How Does NCEP/CPC Make Operational Monthly and Seasonal Forecasts?

1. How Does NCEP/CPC Make Operational Monthly and Seasonal Forecasts? Huug van den Dool (CPC) CPC, June 23, 2011/ Oct 2011/ Feb 15, 2012 / UoMDMay,2,2012/ Aug2012/ Dec,12,2012/UoMDApril24,2013/ May22,2013,/Nov20,2013/April,23,2014/

2. Assorted Underlying Issues • Which tools are used… • How do these tools work? • How are tools combined??? • Dynamical vs Empirical Tools • Skill of tools and OFFICIAL • How easily can a new tool be included? • US, yes, but occasional global perspective • Physical attributions

3. Menu of CPC predictions: 6-10 day (daily) Week 2 (daily) Monthly (monthly + update) Seasonal (monthly) Other (hazards, drought monitor, drought outlook, MJO, UV-index, degree days, POE, SST) (some are ‘briefings’) Operational forecasts (‘OFFICIAL’) and informal forecast tools (too many to list) http://www.cpc.ncep.noaa.gov/products/predictions/90day/tools/briefing/index.pri.html

4. EXAMPLE P U B L I C L Y I S S U E D “ O F F I C I A L ” F O R E C A S T

7. From an internal CPC Briefing package

8. EMP EMP EMP EMP N/A DYN CON CON EMP DYN

9. SMLR CCA OCN LAN OLD-OTLK CFSV1 LFQ ECP IRI ECA CON 9 (15 CASES: 1950, 54, 55, 56, 64, 68, 71, 74, 75, 76, 85, 89, 99, 00, 08)

10. Element  US-T US-P SST US-soil moisture Method:CCA X X X OCN X X CFS X X X XSMLR X XECCA X XConsolidation X X X Constr Analog X X X XMarkov X ENSO Composite X X Other (GCM) models (IRI, ECHAM, NCAR,  N(I)MME): X X CCA = Canonical Correlation AnalysisOCN = Optimal Climate NormalsCFS = Climate Forecast System (Coupled Ocean-Atmosphere Model)SMLR = Stepwise Multiple Linear RegressionCON = Consolidation

14. About OCN. Two contrasting views:- Climate = average weather in the past- Climate is the ‘expectation’ of the future30 year WMO normals: 1961-1990; 1971-2000; 1981-2010 etcOCN = Optimal Climate Normals: Last K year average. All seasons/locations pooled: K=10 is optimal (for US T).Forecast for Jan 2015 (K=10) = (Jan05+Jan06+... Jan14)/10. – WMO-normalplus a skill evaluation for some 50+ years.Why does OCN work?1) climate is not constant (K would be infinity for constant climate)2) recent averages are better3) somewhat shorter averages are better (for T)see Huang et al 1996. J.Climate. 9, 809-817.

15. OCN has become the bearer of most of the skill, see also EOCN method (Peng et al), or other alternatives of projecting normals forward.

17. G H C N - C A M S F A N 2 0 0 8 • huug.vandendool@noaa.gov

18. huug.vandendool@noaa.gov Preview of 2010s, 4 years only

19. NCEP’s Climate Forecast System, now called CFS v2 • MRFb9x, CMP12/14, 1995 onward (Leetmaa, Ji etc). Tropical Pacific only. • SFM 2000 onward (Kanamitsu et al • CFSv1, Aug 2004, Saha et al 2006. Almost global ocean • CFSR, Saha et al 2010 • CFSv2, March 2011. Global ocean, interactive sea-ice, increases in CO2. Saha et al 2014.

20. NCEP’s Climate Forecast System, now called CFS v2 <-- Out of date diagram. Still instructive

23. Major Verification Issues ‘a-priori’ verification (used to be rare) After the fact (fairly normal and traditional)

24. After the fact….. Source Peitao Peng

25. (Seasonal) Forecasts are useless unless accompanied by a reliable a-priori skill estimate.Solution: develop a 50+ year track record for each tool. 1950-present.(Admittedly we need 5000 years)

26. Consolidation

27. --------- OUT TO 1.5 YEARS -------

28. OFFicial Forecast(element, lead, location, initial month) = a * A + b * B + c * C +…Honest hindcast required 1950-present. Covariance (A,B), (A,C), (B,C), and(A, obs), (B, obs), (C, obs) allows solution for a, b, c (element, lead, location, initial month)

29. CFS v1 skill 1982-2003

30. Fig.7.6: The skill (ACX100) of forecasting NINO34 SST by the CA method for the period 1956-2005. The plot has the target season in the horizontal and the lead in the vertical. Example: NINO34 in rolling seasons 2 and 3 (JFM and FMA) are predicted slightly better than 0.7 at lead 8 months. An 8 month lead JFM forecast is made at the end of April of the previous year. A 1-2-1 smoothing was applied in the vertical to reduce noise. CA skill 1956-2005

31. M. Peña Mendez and H. van den Dool, 2008: Consolidation of Multi-Method Forecasts at CPC. J. Climate, 21, 6521–6538. Unger, D., H. van den Dool, E. O’Lenic and D. Collins, 2009: Ensemble Regression. Monthly Weather Review, 137, 2365-2379. (1) CTB, (2) why do we need ‘consolidation’?

33. (Delsole 2007)

34. SEC SEC and CV 3CVRE

42. See also: O’Lenic, E.A., D.A. Unger, M.S. Halpert, and K.S. Pelman, 2008: Developments in Operational Long-Range Prediction at CPC.Wea. Forecasting, 23, 496–515.

43. Empirical tools can be comprehensive! (Thanks to reanalysis, among other things). And very economical.Constructed Analogue(next 2 slides)

44. Given an Initial Condition, SSTIC (s, t0) at time t0 . We express SSTIC (s, t0) as a linear combination of all fields in the historical library, i.e. 2012 or 2013 • SSTIC (s, t0) ~= SSTCA(s) = Σ α(t) SST(s,t) (1) t=1956 or 1957 (CA=constructed Analogue) • The determination of the weights α(t) is non-trivial, but except for some pathological cases, a set of (57) weights α(t) can always be found so as to satisfy the left hand side of (1), for any SSTIC , to within a tolerance ε.

45. Equation (1) is purely diagnostic. We now submit that given the initial condition we can make a forecast with some skill by 2012 or 2013 • XF (s, t0+Δt) = Σ α(t) X(s, t +Δt) (2) t=1956 or 1957 Where X is any variable (soil moisture, temperature, precipitation) • The calculation for (2) is trivial, the underlying assumptions are not. We ‘persist’ the weights α(t) resulting from (1) and linearly combine the X(s,t+Δt) so as to arrive at a forecast to which XIC (s, t0) will evolve over Δt.

46. CA-weights in March 2014

48. Z500 SST CA T2m Precip

49. SST Z500 CFS T2m Precip Source: Wanqiu Wang

50. Physical attributions of Forecast Skill • Global SST, mainly ENSO. Tele-connections needed. • Trends, mainly (??) global change • Distribution of soil moisture anomalies