1. Detection and Attribution of Changes in Arctic Temperature Mohammad Reza Najafi, Francis Zwiers, Pacific Climate Impacts Consortium (PCIC), Nathan Gillett, Canadian Centre for Climate Modeling and Analysis (CCCma) May 2014

2. Outline  Introduction  Data and Data Pre-processing  Methodology for Detection and Attribution (i.e. Optimal Fingerprinting Approach)  Results based on CRUTEM4 observational datasets o Over 1913-2012 (above 65°N and 60°N) o Seasonal D&A o Over 1953-2012 o Spatial segmentation  Results based on GISS and MLOST observational datasets  Temporal segmentation (using CRUTEM4, GISS, MLOST datasets)  Conclusions

3. Introduction  Surface air temperature is a robust climate parameter that can show large scale anomaly patterns and is commonly well observed.  Temperature variations cause changes in snowpack, timing of snow and sea ice melt, snow/rain partitioning of precipitation, hydrologic variables (such as the centroid of streamflows), etc.  The Arctic region has warmed significantly more than global mean surface air temperature, due to positive feedback processes such as sea ice and snowmelt.  Recent changes in Arctic temperature are outside the range of natural variability.  These changes are attributed to the combined effects of anthropogenic forcing agents such as greenhouse gases and sulfate aerosols.

4. Introduction  Observational data represents the net response of the climate system to forcing signals plus natural internal variability.  The climate change contributions from different forcings cannot be distinguished unambiguously by the use of observational data.  GCMs provide the tools to investigate the response of climate system to individual forcing signals. o Simulations with all anthropogenic forcing agents such as greenhouse gases, changes in aerosol emissions, anthropogenic ozone changes, changes in land cover and natural forcing (ALL). o Simulations forced by historical increases in anthropogenic greenhouse gases (GHG). o Historical natural forcing including solar variability and volcanic aerosol emissions (NAT).

5. Introduction o Other anthropogenic forcing agents, prominently the effects of tropospheric sulfate aerosols (OANT). o Control simulations, with no changes in greenhouse gas concentrations, solar output etc. (CTL).

6. Data  Global surface temperature observation datasets: o Climate Research Unit (CRU) surface temperature, version 4 (CRUTEM4) Incorporates additional station series and newly homogenized versions of many individual station records. Does not provide temperature estimates over unsampled areas. o Goddard Institute for Space Studies (GISS) Based on Global Historical Climatology Network (GHCN) Accounts for urban impacts through nightlights adjustments Provides temperature estimates over unsampled areas o NCDC Merged Land-Ocean Surface Temperature analysis (MLOST) GHCN-M is the source of the MLOST dataset Provides temperature estimates over unsampled areas  Multi-model simulations from the Coupled Model Intercomparison Project Phase 5 (CMIP5)

7. Data  42 CMIP5 models with 24,800 years of pre-industrial control run.

8. Questions…  Using the recent climate model simulations (i.e. CMIP5) and observational records with more spatio-temporal coverage, are the temperature changes attributable to human influence?  Are the separate contributions from greenhouse gases (GHG) and other anthropogenic forcing agents (OANT) to observed Arctic temperature detectable?  How sensitive the detection and attribution analyses are to the spatial and temporal domain, and to the choice of observation datasets?  Through a formal D&A analysis, what are the contributions of individual models and seasons to the overall attribution?

9. Data Pre-processing  Take Tmax/Tmin variables for GHG, NAT (up to 2013) and ALL simulations. o Obtain monthly mean land surface temperature.  If needed, extend ALL simulations from 2005 to 2012 using RCP4.5 runs.  Generate N-year segments of CTL runs (N is the number of years in the analysis).  Regrid CMIP5 to the resolution of CRUTEM4 observations (i.e. 5°×5°).  Obtain simulation anomalies from 1961-1990 climatology.  Mask simulations with observational coverage in space and time.  Calculate 5-year means if more than half of values are non-missing.  Discard grid cells with less than 70% missing values.

10. Data Pre-processing  Selected models with available ALL, GHG and NAT simulations up to 2012: o bcc-csm1-1 (1), CanESM2 (5), CNRM-CM5 (1), CSIRO-Mk3 (5), HadGEM2-ES (4), NorESM1-M (1) o Multi-Model analysis is based on the average of 17 ensemble members.  Obtain spatial mean by assigning equal weights to each grid cell.

11. Temperature Trends over 1913-2012 ObsALL

12. Temperature Trends over 1913-2012 GHGOANT

13. Multi-Model Temperature Anomalies (Above 65°N)

14. Methodology (Detection and Attribution)  Determine the amplitude of the response to each forcing agent from the observations using the optimal fingerprinting.  Based on a multiple linear regression model with its scaling factors (β i ) representing the individual contributions of the forcing agents.  In the total least square approach, T i is assumed to be unknown and is obtained from the ensemble mean of climate model runs.  represents the internal climate variability of the ensemble mean. ~Internal Climate Variability

15. Methodology (Detection and Attribution)  It is assumed that and are Gaussian. have covariance matrices proportional to that of.  The scaling factor determines whether the forcing signal is detected. A positive scaling factor (β i ) with 5%-95% range excluding zero implies that the signal is detected at the 5% significance level.  Values consistent with unity which have small uncertainty ranges show good agreement between model and observation (i.e. Attribution).  Is the residual variability ( ) consistent with the estimates of natural internal climate variability obtained from CTL runs? (Residual Consistency Test)

16.  In order to find scaling factors for other signals using ALL, NAT and GHG signals: o For two signal analysis: o For three signal analysis: Methodology (Detection and Attribution)

17. Scaling Factors Corresponding to ANT and NAT Signals

18. Scaling Factors Corresponding to GHG, OANT and NAT Signals

19. Attributable Temperature Trends

20. Scaling Factors (North of 60°N)

21. Seasonal Analysis

22. Analysis over 1953-2012

23. Analysis Based on Other Observational Datasets CRUTEM4

24. Analysis Based on Other Observational Datasets MLOST

25. Analysis Based on Other Observational Datasets GISS

26. D&A Based on Other Observational Datasets Goddard Institute for Space Studies (GISS) Merged Land-Ocean Surface Temperature analysis (MLOST)

27. Seasonal Partition CRUTEM4

28. Seasonal Partition MLOST

29. Seasonal Partition GISS

30. Conclusions  Large-scale changes in the Arctic temperature are attributable to human influence.  Separate contributions from greenhouse gases and other anthropogenic forcing agents to the observed signal are discernable.  Separate detection of OANT signal is mainly due to the cooling period of 1953-1968.  The contribution from natural forcing agents to the observed signal is not detected.  Results are robust to the choice of observational dataset.  Results are robust to the high latitude regional domain (i.e. 60°N and 55°N, not shown here).  Similar conclusions are obtained from the analysis over 1953-2012.

31. References  Bonfils C, Santer BD, Pierce DW, Hidalgo HG, Bala G, Das T, et al. Detection and attribution of temperature changes in the mountainous western United States. Journal of Climate 2008, 21(23).  Brutel-Vuilmet C, Ménégoz M, Krinner G. An analysis of present and future seasonal Northern Hemisphere land snow cover simulated by CMIP5 coupled climate models. Cryosphere 2013, 7(1).  Fyfe JC, von Salzen K, Gillett NP, Arora VK, Flato GM, McConnell JR. One hundred years of Arctic surface temperature variation due to anthropogenic influence. Scientific reports 2013, 3.  Gillett NP, Stone DA, Stott PA, Nozawa T, Karpechko AY, Hegerl GC, et al. Attribution of polar warming to human influence. Nature Geoscience 2008, 1(11): 750-754.  Ribes A, Planton S, Terray L. Application of regularised optimal fingerprinting to attribution. Part I: method, properties and idealised analysis. Climate dynamics 2013, 41(11-12): 2817-2836.  Wang M, Overland JE, Kattsov V, Walsh JE, Zhang X, Pavlova T. Intrinsic versus forced variation in coupled climate model simulations over the Arctic during the twentieth century. Journal of Climate 2007, 20(6).

32. Thank you…