (based on a quantitative understanding of the physical mechanisms) Simulation of the direct changes in radiation balance and associated positive and negative feedbacks (such as water vapour, cloud, albedo and lapse rate feedbacks) with complex climate models. Problem: Although the underlying processes can to some extent be validated by observations, there is still considerable uncertainty in the strength of these feedbacks, most notably the cloud feedback. 4

5 CS estimates of GCM s (IPCC AR4) Most models cluster around 3K Source: IPCC Chapter Implication for emission reductions to meet the 2 C target... Climate Feedback differences between GFDL and NCAR models Simulated climate sensitivity Use observational data to narrow down uncertainty? 5

6 Climate Feedback differences between GFDL and NCAR models Simulated climate sensitivity Low level cloud changes at 2xCO 2 GFDL NCAR Small Scales: Need of Parametrization While many basic aspects of physics can be included (conservation of mass, energy etc.),, many need to be approximated for reasons of efficiency or resolutions Concept of Parameterization: Represent the effects of the unresolved processes on the grid. Assume that unresolved processes are at least partly driven by the resolved climate. Figs from 6

7 Constraining Cloud Behaviour? Satellite Measurements! but: - only short time-series of observations! - Risk of bias in the measurements - Especially difficult to resolve high (thin) clouds What changes in clouds are of interest? - Annual cycle - ENSO cycle Using the seasonal cycle to constrain Albedo Feedback Hall and Xu,

8 Pinatubo Simulation Low sensitivity model Yokohata, et al, 2005 High sensitivity model Approaches of Reducing the Uncertainty in CS 2) Top-down Approach Analyse how climate has changed in the past when GHG concentrations (or other boundary conditions) have changed Key targets: Problems: a) Last Century Warming b) Last millenium c) Ice Age d) Deep time climate history (e.g. PETM) a) The signal is weak as aerosols are strongly masking the observed warming. No equilibrium is reached uncertainties in ocean heat uptake come into play. c) Large climate change signals for constraining model sensitivity range back far in time (e.g. Last Glacial Maximum, 21 kyrs B.P.), but no direct temperature observations are available and there is no direct past analogue for a future large increase in GHG concentrations, with all other factors influencing climate being the same as today: 8

9 2) Top-down Approach Crude test of sensitivity: LAST CENTURY WARMING T = λ * RF T 2 CO * RF 2 = λ 2 CO 2 => Given an estimate of RF (Radiative Forcing) and!t, the feedback factor λ can be calculated 2) Top-down Approach Crude test of sensitivity: LAST CENTURY WARMING 9

10 Masking of 20th Century warming by Aerosols (Aerosols can strongly cool the Planet!) T = λ *( RF OHU ) T λ = RF OHU [Haywood and Boucher, Rev Geophys., 2000] 2) Top-down Approach Crude test of sensitivity: LAST CENTURY WARMING 10

11 Approaches of Reducing the Uncertainty in CS 2) Top-down Approach Crude test of sensitivity: Last Century Warming Radiative Forcing (RF) ~1.6 (0.6 to 2.4) W/m 2. T = λ * RF Strong RF AE Weak RF AE T λ = RF 20 th Century Warming: ~ 0.7 C; RF(20 th C) ~1.6 W/m 2 => CS ~ 1.6 C But the system has not had enough time to equilibrate to the perturbation of 20 th Century Radiative Forcing. => Oceanic Heat Uptake (OHU) has to be accounted for as well ~ 0.6 W/m 2 T = λ *( RF OHU ) T λ = RF OHU => CS ~ 2.6 C (1.4.. ) Further shortcoming: the warming depends on the history of past forcings... Masking of 20th Century warming by Aerosols Better estimates of the magnitude of Radiative Forcing by Aerosols will crucially reduce uncertainty in Climate Sensitivity! Andreae et al., Nature,

12 Watching out for larger Climate Signals The Last Glacial Maximum (LGM) Vostok Ice Core Pronounced differences in boundary conditions between present climate and the Last Glacial Maximum (LGM, 21 kyrs B.P.) in terms of : LGM GHG Concentrations Dust Content Vegetation Patterns Extent of Ice Sheets Petit et al., 1999 Watching out for larger Climate Signals The Last Glacial Well-calibrated paleo data are available for many regions (e.g.for tropical oceans => foraminfera, alkenone, corals, etc., but estimate of global cooling is difficult) But do we know how cold it had been globally during the LGM? (combine modeling simulations of LGM climate with data for filling the gaps of regions with insufficient proxy coverage!) => large signal to noise ratio of the LGM climate change signal T λ = RF 12

13 Watching out for larger Climate Signals The Last Glacial Well-calibrated paleo data are available for many regions (e.g.for tropical oceans => foraminfera, alkenone, corals, etc., but estimate of global cooling is difficult) But do we know how cold it had been globally during the LGM? (combine modeling simulations of LGM climate with data for filling the gaps of regions with insufficient proxy coverage!) => large signal to noise ratio of the LGM climate change signal Quasi-Equilibrium conditions (the perturbation lasted long enough for the system to respond to the new boundary conditions) => uncertainty in OHU plays no role Uncertainty in dust aerosol forcing affects the lower, not the upper bound of Climate Sensitivity estimates (because dust has reinforced the cooling) Watching out for larger Climate Signals Crude test of sensitivity: LGM: Glacial Boundary Conditions must be specified: 1) Glacial ice sheets (e.g. Pelltier reconstructions) ~ -3 W/m 2 +x - extent: -> moraine signatures - volume: -> sea level (LGM sea-level was lower by about 120m!) Uncertainty esp. remains due to the height of glacial ice-sheets. 13

14 Ice sheets at LGM (21 Ky B P) Watching out for larger Climate Signals Crude test of sensitivity: LGM: Glacial Boundary Conditions must be specified: 1) Glacial ice sheets (e.g. Pelltier reconstructions) ~ -3 W/m 2 +x - extent: -> moraine signatures - volume: -> sea level (LGM sea-level was lower by about 120m!) 2) GHG concentration (drop in CO 2, CH 4, N 2 O) ~ -2.7 W/m 2 -> ice cores 14

15 Watching out for larger Climate Signals Crude test of sensitivity: LGM: 1) Glacial ice sheets (e.g. Pelltier reconstructions) ~ -3 W/m 2 +x - extent: -> moraine signatures - volume: -> sea level (LGM sea-level was lower by about 120m!) 2) GHG concentration (drop in CO 2, CH 4, N 2 O) ~ -2.7 W/m 2 -> ice cores 3) Vegetation patterns ~ -0.5 W/m 2 +x -> pollen data 4) Dust Content ~ -1 W/m 2 +x (increased source areas and drier conditions lead to a sharp increase in atmospheric dust content at the LGM) -> ice cores Approaches of Reducing the Uncertainty in CS 2) Top-down Approach Test of sensitivity: Last Glacial Maximum Global LGM Cooling: ~ 4-7 C RF LGM ~ - (6 to 8) W/m 2 => CS ~ 2 to 4 C T λ = RF OHU T = λ * RF 2CO2 2CO2 Can be neglected But Climate Sensitivity can depend on the background climate! => Is the inference of strength of feedbacks from a colder climate a good measure for evaluating future climate change? 15

16 Deep Climate History ~ 5 C increase (over 20,000 years) Climate became more sensitive to orbital forcing Mid-Pliocene (~3Mio Years B.P.) Hansen et al Approaches of Reducing the Uncertainty in CS 3) Perturbed-Physics Approach I) Uncertain model parameters are identified and PDFs for those parameters are estimated (expert guesses, introduction of subjective information...) II) An Ensemble of model versions is run for different parameter settings, e.g. for simulating 20 th Century warming (systematic scanning of parameter uncertainty space) III) Models results then are weighted due to their performance of reproducing past climate change (or present-day climatology). Problem: - large computational demand! - What about modle structure uncertainty...? 16

17 Approaches of Reducing the Uncertainty in CS III) Models then are weighted due to their performance of reproducing past climate change (or present-day climatology). That s where observationalists and modeller should come together! (Research on Earth history <=> Research of future climate change) Recent Estimates of Climate Sensitivity Perturbed Physics Approach Knutti+Hegerl,

18 Recent Estimates of Climate Sensitivity Perturbed Physics Approach Knutti+Hegerl, 2008 Constraining uncertainty in CS by LGM paleo-data e sensitivity [ C] Climate Tropical Atlantic (20S-20N) paleo data LGM SST cooling [ C] Schneider von Deimling et al.,

19 Constraining uncertainty in CS by LGM paleo-data Considering other regions of the globe a Tropical ocean (SST) 3 b Tropical land LGM cooling [ C] LGM cooling [ C] c Greenland d Eastern Antarctica Climate sensitivity T 2x [ C] Climate sensitivity T 2x [ C] Schneider von Deimling et al., 2006 Is the Past a good Analogue for the Future? Does a measure of sensitivity derived from paleo-climate apply to the future? λ ( t) = ( Q( t) RF) dt ( t) Increasing feedback strength Q: global radiative imbalance TOA RF: radiative forcing dt: global mean surface temperature change PMIP2 analysis results from Crucifix 2006, Does the Last Glacial Maximum constrain climate sensitivity?, GRL Vol. 33 Increasing feedback strength 19

20 How strongly do Feedbacks Strengths depend on the climate state? atitude (degrees) La Use Models for planing where you will make a measurement! SAT [ C] 60 a SAT / SAT CO2 a surface air temperature (SAT) change (LGM - pre-industrial); Latitude (degrees) Latitude (degrees) b c Longitude (degrees) STD( SAT) [ C] b ratio of CO 2 attributable SAT cooling to total LGM SAT cooling. c standard deviation of SAT for all model versions Schneider von Deimling et al.,

21 Take Home Message Data and Models should not be considered in isolation! ( Don t be afraid of the modellers...) Climate feedbacks analysed from GCMs Negative Lapse Rate FB partially compensates positive water vapor FB -> Reduction of spread in WV+LR 21

22 Contribution of different feedbacks to the spread in CS Cloud feedback Surface albedo feedback Water vapor feedback Radiative effects only Source: Dufresne & Bony, Journal of Climate