Effects of initial conditions uncertainty on regional climate variability: An analysis using

Transcription

1 1! 2! Effects of initial conditions uncertainty on regional climate variability: An analysis using a low-resolution CESM ensemble 3! 4! Ryan L. Sriver 1, Chris E. Forest 2,3,4, and Klaus Keller 3,4,5 5! 6! 7! 1 Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA 8! 9! 2 Department of Meteorology, The Pennsylvania State University, University Park, PA 10! 11! 12! 3 Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA 13! 14! 4 Department of Geosciences, The Pennsylvania State University, University Park, PA 15! 16! 17! 5 Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA 18! 19! 20! Revised Draft submitted to: Geophysical Research Letters 21! 22! June 2, !! 1!

2 24! 25! 26! 27! Key Points: 1. We show CESM results sampling internal variability of the fully-coupled system 2. Ensemble exhibits skill in capturing temperature and precipitation statistics. 3. Work outlines a useful framework for UQ to guide impacts analyses 28! 29! 30! 31! 32! 33! 34! 35! 36! 37! 38! 39! 40! 41! 42! Abstract: The uncertainties surrounding the initial conditions in Earth system models can considerably influence interpretations about climate trends and variability. Here we present results from a new climate change ensemble experiment using the Community Earth System Model (CESM), to analyze the effect of internal variability on regional climate variables that are relevant for decision-making. Each simulation is initialized from a unique and dynamically consistent model state sampled from a ~10,000 year fully-coupled equilibrium simulation, which captures the internal unforced variability of the coupled Earth system. We find that internal variability has a sizeable contribution to the modeled ranges of temperature and precipitation. The effects increase for more localized regions. The ensemble exhibits skill in simulating key regional climate processes relevant to decision makers, such as seasonal temperature variability and extremes. The presented ensemble framework and results can provide useful resources for uncertainty quantification, integrated assessment, and climate risk management. 43! 44! 45! Index Terms: Earth System Models; Climate Change Ensemble; Natural Climate Variability 46!! 2!

3 47! 48! 49! 50! 51! 52! 53! 54! 55! 56! 57! 58! 59! 60! 61! 62! 63! 64! 65! 66! 1. Introduction Climate models are valuable tools for understanding how the climate system is changing. The resulting climate projections are, however, considerably uncertain. Three main sources of uncertainty are due to: 1) internal variability, 2), forcing uncertainties, and 3) structural differences between models (Hegerl et al., 2000; Hawkins and Sutton, 2009; Yip et al., 2011; Kirtman et al., 2013). The relative contributions of these uncertainties depend on the spatial and temporal scales of climate change projections. Internal variability represents the natural (unforced) variability of the climate system. The effects of internal variability are considerable on interannual to decadal projections, particularly for regional scales (Deser et al., 2012a and 2012b). Forcing uncertainties are due to lack of knowledge about the future emissions of anthropogenic forcings, leading to divergence in projections on inter-decadal time scales (Moss et al. 2010; Kirtman et al., 2013). Structural uncertainties arise due to differences in numerical formulations of the physics and parameterizations of sub-grid scale processes across models leading to varying responses to a given forcing. These uncertainties are primarily responsible for intermodel differences in key climate responses, such as climate sensitivity, aerosol forcing and ocean heat uptake, as well as topographic effects due to different model grid resolutions. Structural uncertainties can influence the spread of climate change metrics across a wide range of spatial and temporal scales (Hawkins and Sutton, 2009; Flato et al., 2013; Collins et al., 2013). 67! 68! 69! The effect of forcing uncertainty and structural model differences on climate change projections is a central focus of international modeling efforts, such as the Coupled! 3!

4 70! 71! 72! 73! 74! 75! 76! 77! 78! 79! 80! 81! 82! Model Inter-Comparison CMIP phases 3 and 5 (CMIP5) (Taylor et al., 2012). However, unforced variability has been identified as an important driver of inter-annual to interdecadal scale trends (Deser et al., 2012). Studies have pointed to its importance in explaining the recent observed slowdown in anthropogenic global warming (e.g. Huber and Knutti, 2014), and it can have important implications for quantifying uncertainties surrounding projections of regional climate change extremes (Fischer et al., 2013; Perkins and Fischer, 2013). Unforced variability also poses challenges for separating externally-forced climate signals from random natural variability within the system (Hegerl et al., 2000; Huber and Knutti, 2014), particularly for the relatively noisy time series on regional-to-local scales (Deser et al., 2014). The effects of unforced variability can substantially contribute to uncertainties in decadal-scale regional climate prediction, while differences in emissions scenarios become the dominant source of uncertainty for end-of-century projections (Hawkins and Sutton, 2011; Monier et al., 2014). 83! 84! 85! 86! 87! 88! 89! 90! 91! 92! Several recent studies have used ensemble-based approaches to address the importance of internal variability on climate change projections (Deser et al., 2012a and 2012b; Fischer and Knutti, 2013; Kay et al., 2014; Monier et al., 2014). These approaches typically feature a single climate model, in which the atmospheric state is perturbed slightly at a predetermined time point of the integration to approximate the effects of uncertainty in initial conditions uncertainties on projections. This atmospheric perturbation can be applied through random round-off level variations of the atmospheric temperature (Fischer et al., 2013; Kay et al., 2014) or by slightly offsetting the atmospheric state (Deser et al., 2012a and 2012b). Aside from these small differences, the model! 4!

5 93! 94! 95! 96! 97! 98! 99! 100! 101! 102! 103! 104! 105! configuration and forcings are consistent for all ensemble members. The memory of the initial shock is lost within the atmosphere over several weeks due to the chaotic nature of atmospheric motions, after which each ensemble member exhibits its own unique realization of internal atmospheric variability. Using these perturbation techniques, only the atmosphere conditions are perturbed, and the initial conditions of the ocean, land, and sea-ice models are identical for all ensemble members. Studies focusing on unforced internal variability provide strong support for ensemble approaches that feature large numbers of simulations using a given model for climate change assessments (Deser et al., 2014; Kay et al., 2014). Results have provided important insight into quantifying uncertainties surrounding unforced variability for key climate change metrics, such as shifts in atmospheric circulations (Deser et al., 2012a), regional climate trends (Deser et al., 2012b), and regional changes in extreme temperature and precipitation (Fischer et al., 2013; Perkins and Fischer, 2013). 106! 107! 108! 109! 110! 111! 112! 113! 114! 115! These studies sampling the effects of atmospheric internal variability have broken important new ground and have been extremely useful for understanding the role of natural variability for climate change. However, it can be computationally expensive to perform relatively large numbers of simulations using a comprehensive Earth system model. Further, ensemble experiments up to this point have been virtually silent on the role of the ocean's internal variability, which is significant for relatively longer timescales than the atmosphere (years to decades) and can remotely influence regional climate through modulation of large-scale atmospheric circulation patterns. Building on these recent initial conditions studies, here we document a different ensemble approach using! 5!

6 116! 117! 118! 119! 120! 121! 122! 123! 124! 125! 126! the Community Earth System Model (CESM), in which we sample the combined effects of internal variability of the coupled system (including the atmosphere, ocean, land surface, and sea ice) in a self-consistent way. We use this ensemble to analyze interannual northern hemisphere climate variability and temperature/precipitation extremes across multiple spatial scales, and we evaluate model skill based on observational data products and results from 34 different CMIP5 models (see Supplementary Table S.1 for a summary of the CMIP5 models used in this study). The paper is organized as follows. Section 2 provides the descriptions of the climate model used in this study and the experimental design. Section 3 includes our key model results and discussion about ensemble skill and variability across multiple spatial and temporal scales. Section 4, outlines our main conclusions, implications, and potential extensions. 127! 128! 129! 130! 131! 132! 133! 134! 135! 136! 137! 138! 2. Methods The ensemble experiment uses the low-resolution version of the Community Earth System Model (CESM) (Gent et al., 2011; Shields et al. 2012). CESM is a fully-coupled climate model consisting of atmosphere, land, ocean and sea-ice model components linked through a central coupler that exchanges information between model components. Our configuration of the model (based on version CESM1.03) features an atmosphere component (Community Atmosphere Model version 4) with T31 spectral resolution (~ 3.75º x 3.75º) and 26 vertical levels. The ocean model component is the Parallel Ocean Program version 2 (POP2) (Smith et al., 2010) with a nominal horizontal grid resolution of 3º (less than 1º near the equator). The ocean model contains 60 vertical levels, which has been increased from 25 levels in earlier versions (Yeager et al., 2006) to be consistent! 6!

7 139! 140! 141! 142! 143! 144! with the higher resolution (nominal 1º horizontal resolution) configuration (Danabasoglu et al., 2012). The low-resolution version of CESM robustly captures key oscillatory coupled climate patterns, such as the interannual variability in tropical Pacific sea surface temperatures associated with the El Niño-Southern Oscillation (Shields et al., 2012). This version of the model exhibits several climate biases, such as lower sea surface temperatures at high-latitudes and excessive Arctic sea ice (Shields et al., 2012). 145! 146! 147! 148! 149! 150! 151! 152! 153! 154! 155! 156! 157! 158! 159! 160! 161! Here we use CESM to perform a climate change ensemble experiment where we sample the initial conditions uncertainty due to the joint internal unforced variability of the fullycoupled system (ocean, atmosphere, land, and sea-ice components). We use a ~10,000- year pre-industrial control simulation as the basis for the climate change ensemble. The control simulation is fully-coupled and forcing data is fixed at pre-industrial levels corresponding to the year Starting at year ~4200, we initialize 50 transient forced simulations ( ) using snapshots of the fully-coupled control run at 100 year intervals as starting points. We chose the year 4200 of the control run as the starting point for the climate change ensemble to ensure near-dynamic equilibrium of the deep ocean, diagnosed using the North Pacific deep ocean temperature trend between 40º and 60º North. The initial conditions for each transient simulation are unique, and differences between the initial conditions reflect the model's representation of the unforced variability of the fully-coupled system. For the transient forced simulations, we use historical anthropogenic and natural forcings between 1850 and 2005 and the Representative Concentration Pathway (8.5) for the projections to 2100 (Moss et al. 2010). The main drawback of this approach is that we use a lower resolution version of! 7!

8 162! 163! 164! 165! 166! 167! CESM than considered in previous studies (Deser et al., 2012; Perkins and Fischer, 2013; Kay et al., 2014). This choice is driven by the need for sizeable computational resources to run a multi-millennial scale fully-coupled equilibrium simulation. In addition, this analysis is silent on effects of other uncertainties, such as different model structures, grid resolution, parameters, and forcings (Hawkins and Sutton, 2009, 2011; Monier et al., 2014). 168! 169! 170! 171! 172! 173! 174! 175! 176! 177! 178! 179! 3. Results and Discussion We analyze ensemble results to identify spatial and temporal scales where the low resolution model ensemble exhibits skill in simulating temperature and precipitation metrics. The analysis regions vary from hemispheric to local (Figure 1). We evaluate model skill based on agreement with gridded observational products (Adler et al., 2003; Brohan et al., 2006; Maurer et al., 2002) and weather station data obtained from the National Climate Data Center ( and we compare our ensemble results with CMIP5 models to highlight the effect of internal variability within CESM versus structural differences between models. Our primary goal is to identify key climate variables and uncertainties that are captured within the ensemble, which in turn can inform regional analyses of climate impacts under climate change. 180! 181! 182! 183! 184! The low-resolution CESM ensemble generally simulates realistic interannual variability of summer temperature for a wide range of spatial scales, from hemispheric to local (Figure 2). The variance increases as we reduce the size of the averaging region. The CMIP5 ensemble tends to over-estimate the year-to-year-variations in temperature,! 8!

9 185! 186! 187! 188! 189! 190! 191! 192! 193! 194! 195! 196! 197! 198! particularly at regional scales, which is highlighted by a slightly larger average root mean square error for the historical period significant to the 99% level for all regions compared to the CESM ensemble (Supplementary Table S.2). Note, however, that the CMIP5 analysis features single simulations from the majority of different participating models that sample also the effects of uncertainties about model structures and parameter choices. As discussed in the introduction, structural model differences can lead to significant differences in climate responses, variability, and trends. In contrast, the CESM ensemble uses a single model that samples the initial conditions uncertainty associated with the internal variability of the fully-coupled climate system. It is difficult to differentiate between the effects of internal variability and structural model differences within the current analysis. Nevertheless, these results point to the potential usefulness of using a low-resolution Earth system model for large ensemble experiments to quantify the effects of internal variability for climate uncertainties on spatial and temporal scales relevant to decision-makers. 199! 200! 201! 202! 203! 204! 205! 206! 207! Similar to the temperature analysis (Figure 2), we find that the CESM ensemble robustly captures the interannual variability of summer precipitation across all averaging regions (Figure 3). The skill of the CESM ensemble (quantified as the average root mean square error of all simulations) is generally consistent with the CMIP5 ensemble (Supplementary Table S.2). However, the time period for the data-model comparison is relatively short (~20 years), and the precipitation variability within the model and observational products is large compared to the length of the observational record. These observational limitations provide important constraints on assessing model performance based on the! 9!

10 208! 209! 210! 211! 212! 213! 214! 215! historical period, as well as interpreting future changes. The results again highlight the skill of the low resolution CESM model in simulating realistic interannual variability in decision-relevant climate metrics. The ability of the low-resolution CESM model to simulate realistic interannual variability in precipitation can be potentially useful for separating forced versus unforced changes in precipitation under global warming, which can be particularly challenging given the significant influences of oscillatory climate patterns (e.g. El Niño-Southern Oscillation) on regional interannual variability (Collins et al., 2010). 216! 217! 218! 219! 220! 221! 222! 223! 224! 225! 226! 227! 228! 229! The frequency and severity of extreme temperature and precipitation events are increasing under climate change (Diffenbaugh and Ashfaq, 2010; Duffy and Tebaldi, 2012; Sillmann et al., 2013a; Sillmann et al., 2013b). A substantial fraction of climaterelated damages are caused by low-probability/high-impact events (e.g. Melillo et al., 2014). As a result, a skillful representation of these processes and uncertainties within climate models is important for producing reliable climate impacts assessments. To illustrate the potential applicability of a low-resolution CESM ensemble to regional impacts analysis, we use a block maxima approach to analyze distributions of summer daily maximum temperature anomalies and precipitation totals aggregated across multiple regions (Figure 4 and Supplementary Figures S.1). The distributions can be approximated using the Generalized Extreme Value Distribution Function (Coles, 2001),. 0 ( " F(x;µ,σ,ξ) = exp 1+ξ $ x µ % + / * '- 10 ) # σ &, where the parameters ( µ,σ,ξ ) represent the location, scale, and shape parameters, 1/ξ ! 10!

11 230! 231! 232! 233! 234! 235! 236! 237! 238! 239! respectively. Similar approaches have been shown to be useful for analyzing the statistics of extreme precipitation events (DeGaetano, 2009) and local flood risks (Lempert et al., 2012). Quantile analysis indicates trends in the observed distributions of daily temperature and precipitation are negligible for the time periods and regions considered here (Supplementary Figures S.2-S.3 and Supplementary Table S.3), and we apply the same limiting assumptions to the model analysis. We compare CESM results against observational fields as well as CMIP5 models. All products are interpolated to the CESM grid to minimize the effect of resolution dependencies. This diagnostic can help to assess the strengths and weaknesses of the model in simulating the distributions of summer maxima during recent decades. 240! 241! 242! 243! 244! 245! 246! 247! 248! 249! 250! 251! 252! CESM tends to under-estimate the magnitude of daily temperature extremes (Figure 4). The bias increases as we reduce the size of the averaging region. We hypothesize that this effect is due to the relatively coarse model resolution, however, the low resolution model also exhibits a cold bias in summer temperature over the United States (Shields et al., 2012). The CESM ensemble generally captures the observed shape and scale of the extreme value distributions across all considered spatial scales (Supplementary Table S.4). Shifting each simulation s distribution by the mean model/data difference in location improves agreement between the observations and the model range for all averaging regions (Supplementary Table S.5). This type of analysis and correction technique may be useful for assessing model biases in simulating extremes, particularly at regional-to-local scales relevant for climate impacts assessments. Similar analysis of CMIP5 models yields a considerably larger spread than the CESM ensemble. Some of! 11!

12 253! 254! 255! 256! 257! 258! 259! 260! the CMIP5 models exhibit more realistic representations of temperature extremes statistics than CESM, while many models significantly over-estimate the observed temperature anomalies for all considered spatial scales even after the bias in the location parameter is removed (Supplementary Tables S.4-S.5). On the other hand, precipitation skill is much more closely linked to grid resolution (Supplementary Figure S.1) (Kopparla et al., 2014). The higher resolution CMIP5 models outperform the lowresolution CESM model in simulating extreme precipitation statistics for all considered spatial regions. 261! 262! 263! 264! 265! 266! 267! To address the role of grid resolution and scale-dependence on these extreme value statistics, we examine the local scale distributions of daily summer temperature and precipitation maxima using weather station data from Springfield IL (station GHCND:USW ) and ensemble results (CESM and CMIP5) (Figure 5). We compare model results from the native grids to site-level weather station data, in order to identify and characterize potential resolution dependencies. 268! 269! 270! 271! 272! 273! 274! 275! The low-resolution CESM under-estimates the magnitude of the temperature extremes, but generally captures the shape and scale of the distribution. On the other hand, most of the CMIP5 models overestimate the most extreme temperature anomalies (Figure 5a-b). The large spread in the higher resolutions CMIP5 models underscores the challenges in using multi-model ensembles for analyzing local scale extremes. Results suggest that initial conditions ensembles, such as presented here using the low-resolution CESM model, can potentially provide insight into the effect of internal variability of regional! 12!

13 276! 277! temperature extremes, when combined with statistical parameter estimation to quantify and correct biases in the distributions of extremes (Supplementary Tables S.4-S.5). 278! 279! 280! 281! 282! 283! 284! 285! 286! The model resolution is considerably more important for simulating the statistics of extreme precipitation events. This is perhaps expected because summer-time local precipitation extremes are typically convective in nature (Berg et al., 2013). While global models do not explicitly simulate convective processes, higher resolution models generally simulate more realistic convective precipitation statistics (e.g. through improved topographic forcing). Nearly all of the CMIP5 models outperform the low resolution CESM model, but they under-estimate the magnitude the extremes compared to observations (Figure 5c-d). 287! 288! 289! 290! 291! 292! 293! 294! 295! 296! 297! 298! 4. Conclusions We document a new climate change ensemble experiment that utilizes a low resolution configuration of the Community Earth System Model (CESM) to analyze the effect of the internal variability within the coupled system. The aim of our analysis is to identify spatial and temporal scales on which the model robustly characterizes key aspects of the observed variability. The low-resolution CESM ensemble exhibits skill in simulating interannual variability in seasonal temperature and precipitation across multiple spatial scales (from hemispheric to local). Further, the ensemble captures key statistical features of temperature extremes averaged across multiple regions (from U.S. to local). Our results indicate that ensemble frameworks using low-resolution coupled Earth system models that accurately simulate the large-scale dynamics (Gao et al., 2014) can be useful! 13!

14 299! 300! tools for quantifying uncertainty due to internal model variability and analyzing regional scale climate impacts. 301! 302! 303! 304! 305! 306! 307! 308! 309! 310! Our results support the importance of initial conditions ensembles to sample the effects of natural variability of the climate system, which can significantly contribute to regional climate uncertainties on interannual-to-decadal scales and influence interpretations about climate trends. Grid-level analyses and statistical estimation such as these provide insight to understanding the strengths and limitations of models for representing temperature and precipitation extremes, providing substantially more robust diagnostics for model evaluation over analysis using small numbers of simulations from a given model. In addition, these diagnostics can provide new tools for quantifying uncertainty surrounding tail-area events and informing regional impacts analyses. 311! 312! 313! 314! 315! 316! 317! 318! 319! 320! 321! Acknowledgements: We thank Robert Nicholas for useful discussions. This study was partially supported by the Department of Energy sponsored Program on Integrated Assessment Model Development, Diagnostics and Inter-Model Comparisons (PIAMDDI), the Penn State Center for Climate Risk Management, and the National Science Foundation through the Network for Sustainable Climate Risk Management (SCRiM) under NSF cooperative agreement GEO and NSF award SES GPCP Precipitation data provided by the NOAA/OAR/ESRL PSD, Boulder, Colorado, USA, from their Web site at HadCRUT temperature data accessed from Weather station data accessed from the National! 14!

15 322! 323! 324! 325! 326! 327! 328! Climate Data Center ( We acknowledge the World Climate Research Programme's Working Group on Coupled Modelling, which is responsible for CMIP, and we thank the climate modeling groups for producing and making available their model output. For CMIP the U.S. Department of Energy's Program for Climate Model Diagnosis and Intercomparison provides coordinating support and led development of software infrastructure in partnership with the Global Organization for Earth System Science Portals. All errors and opinions are those of the authors. 329!! 15!

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22 Latitude 448! Longitude 449! 450! 451! 452! 453! 454! 455! Figure 1. Map of the analysis regions considered in this study (highlighted by white boxes): Northern hemisphere (Region 1), United States (Region 2), Midwestern United States (Region 3), and central Illinois (Region 4). 456!! 22!

23 A. CESM D. CMIP5 Mean RMSE=8 C Mean RMSE=0.20 C B. E. Mean RMSE=0.41 C Mean RMSE=0.43 C C. F. Mean RMSE=1.06 C Mean RMSE=1.22 C 457! 458! 459! 460! 461! 462! 463! 464! 465! 466! 467! 468! 469! Figure 2. Time series of summer average temperature anomalies, averaged over the different regions highlighted in Figure 1, for the 50-member CESM ensemble (A-C) and 34 different CMIP5 models (D-F). Blue and red curves represent individual simulations from the CESM and CMIP5 ensembles, respectively. Gray curves indicate ensemble means, and black curves represent observational surface temperature from HadCRUT3 (Brohan et al., 2006). The Mean RMSE is the average of the root mean square error between the model simulations and the observational time series for overlapping periods. Projections are based on RCP8.5 forcing scenario. All anomalies are referenced to the period ! 23!

24 A. CESM D. CMIP5 Mean RMSE=2.20% Mean RMSE=2.07% B. E. Mean RMSE=4.81% Mean RMSE=5.17% C. F. Mean RMSE=20.0% Mean RMSE=23.54% 470! 471! 472! 473! 474! 475! 476! 477! Figure 3. As in Figure 2, except for summer precipitation anomalies. Observational precipitation (black curves) is from the Global Precipitation Climatology Project (GPCP) for years (Adler et al., 2013). 478! 479!! 24!

25 A. 1 United States (Region 2) Observations CESM D. 1 United States (Region 2) Observations CMIP5 1 F(x) 1 F(x) United States (Region 2) Midwest (Region 3) B. 1 F(x) 1 F(x) Observations Midwest (Region CESM 3) 1 E. 1 F(x) 1 F(x) Midwest (Region 3) Daily summer temperature anomaly (C) Daily summer temperature anomaly (C) C. Central Illinois (Region 4) F. Central Illinois (Region 4) F(x) 1 F(x) ! Daily summer temperature anomaly (C) Daily summer temperature anomaly (C) 481! 482! 483! 484! 485! 486! 487! 488! Figure 4. Survival function (1-cumulative frequency) of summer block maxima of daily surface temperature anomalies ( ) for the CESM ensemble (A-C) and CMIP5 models (D-F), averaged over different spatial areas shown in Figure 1: United States (Region 2), the Midwest (Region 3), and central Illinois (Region 4). Blue curves (A-C) represent individual CESM simulations (50 total), and red curves indicate individual simulations from different CMIP5 models (34 total). Black circles represent gridded observations (Maurer et al., 2002). Temperature fields from all CMIP5 models and gridded observations are interpolated to the CESM model resolution.! 25!

26 A. 1 Observations CESM B. 1 Observations CMIP5 1 F(x) 1 F(x) C. Daily summer temperature anomaly (C) 1 Observations CESM D. Daily summer temperature anomaly (C) 1 Observations CMIP5 1 F(x) 1 F(x) 489! 490! 491! Daily summer precipitation (mm) Daily summer precipitation (mm) 492! 493! 494! 495! 496! 497! 498! 499! Figure 5. Survival function (1-cumulative frequency) of summer block maxima of daily surface temperature anomalies (A-B) and daily summer precipitation (C-D), for single point locations corresponding to central Illinois during the period Black circles denote observations from a single weather station near Springfield, Illinois (station GHCND:USW ). Blue curves represent individual CESM ensemble simulations (50 total), and red curves represent individual simulations from different CMIP5 models (34 total).!all!model!results!reflect!single!point!values!on!the!models!native!grids,! corresponding!to!locations!closest!to!the!weather!station!data!point.! 26!

27 Geophysical,Research,Letters, Supporting*Information*for* Effects of initial conditions uncertainty on regional climate variability: An analysis using a low-resolution CESM ensemble! Ryan L. Sriver 1, Chris E. Forest 2,3,4, and Klaus Keller 3,4,5 1 Department of Atmospheric Sciences, University of Illinois at Urbana-Champaign, Urbana, Illinois, USA 2 Department of Meteorology, The Pennsylvania State University, University Park, PA * 3 Earth and Environmental Systems Institute, The Pennsylvania State University, University Park, PA 4 Department of Geosciences, The Pennsylvania State University, University Park, PA 5 Department of Engineering and Public Policy, Carnegie Mellon University, Pittsburgh, PA* * Contents!of!this!file!! * * Figures*S1*to*S3* Tables*S1*to*S5* *! * Introduction!! * This*document*contains*four*supplementary*figures*and*5*supplementary*tables.**We*present* expanded*analysis*of*precipitation*extremes*in*the*cesm*ensemble*and*observations*(figure* S1),*and*we*present*results*of*quantile*analysis*(Figure*S2IS3).**Table*S1*shows*the*list*of*CMIP5* models*used*in*the*study,*and*tables*s2is5*summarize*key*statistical*results.*! * 1

28 A. 1 United States (Region 2) Observations CESM D. 1 United States (Region 2) Observations CMIP5 1 F(x) 1 F(x) United States (Region 2) Midwest (Region B. 1 F(x) Midwest (Region 3) E. 1 F(x) 1 F(x) Observations Midwest (Region CMIP5 3) Daily summer precipitation (mm) 1 F(x) Daily summer precipitatio C. Central Illinois (Region 4) F. Central Illinois (Region 4) F(x) 1 F(x) Daily summer precipitation (mm) Daily summer precipitation (mm) Figure S1. Survival function (1-cumulative frequency) of summer block maxima of total daily precipitation ( ) for the CESM ensemble (A-C) and CMIP5 models (D-F), averaged over different spatial areas shown in Figure 1 of the main text: United States (Region 2), the Midwest (Region 3), and central Illinois (Region 4). Blue curves (A-C) represent individual CESM simulations (50 total), and red curves indicate individual CMIP5 simulations (34 total). Black circles represent gridded observations (Maurer et al., 2002). Precipitation fields from all CMIP5 models and gridded observations are interpolated to the CESM model ensemble resolution. 2

29 Temperature Precipitation A. United States (Region 2) D. United States (Region 2) Expected GEV Value (C) Expected GEV Value (mm) Observations (C) Observations (mm) B. Expected GEV Value (C) Midwest (Region 3) E. Expected GEV Value (mm) Midwest (Region 3) Observations (C) Observations (mm) C. Central Illinois (Region 4) F. Central Illinois (Region 4) Expected GEV Value (C) Expected GEV Value (mm) Observations (C) Observations (mm) Figure S2. Quantile-Quantile plots of summer block maxima of daily surface temperature anomalies (left column) and precipitation (right column) for the period , based on gridded observations (Maurer et al., 2002). Theoretical quantiles correspond to the estimated GEV distributions using a probability weighted moment method to estimate parameters. 3

30 Temperature Precipitation A. Daily summer temperature anomaly (C) United States (Region 2) 95% quantile 50% quantile 5% quantile D. Daily summer precipitation (mm) United States (Region 2) 95% quantile 50% quantile 5% quantile Year Year B. Daily summer temperature anomaly (C) Midwest (Region 3) E. Daily summer precipitation (mm) Midwest (Region 3) Year Year C. Daily summer temperature anomaly (C) Central Illinois (Region 4) F. Daily summer precipitation (mm) Central Illinois (Region 4) Year Year Figure S3. Quantile-time plots of summer daily surface temperature anomalies (left column) and precipitation (right column) for the period , based on gridded observations (Maurer et al., 2002). We highlight the 5% (blue), 50% (black) and 95% (red) quantiles, and dashed lines denote the trend lines. Trends and standard deviations of the residuals are listed in Supplementary Table S.3. 4

31 Model ACCESS1.0 ACCESS1.3 BCC-CSM1.1 BCC-CSM1.1(m) BNU-ESM CanESM CCSM4 CESM1(BGC) CESM1(CAM5) CESM1(FASTCHEM) CMCC-CESM CMCC-CM CMCC-CMS CNRM-CM5 CSIRO-Mk3.6.0 EC-EARTH FGOALS-g2 GFDL-CM3 GFDL-ESM2G GFDL-ESM2M INM-CM4 IPSL-CM5A-LR IPSL-CM5A-MR IPSL-CM5B-LR MIROC4h MIROC5 MIROC-ESM MIROC-ESM-CHEM MPI-ESM-LR MPI-ESM-MR MPI-ESM-P MRI-CGCM3 NorESM1-M Institution CSIRO (Commonwealth Scientific and Industrial Research Organisation, Australia), and BOM (Bureau of Meteorology, Australia) Beijing Climate Center, China Meteorological Administration College of Global Change and Earth System Science, Beijing Normal University Canadian Centre for Climate Modelling and Analysis National Center for Atmospheric Research National Science Foundation, Department of Energy, National Center for Atmospheric Research Centro Euro-Mediterraneo per I Cambiamenti Climatici Centre National de Recherches Meteorologiques / Centre Europeen de Recherche et Formation Avancees en Calcul Scientifique Commonwealth Scientific and Industrial Research Organisation in collaboration with the Queensland Climate Change Centre of Excellence EC-EARTH consortium LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences; and CESS, Tsinghua University Geophysical Fluid Dynamics Laboratory Institute for Numerical Mathematics Institut Pierre-Simon Laplace Atmosphere and Ocean Research Institute (The University of Tokyo), National Institute for Environmental Studies, and Japan Agency for Marine-Earth Science and Technology Japan Agency for Marine-Earth Science and Technology, Atmosphere and Ocean Research Institute (The University of Tokyo), and National Institute for Environmental Studies Max Planck Institute for Meteorology (MPI-M) Meteorological Research Institute Norwegian Climate Centre Table S.1: List of CMIP5 models used in this study. 5

32 Temperature Precipitation Northern Hemisphere (Region 1) United States (Region 2) Midwest (Region 3) Table S.2: Hypothesis test results analyzing the differences in the mean root mean square error between the CESM and CMIP5 ensembles shown in Figure 2 (summer average temperature) and Figure 3 (summer average precipitation). The values show the significance levels that the means are statistically different, based on two-sample, twosided t-tests. Temperature (C/year, C) 5% 50% 95% United States (Region 2) Midwest (Region 3) Central Illinois (Region 4) Precipitation (mm/year, mm) 5% 50% 95% Northern Hemisphere (Region 1) United States (Region 2) Midwest (Region 3) Table S.3: Trends and standard error results of the quantile-time plots shown in Supplementary Figure S.3 for summer daily temperature (upper panel) and precipitation (lower panel). For the specified quantiles, we include both the linear trend using 50 years of observational daily data (top entry) and the standard deviation of the residuals (bottom entry). 6

33 Observations CESM ξ µ σ ξ µ σ United States (Region 2) Midwest (Region 3) Central Illinois (Region 4) Table S.4: Best-fit Generalized Extreme Value (GEV) distribution parameters for summer block maxima of daily surface temperature anomalies shown in Figure 4, using a probability weighted moment method to estimate parameters. The parameters (ξ, µ, σ ) correspond to the shape, location, and scale of the distributions, respectively. CESM results represent the mean parameter estimates averaged over all 50 simulations. CESM (raw, bias-corrected) CMIP5 (raw, bias-corrected) United States (Region 2) 0.64 C 8 C 0.40 C 0.25 C Midwestern US (Region 3) 1.04 C 7 C 0.92 C 3 C Central Illinois (Region 4) 1.39 C 0.23 C 1.22 C 0.68 C Table S.5: Table of the averaged root mean squared error between the model ensembles and observations shown in Figure 4. We apply bias-correction by shifting the distributions according to the difference in modeled/observed location parameters. 7