EMPIRICAL PREDICTION OF THE GLOBAL TEMPERATURE ANOMALY FOR 2002

contributed by CHRIS Chris FOLLAND Folland & ANDREW Andrew COLMANColman

ISSUED TO UK DEPARTMENT OF THE ENVIRONMENT, TRANSPORT AND THE REGIONS, DECEMBER 134^th 2001. IN A MORE GENERAL FORM IN A PRESS RELEASE, DECEMBER 18^th 2001.

1. INTRODUCTION

Global temperature is an important indicator of global climate, and has been at or near record levels in recent years. Analysis of observed and model data has linked interannual to decadal fluctuations in global mean temperature to various natural phenomena including ENSO, volcanic activity and solar flux variability. Global temperature change has also been linked to human activity including changing greenhouse gas and aerosol concentrations and stratospheric ozone depletion and tropospheric ozone increases. The existence of these numerous forcings raises the possibility of skilful predictions of global temperature. In this study, indices of the known important climate forcings and influencing phenomena are used to make empirical predictions of the global temperature anomaly from a 1961-90 average. Based on a multiple regression analysis, the state of ENSO is the most important predictor on the interannual time scale. On the multi-decadal time scale the net radiative forcing of the atmosphere is most important.

We use threetwo forms of multiple linear regression to make these forecasts (a) using predictors based on physical understanding which are forced into the regression and (b) a modified version where one of the predictors is a forecast of SST anomaly in the Nino3.4 region of the Tropical Pacific. The latter was chosen to be the forecast made by the US National Center for Environmental Prediction (NCEP) coupled ocean-atmosphere global circulation model. This model was chosen as it is possible theto assess the intrinsic skill of the method from published hindcasts and previous forecasts.

An orthogonalised version of method (a) gives almost identical results to method (a) itself, so has been dropped this year but is termed method 1a whenin the assessment ofing the 2001 forecast in section 2.2.

1.1 PREDICTORS

The six predictors listed below have been identified by more than one author to be related to large-scale temperature:

a) AMD: Atlantic MultiDecadal mode. This is an index of low frequency (low pass filtered <13 years) mean North Atlantic SST with the global change component (represented by the first EOF of Low pass filtered (>13 years) global SST in Folland et al, 1999) removed, similar to that described by Enfield, and Mestas-Nunez and Trimble (2001). It is considered to be scientifically a better index of thermohaline circulation-based effects on global temperature than the Inter-Hemispheric Contrast (IHC) index it replaces. This decision is based on recent unpublished work using a long run of the Hadley Centre’s coupled climate model HadCM3. However the two indices are highly correlated.The index is filtered as this the time scale of influences is likely to be decadal. The index is currently not filtered; further research is needed to determine whether smoothing should be done.

b) ENSO HF1: The High Frequency El Nino Southern Oscillation iIndex 1 (ENSO HF 1). This is the time series of the first covariance eigenvector of high frequency (<13 years) global SSTA for 1911-95 in Folland et al (1999). This eigenvector pattern is related strongly to ENSO.

c) ENSO HF2: The High Frequency El Nino Southern Oscillation iIndex 2 (ENSO HF 2). This is the time series of the second covariance eigenvector of high frequency (<13 years) global SST. This eigenvector pattern is also ENSO-related, but the time series is typically 6-9 months out of phase with HF ENSO 1. This pattern is also from Folland et al (1999).

d) VOLCANO: An index of global volcanic dust cover (VOLCANO) produced by Sato et al (1993). Dust veils from major volcanic eruptions, particularly in the tropics,; lead to a significant drop in global temperature for a year or two after the eruption.

e) SOLAR: An index of solar irradiance (SOLAR) as supplied by Lean (Frohlich & Lean, 1998) and extrapolated to the present.

f) GSO: An estimate of the global mean anthropogenic net radiative forcing at the tropopause. This comes from changing concentrations of well-mixed anthropogenic greenhouse gases, the direct and indirect effects of sulphate aerosol emissions and from stratospheric and tropospheric ozone concentration changes (GSO). This index was calculated using the Hadley Centre’s current Coupled Ocean-Atmosphere general circulation model, HADCM3. It is expressed as the annual mean forcing at the top of the troposphere in Wm^-2 (Johns, personal communication).

g) NCEP NINO 4: In method (b) , predictions of the average Nino3.4 area (170-120^oW, 5^oN-5^oS) SST anomaly made by the NCEP coupled ocean-atmosphere global circulation model (NCEP NINO3.4) in the first six months of the predicted year are used. This replaces both the ENSO HF1 and ENSO HF2 indices.

The North Atlantic Oscillation (NAO) does not contribute significantly in the regression method mostly probably because there are large areas of negative as well as positive anomalies. Future work will determine if the Arctic Oscillation has any significant effects because there is a distinct difference in relative index values from the NAO some years. Similarly the Interdecadal Pacific Oscillation or the very similar Pacific Decadal Oscillation does not contribute because it is highly correlated with ENSO. Future work will investigate this in more detail as small residual effects on global temperature may exist.

We chose 1947-2000 as our training period because the predictor and predictand data are best at that time. Soon, advances in data sets might allow this period to be substantially extended. In the cross validation skill testing method, we allow for serial correlation on the interannual time scale as described below. The multiple regression equations include December data of the prior year

Predictor data for the following periods are used. The examples are for the 2002 prediction.

AMD January-December 2001

ENSO HF1 October-December 2001

ENSO HF2 October-December 2001

VOLCANO December 2001 (extrapolated from data ending in 1997 assuming no significant recent activity)

SOLAR January-December 2001 (Extrapolated from data up to 1998 by Lean allowing for the solar cycle

(pers. comm.)

GSO January-December 2001

NCEP NINO3.4 January-June 2002 (used in place of ENSO HF1 )

Note: December 2001 SSTA are based only on observations from the two pentads covering 2^nd-11^th December. In real-time forecasts, this data is persisted for the whole month. In hindcast tests, the full December SST data is used.

The predictor periodsperiod’s chosen were selected to extract maximum available skill from data available at the time of the forecast. Updating to December is only influential for SSTA based predictors. So far, no investigation of the optimum lags have been made concerning the radiative forcing predictors, which are based on the previous year but such effects are likely to be small.

1.2 PREDICTAND

The predictand is mean global land surface air and sea surface temperature anomalies relative to 1961-90 for the forthcoming year. This is chosen to be the IPCC Third Assessment Report optimally averaged series produced by the Hadley Centre and the Climatic Research Unit (Folland et al, 2001). This represents a change from the forecast for 2000. That forecast used the non-optimally averaged IPCC (1996) data based on Jones (1994) and Parker et al (1995). The IPCC (1996) method was based on the weighted averaging of available 5^o latitude x5^o longitude areas. In principle this had the problem that the weighting of the land relative to the ocean tended to be slightly too small because of a greater fraction of data gaps over land due to non receipt of data or lack of stations. Antarctica was particularly under-weighted. Optimum averaging objectively allows for data gaps as well as observational uncertainties. The changes in annual global mean temperature anomalies for recent years are, however, small.

1.3 FORECAST METHOD

ThreewoWO forecasts are made using multiple linear regression (METHODS 1 and- 23). A global temperature anomaly forecast is produced by applying each regression equation to the predictorthe predictor indices described above. All regression equations use historical data for 1947-2000. The threewo regression equations are:

1. An equation using predictors a-f of section 1.1, calculated using data for 1947-2000.

2. A modified method using NCEP couple model forecasts of the NINO3.4 SSTA index for January-June of 2002 instead of observations of HF ENSO EOF 1 for late 2001. The NCEP forecast is corrected for bias compared to observations estimated from 19 model hindcasts from 1982-2000.

The forecast from each model uses "inflated" linear regression to retain the same forecast as observed variance. However because of the high hindcast correlation skill of these methods, the level of inflation is small. The Forecast Probability Distribution Function (FPDF) for each method is based on the assessed standard errors of the regression predictions, assuming the forecast errors are normally distributed.

1.4 ASSESSMENT METHODS

To estimate forecast skill, trial forecasts (hindcasts) were made using the jack-knife method in a fairly severe way. Jack-knife forecasts were made for every year in the data period used to create the forecast equations using equations calculated using the majority of the remaining years in that period. Thus the coefficients of the predictors change from year to year but the predictors do not. The forecast year is always excluded from the regression equation, along with data for the 5 years before and 5 years after the forecast year. During the first and last five years of the data period only a one sided exclusion of data is possible. This process minimises artificial hindcast skill due to persistence.

In real time forecasts, we only have an estimate of December SST up to about December 11^th . Two measures of forecast skill are used:

(a) Correlation: Standard (Pearson) Correlation. This ignores biases between forecast and observed values and the difference in standard deviation between the forecast and the observed value. We use a total correlation score (Correlation) and a high frequency correlation (HF correlation). The latter calculates correlations on time scales less than about 10 years.

(b) RMS (Root Mean Square error): RMS scores are very appropriate as the forecast standard deviation is equal to that observed.

We intend to introduce other skill measures in future.

2. PERFORMANCE OF HINDCASTS AND FORECASTS OF OPTIMALLY AVERAGED GLOBAL TEMPERATURE ESTIMATES

2.1 JACKKNIFE HINDCAST SKILL 1947-2000

Jack-knife multiple regression forecasts are plotted against observed global temperatures in figure 1 for METHOD 1. The jack-knife correlation of 0.93 is very high for a climate prediction scheme. Because an important aim of the forecasts is to indicate how next year will differ from this year, the high frequency correlation of 0.74 gives a more realistic estimate of skill on this time scale. Nevertheless the excellent reconstruction of the low frequency would only be possible if the shape of the low frequency forcing had been captured well. So our technique to some extent corroborates estimates of the time-dependent shape of the total net radiative forcing that we use. However, as long as the shape of such forcing is well captured, our linear method would not be sensitive to the overall magnitude of radiative forcing change. In Figure 1, 450% and 95% confidence intervals are plotted in green and the best estimate forecasts in red.

Table 1 shows the contributions of the different predictors in METHOD1. The regression equation is built up in a stepwise manner, with predictors incorporated in order using the results of an F test. The importance of each predictor is shown by the standardised regression coefficient. This is the coefficient estimated when both predictor and predictand index are standardised. Choice of the predictors themselves is judged on physical grounds, not on the F test. Bold numbers show the skill of the complete regression equation.

TABLE 1 PERFORMANCE OF JACKKNIFE HINDCASTS 1947-2000 ADDING 1 PREDICTOR AT A TIME,

USING SIX PREDICTORS (METHOD 1)

Predictor added	Standardised Coefficient (1947-2000)	Correlation 1947-2000	HF Corr. 1947-2000	RMS 1947-00 ^oC	RMS Standard. Units
GSO	0.82	0.82	0.20	0.110	0.631
ENSO HF 1	0.39	0.85	0.60	0.096	0.551
VOLCANO	-0.24	0.89	0.69	0.083	0.475
SOLAR	0.14	0.89	0.69	0.079	0.455
AMD	0.16	0.92	0.68	0.067	0.387
ENSO HF 2	-0.11	0.93	0.73	0.066	0.382

The strongest predictor (over 1947-2000), the GSO index , predicts the warming trend this century and the accelerated warming over the past 30 years but does not predict variability on time scales less than 20 years. The second predictor, ENSO HF 1, contributes most to interannual skill. The third predictor, VOLCANO, is important only during the 2 or 3 years following a major eruption. It is negligible in the 2002 forecast.

TABLE 2 SUMMARY PERFORMANCE OF JACKKNIFE FORECASTS USING ORTHOGONAL PREDICTORS AND USING NCEP NINO3.4 SST FORECASTS AS A PREDICTOR.

Predictors	Assessment Period	Correlation	HF Corr.	RMS ^oC	RMS S. U.
Method 1	1947-2000	0.93	0.74	0.064	0.365
Method 2 (ENSO represented by NCEP NINO 3.4)	1982-2000	0.82	0.67	0.080	0.461

The NCEP Nino3.4 forecasts have slightly less skill than method 1. However, the latter assessments are substantially less reliable due to the short period of testing.

2.2 ASSESSMENT OF FORECAST FOR 2001 (Folland & Colman, 2000)

The forecasts for 2001 were expressed as the boundaries corresponding to the following values of the cumulative probability of the forecast, starting at the coldest level:

TABLE 3 ASSESSMENT OF FORECASTS FOR 2001

	2.5%	30%	50%	70%	97.5%
Method 1	0.39	0.48	0.51	0.54	0.63
Method 1a*	0.38	0.48	0.51	0.54	0.64
Method 2	0.28	0.40	0.44	0.48	0.60
Weighted Mean	0.33	0.43	0.47	0.51	0.61

The predictors for method 1a were orthogonalised versions of those used for method 1 which have been discontinued for the 2002 forecast as the results are so similar.

TThe mean forecast anomaly of 0.47^oC was based on a skill- weighted average of the three methods. The observed anomaly to October 2001 inclusive is 0.44^oC. Partial later data and a projection to the end of 2001 using 500hPa height forecasts suggests the final global anomaly may be 0.42^o and a a projecC.tion to th, s So the 2001 forecast mayis likely to be be rathera little too warm but was it within the 450% confidence range; and hence it can be considered considered to be an moderately ACCURATEaccurate forecast. If the value2001 global temperature anomaly is 0.42^oC,At present, we arewere just correct in predicting 2001 to be the 2nd warmest year on record . Note the accuracy of the forecast depended on the collapse of the 1998-2001 La Nina, which happened, but the expected weak to moderate La NinaEl Nino has notdid not developed. (to mid December 2001).

3. FORECAST FOR 2002

The best estimate forecasts of global temperature anomaly made by the two methods were:

1. USING SIX EMPIRICAL PREDICTORS INCLUDING OBSERVED ENSO INDEX, OCT-DEC 2000 0.44 ^oC

2. AS 1 BUT USING NCEP NINO3.4 SST FORECAST FOR JANUARY-JUNE 2001 0.52 ^oC

The associated probability forecasts are expressed as the boundaries corresponding to the following values of the cumulative probability of the forecast, starting at the coldest level. The mean and standard deviation is calculated by weighting the forecasts according to their intrinsic skill as measured by total variance explained in the cross validated tests. This year we now show the 50% (25-75) confidence interval rather than the 40% (30-70) interval. Weighting is proportional to the hindcast correlation skill.

TABLE 4 FORECAST FOR 2002

	2.5%	25%	50%	75%	97.5%
Method 1	0.31	0.39	0.44	0.48	0.57
Method 2	0.36	0.47	0.52	0.57	0.68
Weighted Mean	0.33	0.42	0.47	0.52	0.61

Our best estimate forecast of the global temperature anomaly for 2002 is 0.47+-0.14 ^oC, with a 95% confidence range from 0.33 ^oC to 0.61 ^oC. The best estimate forecast would probably be the (new) second warmest year in the record. There is about a 6750% probability that 2002 will be warmer than the provsionalprovisional 2001 anomaly of 0.442^oC. There is only about a 10% probability that 2002 will be as warm or warmer than the warmest year, 1998 (0.57^oC in the Folland et al, 2001, data set).

Warning: the accuracy of this forecast may depend on the development of a weak to moderate El Nino in 2002. At the time this forecast was issued, the mean of central and eastern tropical Pacific SST together was close to average.

Acknowledgements:

Thanks to Dr Judith Lean, Nick Rayner and Dr Jeff Knight for providing data for this forecast

References:

Enfield, D.B., Mestas-Nunez, A.M. and P.J. Trimble, 2001: The Atlantic multidecadal oscillation and its relation to rainfall and river flows in the continental U.S.. Geophys. Res. Lett., 28, 2077-2080.,

Folland, C.K & Colman, A.W. 2001. Empirical Prediction of the global temperature anomaly for 2001. Experimental Long Lead Forecast Bulletin 9 4 published by COLA, USA, www.iges.org/ellfb

Folland, C.K & Colman, A.W. 2000. Empirical Prediction of the global temperature anomaly for 2000. Experimental Long Lead Forecast Bulletin 9 1 published by COLA, USA, www.iges.org/ellfb

Folland, C.K., Parker D.E., Colman AW. and R. Washington, 1999: Large scale modes of ocean surface temperature since the late nineteenth century. Chapter 4, pp73-102 of Beyond El Nino: Decadal and Interdecadal Climate Variability. Ed: A. Navarra. Springer-Verlag, Berlin, pp374.

Folland, C.K, N.A. Rayner, S.J. Brown, T.M. Smith, S.S. Shen, D.E. Parker, I. Macadam, P.D. Jones, R.N. Jones, N. Nicholls and D.M.H. Sexton, 2001: Global temperature change and its major uncertainties since 1861. Geophys. Res. Lett., 28, 2621-2624.

Frohlich, C. and Lean, J. 1998: The sun's total irradiance: cycles, trends and related climate change uncertainties since 1976. Geophys. Res. Lett., 25, 4377-4380.

Jones, P.D., 1994: Hemispheric surface air temperature variations: a reanalysis and an update to 1993, J. Climate, 7:1794-1802.

Parker, D.E., C.K. Folland, and M. Jackson, 1995: Marine surface temperature: observed variations and data requirements, Climatic Change, 31:559-600.

Sato, M., J.E. Hansen, M.P. McCormick and J.B. Pollack, 1993: Stratospheric aerosol optical depths, 1850-1990. J. Geophys. Res., 98, 22987-22994.

Figure 1: Jack-knife inflated regression hindcasts of optimally averaged global mean temperature anomalies for 1947-2000 (red) and forecasts for 2001 and 2002 (blue) using method 1. The black line represents observations based on optimal averages. The 50% and 95% confidence intervals are marked by lines in green.