Introduction to climate modelling: Evaluating climate models

Transcription

1 Introduction to climate modelling: Evaluating climate models Why? How? Professor David Karoly School of Earth Sciences, University of Melbourne Experiment design Detection and attribution of climate change Why evaluate climate models? Higher confidence if a good model is used The major difference between a climate model and a weather forecast model is that the climate model is used to predict the longer term behavior of the global climate system The chaotic nature of the atmosphere means that deterministic predictions for a specific time are not possible While numerical weather predictions can be tested using observations, it is harder to directly test climate model predictions Need different strategies for testing climate models 1

2 Christopher Monckton in SPPI Monthly CO2 Report Jan 2010 compares observed global mean temperatures with models Comparison of observed global mean temperature for with climate model simulations

3 Recent global mean temperature variations from model simulations and observations From How to evaluate climate models? Compare with observations, preferably using multiple observational datasets to estimate uncertainty Spatial fields of long-term means; bias, RMS error, pattern correlation Mean seasonal cycle Measures of temporal variability at appropriate scales 3

4 Model? Annual mean precipitation Observations? IPCC WG1 AR4 Fig. 8.3 Taylor diagram Comparison of global precipitation field for 8 models - Radial distance shows variability - Angle shows pattern correlation - Distance from obs shows RMS error - Can also be used for time variations From Taylor (2005) 4

5 We think models adequately represent internal surface temperature variability on global scales Variability of observed and simulated annual global mean surface temperature ( ) ALL forcings 58 simulations 14 models IPCC WG1 AR4 Fig. 9.7 Experiment design Consider what you are trying to simulate and an appropriate model for such simulations Evaluate the model simulations using appropriate observational datasets Complete an ensemble of control model simulations and evaluate the performance of the model Complete an ensemble of perturbed model simulations and test for significant changes from the control simulations 5

6 Global mean temperature variations from models and observations Most of the observed increase in global average temperatures since the mid-20th century is very likely (more than 90% certain) due to the observed increase in anthropogenic greenhouse gas concentrations. IPCC(2007) WGI Fig TS.23 It is likely that there has been significant anthropogenic warming over the past 50 years averaged over each continent except Antarctica. IPCC WGI Fig SPM.4 6

7 What is detection and attribution? Detection of significant observed climate change and attribution of this observed change to one or more causes is a signal-in-noise problem: identifying possible signals in the noise of natural internal climate variations in the chaotic climate system. Detection is the process of demonstrating that an observed change is significantly different (in a statistical sense) than can be explained by natural internal climate variability. What is detection and attribution? Attribution of climate change to specific causes involves statistical analysis and the careful assessment of multiple lines of evidence to demonstrate that the observed changes are: unlikely to be due entirely to internal climate variability; consistent with the estimated responses to a given combination of anthropogenic and natural forcing; and not consistent with alternative, physically plausible explanations of recent climate change 7

8 Requirements of detection and attribution? Variable with high signal-to-noise ratio Long observational record Long control model simulations and ensembles of forced climate model simulations Consistent response to specified forcings between different models consistent signals Separable signals between different forcings Statistical analysis methods that enhance signal relative to noise and for identifying signals in observed changes Fingerprint detection and attribution Greater confidence when We are able to separate the contributions to observed change from individual sources Decompose the observed global surface temperature change into space-time patterns from GHG forcing, aerosol forcing, natural forcing, internal variability Account for multiple known sources of uncertainty Models and observations agree on the amplitude of the contributions Able to demonstrate that competing explanations are not viable Models simulate similar levels of internal variability as observed 8

9 Fingerprints Solar Volcanic 20th century response to forcing simulated by PCM GHGs Ozone IPCC WG1 Fig. 9.1 Direct SO 4 aerosol All Observations Model Filtering and projection onto reduced dimension space Y Y = β X + ε X Total least squares regression in reduced dimension space Evaluate amplitude estimates βˆ εˆ Weaver and Zwiers, 2000 Evaluate goodness of fit 9

10 Y = (X î)â + å Observations represented in a dimension-reduced space Typically filtered Spatially (to retain large scales) Temporally (to retain decadal variability decades) Projected onto low-order space-time EOFs Signals estimated from Multi-model ensembles of 20 th century simulations With different combinations of external forcings Anthropogenic (GHG, aerosols, etc) Natural (Volcanic, solar) Multiple models, ensemble sizes from 1-9 runs Assume linearity of response IPCC WG1 AR4 Fig. TS-23 Most of the observed increase in global average temperatures since the mid-20th century is very likely due to the observed increase in anthropogenic greenhouse gas concentrations Estimated contribution from greenhouse gas (red), other anthropogenic (green) and natural (blue) components to observed global mean surface temperature changes, based on optimal detection analyses

11 Simple indices of climate variability & change Select a small number of indices of surface temperature variations that represent key features of spatial patterns of natural climate variations and anthropogenic climate change (following Braganza et al., 2003, 2004) Want indices that show a common signal due to greenhouse climate change but are nearly independent for natural climate variations Global mean surface temperature (GM) Mean land ocean temp difference (LO) Mean magnitude of the annual cycle over land (AC) Mean meridional temperature gradient in the NH (MTG) CMIP5 historical runs with different forcings Consider 8 CMIP5 models, all with multi-member ensembles for historical D&A runs with different forcings Can ESM2, CNRM-CM5, CSIRO Mk3.6, GFDL CM3, GISS E2_H, GISS E2_R, HadGEM2-ES, NCAR CCSM4 CMIP5 D&A experiments: all historical forcings (hist), historical GHG only (histghg) and historical Natural forcings only (histnat), 11

12 Decadal variability of the indices Observations (Obs.; left to right (yellow circles) GHCN-ERSST, HadCRUT4, GISS) and 8 CMIP5 models for all ensemble members for the hist (grey squares), histghg (red diamonds) and histnat (green pentagrams) experiments. Evolution of the indices for all forcings CMIP5: mean is solid line, 5%-95% range is shaded blue. CMIP3: mean is dash-dot line, 5%-95% range is dotted lines. Obs: Coloured lines, NCDC - blue, HadCRUT3c - red, GISS - green 1 2

13 Trends in the indices Symbols as before. The grey band centred on zero is the 5%-95% confidence interval for zero trend in a single realisation. Conclusions Climate model simulations represent both forced and chaotic variations of the climate system Agreement between climate model simulations and observations requires that the observed variations lie within the range of simulated variations from a large ensemble of model simulations There is generally better agreement between the pattern and magnitude of simulated and observed temperature variations, than for precipitation variations, or at smaller spatial scales All models are wrong! but some are useful (G E Box) 1 3

14 References A Climate Modelling Primer, 3rd edn., K McGuffie and Ann Henderson-Sellers IPCC Fourth Assessment Report Climate Change 2007: The Physical Science Basis, chapter 8 Climate models and their evaluation (available from Taylor, K.E. (2005) Taylor Diagram Primer www-pcmdi.llnl.gov/about/staff/taylor/cv/taylor_diagram_primer.pdf Drost, F., and D. J. Karoly (2012) Evaluating global climate responses to different forcings using simple indices, Geophys. Res. Lett., 39, L16701, 5pp, doi: /2012gl