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/

1

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 2

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/9 0day/tools/briefing/index.pri.html

3

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

5

6

From an internal CPC Briefing package 7

EMP EMP EMP EMP N/A DYN DYN CON EMP CON

8

SMLR CCA OCN LAN OLD-OTLK LFQ CFSV1 ECP IRI ECA CON (15 CASES:

1950, 54, 55, 56, 64, 68, 71, 74, 75, 76, 85, 89, 99, 00, 08

)

Method: CCA OCN CFS SMLR ECCA Element  US-T Consolidation X X X X X X X US-P X X X X X SST X X X US-soil moisture X Constr Analog Markov X X X X X ENSO Composite X X Other (GCM) models (IRI, ECHAM, NCAR,  X X N(I)MME ):

CCA = Canonical Correlation Analysis OCN = Optimal Climate Normals CFS = Climate Forecast System (Coupled Ocean-Atmosphere Model) SMLR = Stepwise Multiple Linear Regression CON = Consolidation

10

Long Lead Predictions of US Surface Temperature using

Canonical Correlation Analysis

. Barnston(J.Climate, 1994, 1513) Predictor - Predictand Configuration Predictors Predictand * Near-global SSTA * N.H. 700mb Z * US sfc T * US sfc T four predictor “stacked” fields 4X652=2608 predictors one predictand period 102 locations Data Period 1955 - last month 11

12

13

About

OCN

. Two contrasting views: - Climate = average weather in the past Climate is the ‘expectation’ of the future 30 year WMO normals: 1961-1990; 1971-2000; 1981-2010 etc OCN = 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-normal plus 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 better 3) somewhat shorter averages are better (for T)  see Huang et al 1996. J.Climate. 9, 809-817.

14

OCN has become the bearer of most of the skill,

see also EOCN method (Peng et al), or other alternatives of projecting normals forward.

15

16

17 G H C N C A M S 2 0 0 8 F A N

Preview of 2010s, 4 years only 18

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.

19

NCEP’s Climate Forecast System, now called CFS v2

<- Out of date diagram.

Still instructive 20

21

22

Major Verification Issues

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

After the fact…..

Source Peitao Peng 24

(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) 25

Consolidation

26

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

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) 28

CFS v1 skill 1982-2003

29

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

CA skill 1956-2005

reduce noise.

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’?

31

32

(Delsole 2007) 33

SEC SEC and CV

3CVRE

34

35

36

37

38

39

40

41

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. 42

Empirical tools can be comprehensive! (Thanks to reanalysis, among other things).

And very economical.

Constructed Analogue

(next 2 slides) 43

• Given an Initial Condition, SST IC (s, t 0 ) at time t 0 We express SST IC (s, t 0 ) as a linear combination of all fields in the historical library, i.e.

. • SST IC 2012 or 2013 (s, t 0 ) ~= SST CA (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 SST IC within a tolerance ε. , to

• Equation (1) is purely diagnostic. We now submit that given the initial condition we can make a forecast with some skill by • X F 2012 or 2013 (s, t 0 +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 X IC (s, t 0 ) will evolve over Δt.

Year 1956 1957 1958 1959 1960 1961 1962 1963 1964 1965 Wgt -5 12 3 13 -7 -2 5 5 -8 -9 Year 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 Wgt -10 0 1 -6 -4 2 4 10 6 -2 Year 1976 1977 1978 1979 1980 1981 1982 1983 1984 1985 Wgt -4 0 -3 -3 8 -7 -12 -7 3 2 Year 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 Wgt 5 -9 -10 0 5 14 -3 -4 -7 -1 Year 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 Wgt 2 9 -11 -2 -17 3 -2 20 -1 7 Year 2006 2007 2008 2009 2010 2011 2012 2013 2014 Weigt 2 2 2 11 6 -1 12 7 NA Xx CA-weights in March 2014

47

SST CA Precip Z500 T2m 48

SST CFS Source: Wanqiu Wang Precip Z500 T2m 49

Physical attributions of Forecast Skill

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

Website for display of NMME&IMME

NMME=National Multi-Model Ensemble IMME=International Multi-Model Ensemble • http://origin.cpc.ncep.noaa.gov/products/N MME/

Please attend

• Friday 2pm June 14 • Tuesday 1:30pm June 18 Two meetings to Discuss the Seasonal Forecast.

52