TRENDS REVISITED Huug van den Dool Climate Prediction Center NCEP/NWS/NOAA CDPW Reno October, 22, 2003 CPCCPC.

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Presentation transcript:

TRENDS REVISITED Huug van den Dool Climate Prediction Center NCEP/NWS/NOAA CDPW Reno October, 22, 2003 CPCCPC

(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: BNAat 102 US locations %

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 (K=10; T; no space averaging)

What might explain the skill of such simple forecasts?

Table 1. Weights (X100) of the constructed analogue on global SST with data thru Feb An example. Yr(j)Wt( α j )YrWtYrWtYr Wt sum -24 sum -7 sum+4 sum CA-SST(s) = 3 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 An example. Yr(j)Wt( α j )YrWtYrWtYr Wt sum -24 sum -7 sum+4 sum CA-SST(s) = 3 α j SST(s,j), where α j is given as in the Table. j OCN-SST(s) = 3 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

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

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 ?)