El Niño and a record CO 2 rise

Transcription

1 1 2 3 El Niño and a record CO 2 rise Richard A. Betts, Chris D. Jones, Jeff R. Knight, Ralph F. Keeling, John J. Kennedy Data sources: CO 2 concentrations, emissions and sea surface temperatures CO 2 concentration data were from the Scripps Institution of Oceanography 1,2 We used "filled" but not "seasonally adjusted" data. The minimum daily value for September was previously published as ppm [Ref.3], but following a recalibration, measurements reported since April 2015 were adjusted upwards by 0.4 ppm [Ref.4]. CO 2 emissions data were from the Global Carbon Project 5,6. Fossil fuel and land-use emissions are both included up to 2013 inclusive. Emissions data were extended to 2014 and 2015 according to a calculation of the annual carbon budget 5 : 2014 emissions were 10.9 GtC (9.8 GtC fossil fuel and 1.1 GtC land use) and 2015 emissions were 10.3 GtC (9.2 GtC fossil fuel and 1.1 GtC land use). 15 SST observations are from the HadSST gridded dataset 7,8. This is an ensemble dataset reflecting uncertainties in the method for deriving a homogenized, gridded dataset from source observations. Here we used the ensemble median. Forecast SSTs were from the GloSea5 global seasonal forecasting system 9, run in November GloSea5 is a global ocean-atmosphere general circulation model, initialized by assimilating recent observations with a set of small perturbations to provide an ensemble of 40 members. Here, the initial condition observations were from 5-25 October 2015 (for the November mean) and 9-29 November 2015 (for the mean conditions in December to March). The output was bias-corrected using a suite of hindcast simulations initialised around the same dates in the years 1996 to 2009 to account for the possibility of systematic drifts. The result was corrected to be an NATURE CLIMATE CHANGE 1

2 anomaly with respect to the climatology, consistent with the data from HadSST All uncertainties in the paper are two standard deviations from the mean ENSO-CO 2 growth rate relationship We based our reconstruction and prediction of the annual CO 2 increment on a bilinear relationship 10 which uses calendar year mean global emissions of CO 2 and the April-March annual mean sea surface temperature (SST) in an area of the equatorial Pacific Ocean as predictors. This used a lag of Mauna Loa CO 2 behind SST anomalies of 3 months found in previous work 11, which had focused on the Niño3 region (90 W and 150 W, 5 N and 5 S) Testing predictive skill of the regression As a simple test of the predictive skill of the statistical relationship between SSTs and the CO 2 growth rate anomaly, we first used the regression coefficients previously derived from data for , applied them to observed Niño3 SSTs and emissions for and compared with the observed CO 2 concentrations data. This provided an out-of-sample hindcast of the CO 2 growth rate anomalies since The match between the hindcast and observed growth rates, with a correlation coefficient of 0.67 and standard deviation of observed-reconstructed values of 0.24 ppm, suggests that the regression developed in 2005 had some predictive skill (Fig. S1). This suggests that, had it been possible to forecast the year-by-year variability in tropical Pacific SSTs, a forecast of the variability of CO 2 growth rate would also have been possible. 2 NATURE CLIMATE CHANGE

3 observed reconstructed blind test CO 2 growth rate (ppm / yr) year Figure S1. Testing the predictive skill of the relationship between Niño3 SST anomalies and Mauna Loa CO 2 growth rates. Timeseries of CO 2 growth rate anomaly from observations (black) and reconstructed from regression against emissions and Niño3 anomaly before 2005 (blue) and after 2005 (red), using regression coefficients calculated on the basis of data available in Update for the El Niño In the El Niño, the highest SST anomalies were focused more in the Niño3.4 of the central Pacific, as opposed to the Niño3 further towards the east. We therefore recalculated the regression using Niño3.4 SSTs and the full range of SST, CO 2 emissions and CO 2 concentrations data up to We find an improved fit of reconstructed and observed CO 2 concentrations, which improves further if 1992 and NATURE CLIMATE CHANGE 3

4 are excluded the CO 2 growth rate in those years was affected primarily by the temporary cooling effect of the Mt Pinatubo eruption 11.We then updated the regression using the full data record to 2014 to perform the growth rate forecast. Equation 1 including the coefficients is: ΔCO 2 = N Ɛ with units of ppm for ΔCO 2, Kelvin for the Niño3.4 SST anomaly N, and GtC for emissions Ɛ Forecasting the CO 2 growth rate To obtain the CO 2 increment for 2016, we first used the ensemble median HadSST3 observed SST data from 1 st April to 31 st October 2015, and combine this with SSTs from the 40-member GloSea5 forecast from 1 st November 2015 to 31 st March 2016 to give 40 realisations which are identical in the first six months. These were then used in the regression relationship to make 40 forward projections of the annual CO 2 growth rate using 2014 and 2015 emissions data as above. Once 2015 and 2016 annual increments were calculated they were added to annual mean CO 2 concentrations from 2014 to create annual mean projections. Monthly projections were then created by first diagnosing a simple seasonal cycle and then adding this to the projected annual value. The seasonal cycle was derived by subtracting a linear trend from the last 5 years ( inclusive) and creating an average of each month within that period. Uncertainty in the CO 2 forecast includes the uncertainty from the combined observed and forecast SSTs and that from the regression, and is 4 NATURE CLIMATE CHANGE

5 88 89 given as two standard deviations from the mean. Estimated uncertainty in emissions data are relatively small and so are not taken into account We later repeated the process using observed SSTs to March 31 st, to assess the impact of biases in the forecast SSTs for the forecast CO 2 rise. This updated forecast is shown in the green bar in Fig. 1(a). However we regard the original forecast using GloSea5 forecast SSTs as our main prediction of interest, in order to assess the predictability of the CO 2 rise a full year ahead including the contribution of the SST forecast Additional CO 2 forecast for a high-latitude location Repeating the regression for Barrow in Alaska gives a less good fit, indicating a lower level of predictability due to larger interannual variability in the growth rate (Fig. S2). While a longer lag (eg. 8 months [Ref. 2]) may be applicable for the Barrow regression, we did not explore this here and instead used the same annual mean SST as for the Mauna Loa regression (April-March) throughout. Nevertheless, we forecast a growth rate of 3.1 ppm/yr at this location. The annual mean CO 2 concentration is forecast as ppm, but concentrations below 400 ppm will still be seen in late summer as the seasonal cycle of CO 2 is of larger amplitude than at Mauna Loa. NATURE CLIMATE CHANGE 5

6 batmospheric CO2 concentration (ppm) a Forecast Hindcast GtC / yr 4 ppm / yr CO 2 growth rate year Observed CO 2 concentration Forecast CO 2 concentration c atmospheric CO 2 concentration (ppm) year Observed CO 2 concentration Forecast CO 2 concentration year Figure S2 CO 2 observations, reconstructions and forecast for Barrow. (a) Observed (black), reconstructed (blue) and forecast (orange) CO 2 growth rates. (b) observed (black) and hindcast / forecast (orange) CO 2 concentrations from 1974 to (c) as (b) from Width of orange shading shows uncertainty in hindcast / forecast as two standard deviations from the mean. 6 NATURE CLIMATE CHANGE

7 Forecast verification Verifying CO 2 data can be obtained from the Scripps dataset 2, and also from NOAA s Global Monitoring Division 12 which are independent from the Scripps CO 2 data used to create our regression. These are regularly updated and will be used to verify our growth rate projection at the end of References 1. Keeling, C.D., et al. Exchanges of atmospheric CO 2 and 13CO 2 with the terrestrial biosphere and oceans from 1978 to I. Global aspects, SIO Reference Series, No , Scripps Institution of Oceanography, San Diego, 88 pages, Accessed 1 Dec Keeling, R.F. Is this the last year below 400? October 21, Keeling, R.F. Measurement note: an adjustment to the record. November 9, Le Quéré, C., et al. Global Carbon Budget Earth Syst. Sci. Data, 7, Doi: /essd (2015) Accessed 10 Nov Kennedy J.J., Rayner, N.A., Smith, R.O., Saunby, M. & Parker, D.E. Reassessing biases and other uncertainties in sea-surface temperature observations since 1850 part 1: measurement and sampling errors. J. Geophys. Res., 116, D14103, doi: /2010jd (2011). NATURE CLIMATE CHANGE 7

8 Kennedy J.J., Rayner, N.A., Smith, R.O., Saunby, M. & Parker, D.E.. Reassessing biases and other uncertainties in sea-surface temperature observations since 1850 part 2: biases and homogenisation. J. Geophys. Res., 116, D14104, doi: /2010jd (2011) 9. MacLachlan, C., et al. Global Seasonal forecast system version 5 (GloSea5): a high-resolution seasonal forecast system. QJR Meteorol Soc, doi: /qj.2396 (2014) 10. Jones, C.D. & Cox, P.M. On the significance of atmospheric CO 2 growth rate anomalies in , Geophys. Res. Lett., 32, L14816, doi: /2005gl (2005) 11. Jones, C. D., Collins, M., Cox, P.M. & Spall, S.A. The carbon cycle response to ENSO: A coupled climate-carbon cycle model study, J. Clim., 14, (2001) NATURE CLIMATE CHANGE