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1 Supplementary Information for: Linkage between global sea surface temperature and hydroclimatology of a major river basin of India before and after 1980 P. Sonali, Ravi S. Nanjundiah, & D. Nagesh Kumar Manuscript Number: ERL

2 Supplementary Information README 1. Fingerprint-based detection and attribution analysis Wider spread warming in climate during second half of the 20 th century is very likely due to the human induced anthropogenic effects, and temperature at the regional scale is increasing in line of global warming (IPCC 2013). Relatively lesser work related to climate change detection and attribution has been done in India compared to other countries (Mujumdar 2017, Sonali and Nagesh Kumar 2016). Climate change involves many complicated interactions among natural external, internal forcings and human induced anthropogenic effects. Fingerprint-based detection and attribution formally analyzes the likelihood of observed changes occurring due to natural climate variability and attributes the detected changes to different causative factors (Hidalgo et al 2009). MRB's changing climate is critical and important to be analyzed because of its geo-climatic setting (discussed in Introduction section). Water flow in this river is influenced by rainfall received predominantly during monsoon season. Mondal and Mujumdar (2012) in their detection and attribution study on monsoon precipitation and streamflow of MRB, reported that the changes are not due to natural internal climate variability and highlighted the difficulties in unequivocal attribution across all climate models at a regional scale. But no study till date has attributed a cause (natural and man-made factors) to the change in temperature over MRB. Hence, a fingerprint-based detection and attribution study is performed to analyze the observed changes in seasonal and in MRB using simulations from the coupled model intercomparison project 5 (CMIP5). The all-forcings experiment (i.e. historical experiment in CMIP5) which simulates climate evolution over 20 th century precisely is adopted in the present analysis. Control experiments (i.e. picontrol experiment in CMIP5) which characterizes climate change since the preindustrial period are employed to assess the potential long-term control drifts to mainly represent the natural climate variability. All CMIP5 climate models include historical and picontrol experiment simulations. Additionally, for detection and attribution studies, experiments with natural forcing (such as, solar irradiance and volcanic activities) only (i.e. historicalnat experiment in CMIP5) and GHGs forcing only (i.e. historicalghg experiment in CMIP5) are employed to distinguish between anthropogenic and natural influences on 20 th century climate (IPCC 2013). Detailed information on different experiments can be found in CMIP5 archive (Taylor et al 2012).

3 An evaluation study of climate models' performance in simulating temperature of India has been conducted by Sonali et al (2016) and suggested models are employed for the present analysis. Four shortlisted models (each for and ) are considered based the availability of different experiments (such as, historicalghg, historicalnat) and length of picontrol simulations (picontrol 500 years) along with their performance. These extended historical experiments (historicalghg, historicalnat) are included first time in CMIP5 archive for attribution of climate change. Details of the models used and corresponding available experiments are presented in Table S1. Most of the historical simulations are available till Hence, the fingerprint-based detection and attribution analysis is performed considering seasonal and during To facilitate the analysis, both observations and model simulations are interpolated on a common grid of 1 1 longitude-latitude employing nearest neighbourhood. The arithmetic mean of the ensemble members from each model is considered. For attribution, multi-model ensemble mean (MMM) simulation (averaged considering all models) for each experiment are considered along with individual model simulations. Data over each grid are expressed in anomaly form considering climatological mean calculated over the entire period i.e The general idea of fingerprint-based detection and attribution is to search the variable change in a low-dimensional space and use it to estimate the attribution index i.e. signal strength (S). The S is the trend of the climate vector (observations or model simulations) that are projected into fingerprint (Hegerl et al 1996): =., (S1) The fingerprint determines the direction of human-induced signal and is defined as the first empirical orthogonal function (EOF) of anthropogenically forced (obtained from historical experiment) MMM time series of seasonal temperature. T(x, t) is the time series either obtained from observations or model simulations from different experiments. The detected changes which are contrasted from natural climate variability (obtained from picontrol experiment) are attributed to different factors. Attribution is achieved by comparing S corresponding to different experiments (such as, historical, historicalghg and historicalnat) with that of observation. In line with Sonali and Nagesh Kumar (2016) study, Monte Carlo simulation is used to assess whether the observed significant changes have occurred due to natural variations in

4 the climate system (Hidalgo et al 2009). It evaluates the likelihood of observed signal strength traced in the distribution of signal strength obtained from the picontrol experiment. Combined simulations of picontrol experiment from all models in addition to individual model are considered for this analysis (Sonali and Nagesh Kumar 2016). Control simulations are added across all models (Combined all Models) and it comprises a total of 3020 years for and 2670 years for respectively. Step-by-step procedure for fingerprint-based detection and attribution analysis Repeat following steps for all the variables considered i.e., JF, MAM, JJAS, OND, Annual, JF, MAM, JJAS and OND in this study separately. Estimate the fingerprint F(x) using MMM historical simulation Compute S (signal strength) for observation and all model simulations from different experiments such as "picontrol", "historical", "historicalnat" and "historicalghg" using equation S1(as described above). Select randomly a group of non-overlapping k (k=1 for observation, k=4 for historical simulation (i.e. 4 model simulations considered for historical experiment)) members among all n-year segments of control simulations. Here n is the length of the observed time series (n= 56, i.e ). Ensemble averaged k members are used to calculate the S. Repeat this 10,000 times to obtain a distribution of control S. Check whether the observed S falls beyond range of control S distribution at 5% significance level. If the observed S lies beyond the range, then it can be claimed that changes are not due to natural internal variability and human influence on climate are discernible. Similarly, check whether the S of historical simulations are drawn from the control S distribution. Once detection is achieved, attribution analysis will be performed on those detected variables (where the observed changes are different from natural internal climate variability). For attribution, observed S are compared with S for different climate model experiments (for unequivocal attribution the observed S should be consistent with the S of "historical" or "historicalghg" simulations and simultaneously inconsistent with the S of "historicalnat" simulations).

5 The results of Monte Carlo simulations are presented in Table S2. In Table S2, a circle (O) indicates observed signal strength is statistically different from control signal strength at 5% significance level. Comparison between observed and control signal strengths showed that the observed signal strengths are inconsistent with the natural internal variability and the differences are statistically significant (at p <5%). It is found that 3 out of 10 considered variables i.e. OND, MAM and JJAS qualified the test. Disagreement is seen across models, but test qualified in major cases (3 out of 5 cases: i.e. 2 individual model and combination of all models) and hence it is concluded that the changes in OND, MAM and JJAS are unnatural. Monte Carlo simulation is again performed considering these three variables and found that the signal strengths of historical experiment are statistically different from control signal strengths. Further attribution analysis is performed for these detected variables. The observed OND signal strength is very close to zero. Hence, results are shown only for MAM and JJAS in Figure S1 and Figure S2 respectively. The signal strengths (S values) and their 95% confidence intervals related to different experiments such as, "historical" (blue), "historicalghg" (red) and "historicalnat (green) along with observations (black) are plotted. As discussed, signal strengths of different experiments from individual model and MMM are obtained. The results indicate observed signal strength (in black) of MAM and JJAS does not contain zero at 95% confidence level and showed positive signals. In both the cases, MMM_historicalGHG signal strengths (which are marked in red) are consistent in sign and magnitude with the observed signal strengths (shown in black). Similarly, the observed signal strengths are close to MMM signal strengths of historical experiment (which are marked in blue) and the difference (difference in S value between observation and MMM historical experiment) is statistically insignificant. However, the signal strengths of MMM historicalnat experiment (which are marked in green) include zero in both the cases (Figure S1 and S2). Hence the natural external forcings alone is inadequate to explain the observed changes. Summarizing the analysis: among all considered variables unequivocal attribution is achieved in case of during pre monsoon and monsoon seasons and hence the changes can be confidently attributed to anthropogenic effects. Further research considering urbanization, growth in population and land-use change is strongly recommended to enhance the urban planning and water resources management.

6 Table S1. Details of the considered climate models used for fingerprint-based detection and attribution analysis N0 Model_ Institution picontrol 500 years historical historicalnat historicalghg 1 CCSM4 National Center for Atmospheric Research Yes Yes Yes Yes 2 MIROC5 Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, and Japan Agency Yes Yes for Marine-Earth Science and Technology 3 CESM1-BGC National Science Foundation, Department of Energy, National Center for Atmospheric Research Yes Yes 4 CNRM-CM5 Centre National de Recherches Meteorologiques / Yes Yes Yes Yes N0 Model_ Institution picontrol 500 years historical historicalnat historicalghg 1 CCSM4 National Center for Atmospheric Research Yes Yes Yes Yes 2 MIROC5 Atmosphere and Ocean Research Institute, National Institute for Environmental Studies, and Japan Agency Yes Yes for Marine-Earth Science and Technology 3 CESM1-BGC National Science Foundation, Department of Energy, National Center for Atmospheric Research Yes Yes 4 MRI-CGCM3 Meteorological Research Institute Yes Yes Yes Yes 6

7 Table S2. Results from Monte Carlo simulation. Symbols circle (O) and not equal ( ) respectively indicate observed signal strengths are statistically different and not different and from control simulations at 5% significance level. Climate Model ( ) picontrol Experiment Annual JF MAM JJAS OND CCSM4 O picontrol (Combined) O CESM1-BGC O CNRM-CM5 MIROC5 Climate Model ( ) picontrol Experiment Annual JF MAM JJAS OND CCSM4 picontrol (Combined) O O CESM1-BGC O O MIROC5 O O MRI-CGCM3 7

8 Table S3.,, Precipitation () & Climatic Indices lag-lead relationship for the period 1950 to (Inside each box, & symbols respectively indicate significant positive and negative correlation at 5% level.) Seasons Index DMI IOD_E_Box IOD_W_Box EQWIN Niño3.4 Niño1+2 Niño3 Niño4 Trans-Niño SOI Winter Pre monsoon Monsoon Post monsoon Lag Lead Lag Lead Lag Lead Lag Lead

9 Table S4.,, Precipitation () & Climatic Indices lag-lead relationship for the period 1981 to (Inside each box, & symbols respectively indicate significant positive and negative correlation at 5% level.) Seasons Index Winter Pre monsoon Monsoon Post monsoon Lag Lead Lag Lead Lag Lead Lag Lead DMI IOD_E_Box IOD_W_Box EQWIN Niño3.4 Niño1+2 Niño3 Niño4 Trans-Niño SOI 9

10 Figure S1. Signal strengths and their 95% confidence interval (bars) for various model experiments (individual and MMM) and observation for monsoon (MAM ) 10

11 Figure S2. Signal strengths and their 95% confidence interval for various model experiments (individual and MMM) and observation for monsoon (JJAS ) 11

12 (a) (b) (c) (d) (e) (f) Figure S3. Correlations between Global SLP and Mahanadi (a & b), (c & d) and Precipitation (e & f) in monsoon season during () and () time slots. 12

13 (a) (b) (c) (d) (e) (f) Figure S4. Correlations between SLP and Mahanadi (a & b), (c & d) and Precipitation (e & f) in post monsoon season during () and () time slots. 13

14 (b) (a) (c) (d) (e) (f) Figure S5. Correlations between vertical velocity at 500 hpa and Mahanadi Tmax (a & b), Tmin (c & d) and Precipitation (e & f) in monsoon season during () and () time slots. 14

15 (a) (b) (d) (c) (e) (f) Figure S6. Correlations between vertical velocity at 500 hpa and Mahanadi Tmax (a & b), Tmin (c & d) and Precipitation (e & f) in post monsoon season during () and () time slots. 15

16 References Hegerl G C, von Storch H, Hasselmann K., Santer B D, Cubasch U, and Jones P D 1996 Detecting greenhouse-gas induced climate change with an optimal fingerprint method. J. Clim Hidalgo H G, Das T, Dettinger M D, Cayan D R, Pierce D W, Barnett T P, Bala G, Mirin A, Wood A W, Bonfils C, Santer B D and Nozawa T 2009 Detection and attribution of streamflow timing changes to climate change in the Western United States. J. Clim. 22 (13), IPCC (Intergovernmental Panel on Climate Change) 2013 Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change 1535 pp. Cambridge Univ. Press. Cambridge. U. K. and New York Mondal A and Mujumdar P P 2012 On the basin scale detection and attribution of human induced climate change in monsoon precipitation and stream flow Water. Resour. Res. 48W10520 Mujumdar P 2017 Hydrologic Modelling. Proceedings of the Indian National Science Academy 82(3) Sonali P, Nagesh Kumar D and Nanjundiah R S 2016 Intercomparison of CMIP5 and CMIP3 Simulations of the 20th Century Maximum and Minimum Temperatures over India and Detection of Climatic Trends. Theor. Appl. Climatol Sonali P and Nagesh Kumar D 2016 Detection and attribution of seasonal temperature changes in India with climate models in the CMIP5 Archive. J. Water Clim. Change. 7(1) Taylor K E, Stouffer R J and Meehl G A 2012 An Overview of CMIP5 and the experiment design. Bull. Am. Meteorol. Soc

Correction notice. Nature Climate Change 2, (2012)

Correction notice. Nature Climate Change 2, (2012) Correction notice Nature Climate Change 2, 524 549 (2012) Human-induced global ocean warming on multidecadal timescales P. J. Gleckler, B. D. Santer, C. M. Domingues, D.W. Pierce, T. P. Barnett, J. A.