# Presentation: 670677

## C P C

- Huug van den Dool
- Climate Prediction Center
- NCEP/NWS/NOAA
- 			*CDPW **Reno October, 22, 2003*

**C P C**

## (Trends: not a straight line, LF ups and downs.)

- Trends: Diagnostics
- OR rather: How to ‘deal with trends’ in a real time forecast setting.?
- How to improve Trend forecast tools?
- How to physically explain Trends?

## Intro IWhere does 2003 stand over the US ‘trendwise’???Is it another warm year??

## Sofar,  DJF thru JAS 2003:B	N	A	at 102 US locations23	37	41%	

## Intro II: The Great Performance Measure (PM)

## The PM (blue line)Retro-active OCN (pink line)

## What is OCN? (Optimal Climate Normals). Essentially a forecast in which one persists the average of the anomalies observed in the same named season over the last K years.Example of OCN for JFM 2004:	The average anomaly for JFM over 1994-2003 (K=10; T; no space averaging)

## Slide 32

## Slide 33

## What might explain the skill of such simple forecasts?

## Table 1. Weights (X100) of the constructed analogue on global SST with data thru Feb 2001. An example.Yr(j)	Wt(αj)	Yr	Wt	Yr	Wt	Yr 	Wt	56	 5	67	-8	78	-1	89	 8	57	 2	68	-5	79	-3	90	13	58	-4	69	-3	80	-4	91	 7	59	-7	70	-5	81	-8	92	11	60	-3	71	-2	82	 1	93	-6	61	 1	72	 6	83	 0	94	 2	62	-1	73	 1	84	-1	95	 7	63	-1	74	 1	85	 3	96	 2	64	-3	75	 2	86	12	97	14	65	-8	76	 5	87	 5	98	 2	66	-5	77	 1	88	 0	99	26sum       -24          sum	 -7         sum	+4        sum          +86----------------------------------------------------------------------------------------

**CA-SST(s) = ******** α****j**** SST(s,j), where α****j****  is given as in the Table.**
**	****	****	****         ****j**
**	****	**

## Table 1. Weights (X100) of the constructed analogue on global SST with data thru Feb 2001. An example.Yr(j)	Wt(αj)	Yr	Wt	Yr	Wt	Yr 	Wt	56	 5	67	-8	78	-1	89	 8	57	 2	68	-5	79	-3	90	13	58	-4	69	-3	80	-4	91	 7	59	-7	70	-5	81	-8	92	11	60	-3	71	-2	82	 1	93	-6	61	 1	72	 6	83	 0	94	 2	62	-1	73	 1	84	-1	95	 7	63	-1	74	 1	85	 3	96	 2	64	-3	75	 2	86	12	97	14	65	-8	76	 5	87	 5	98	 2	66	-5	77	 1	88	 0	99	26sum       -24          sum	 -7         sum	+4        sum          +86----------------------------------------------------------------------------------------

**CA-SST(s) = ******** α****j**** SST(s,j), where α****j****  is given as in the Table.**
**	****	****	****         ****j**
**	****	****OCN-SST(s) = ******** α****j**** SST(s,j),  where α****j****=0 (+1/K) for older(recent) j.**
**	****	****	****             ****j**

## Trends in lower boundary conditions?: global SST

## EOFs for JAS global SST 1948-2003

## Slide 39

## Trends in lower boundary conditions?: global Soil Moisture

## Is the inter-decadal component of climate variation accurately known ???Probably not. Nature provides just one realization.

## Evidence:

- 1) 70% of skill of OCN over US can be obtained by replacing the K year average of T(s,m) by the annual mean spatial mean value, i.e. we can ignore some, if not most, of the spatial and seasonal dependence.

## 2) We can try to fight noise by :

- a) determining optimal K in EOF space ( Peitao Peng), i.e. build a smooth spatial dependence
- b) We could generate more data with a credible model

## Courtesy :  Marty Hoerling

## DJF US Nationwide (NCDC)

## JJA US Nationwide (NCDC)

## East Anglia Climate Unit

## Slide 51

## Closing comments

- LF (inter)decadal variability (“trends”) are important for seasonal forecasts, even at short leads.
- Are there any situations we can identify a-priori where trend tools should be played down ?
- Trends over the US appear related to trends in the NH, even worldwide

## Do trends have spatial patterns, and seasonality ? (probably yes)Can we extract such patterns (and seasonality) from limited observations ? (probably, barely)So either we fight noise by EOF or other spatial smoothing, ORWe generate ‘artificial’ data by running a trustworthy GCM

## Explaining trends may require understanding ‘global change’Shall we start forecasts for K year averages ??  (Regress from the global mean ?)