Continuation of Lecture 1

Corrections/Clarifications from Day 1

1. Lin-Rood scheme stability comes about because it is equivalent to a Semi-Lagrangian scheme not using implicit differencing. (Thanks to Ravi for pointing this out).

2. Definition: An anomaly is the deviation from some mean (frequently also called a climatology in meteorology/oceanography)

3. What do we mean by seasonal forecasts?a. Forecast for a three month average of field of interest (precipitation). For example, forecast for June-July-August (JJA) starting from May 1.

4. Limit of predictive skill for seasonal forecasts (Tropical Pacific SST) that I gave (order of six months) is based on skill level that is realized currently. Here a usual metric for skillful would be an anomaly correction of 0.6.Lorenz limit for weather predictability of two weeks is from perturbation experiments, i.e. how small differences in initial state grow in time. Currently realized forecast skill is (significantly) less than two weeks.

Corrections/Clarifications from Day 1 (continued)

4. Continued: You can do same error growth type of calculation for monthly means of tropical Pacific SST indices (Nino 3). This has been done with “Intermedate” coupled models such as Cane-Zebiak model. “Potential” predictability limit from those sorts of equations is 36 months. Currently realized forecast skill is less than this.

5. Based on my comment that the atmospheric initial condition memory is gone in two weeks then what use is assimilation to longer term forecasting problem?

a. Memory of the initial state in the ocean (large thermal inertia) is on the order of at least 6 months for fields of relevance to the seasonal forecasting problem (Near equatorial mass anomalies associated with equatorial Rossby and Kelvin waves). Therefore initial state specification in ocean is very important. The assimilation also serves another role in correcting the model bias, especially the structure of the thermocline.

Corrections/Clarifications from Day 1 (continued)

5b. For the decadal forecasting problem different SST anomalies (modes of variability) are important. Initializing the ocean is important to capture these modes of variability. This is an area of active research. Next round of IPCC/CMIP will include extensive set of decadal forecasts by many international groups.

6. What do skill maps look like for current CGCM SST forecasts?

Maps from CFS will be shown.

7. If you have questions in next few weeks or in the future please send me e-mail: daved@iri.columbia.edu. Please put in the e-mail Subject: TIFR Summer School.

Seasonal Forecast Skill of SST

1. Forecast skill of is dependant on the initial condition (IC) month. In the central and eastern Pacfiic the SST forecast skill undergoes quick dcline in the Northern Hemisphere Spring which is consequently known as the Spring Predictability barrier.

Examine SST forecast skill from NCEP CFS or 2 IC months: January and August. Deterministic skill score (ACC).

Define a signal to noise ratio. Signal is ensemble mean standard deviation and noise is deviation of ensemble members around ensemble mean.

CFS SST January IC (Anomaly Correlation) (W. Wang)

CFS SST January IC (Anomaly Correlation) (W. Wang)

CFS Signal to Noise Ratio for Jul IC (W. Wang)

Decadal Forecasting

1. Two modes of variability that people hope to predict:

Pacific Decadal Oscillation (PDO)

Atlantic Meridional Oveturning Circulation (AMOC)

2. What do we mean by decadal variability? Interannual variability around the decadal variability?

3. What are challenges? Prescribing ocean initial state. Difficult do to

Lack of data.

4. Decadal forecast data will be available for anyone to examine and come to your own conclusions. IPCC AR5.

More strong hurricanes

Drought

More rain over Saheland western India

Warm North Atlantic linked to …

Two important aspects:a. Decadal-multidecadal fluctuationsb. Long-term trend

Atlantic Meridional Overturning Circulation (AMOC)

North Atlantic Temperature

(Courtesy: Joe Tribbia, NCAR)

PDO refers mainly to N. Pacific sea surface temperatures (SSTs).

Climate teleconnectionssimilar to those of ENSO

Substantial interannualvariability

Several processes at work

PDV (PDO): Pacific Decadal VariabilityThe principal “mode” in the Pacific

(Source: http://jisao.washington.edu/pdo)

Precipitation Correlation

Meehl & Hu, J. Climate, 2006

POSITIVE Phase NEGATIVE Phase

But there are challenges …

• Initialization

o Many different global reanalysis products, but significant differences exist Large inherent uncertainty in driving of AMO

Atlantic Salinity Anomalies (upper 300 m)

Tropics

Mid-Lat

12m-rm seasonal anom: TRATL Averaged salinity over the top 300m

1950 1960 1970 1980 1990 2000Time

-0.2

0.0

0.2

0.4

0.6

ukdpukoicfcs2cfas2ecco50y

gfdlsodaecmfaecmfcukgs

ingvmri-eccoSIOcfasamct2

mct3eccoJPLaeccoJPLceccoMITGMAO

sdv ensm = 0.014s/n ensm = 0.246

sdv all = 0.051s/n all = 0.912

spread = 0.056

12m-rm seasonal anom: NPAC Averaged salinity over the top 300m

1950 1960 1970 1980 1990 2000Time

-0.10

-0.05

0.00

0.05

0.10

0.15

ukdpukoicfcs2cfas2ecco50y

gfdlsodaecmfaecmfcukgs

ingvmri-eccoSIOcfasamct2

mct3eccoJPLaeccoJPLceccoMITGMAO

sdv ensm = 0.010s/n ensm = 0.423

sdv all = 0.024s/n all = 1.015

spread = 0.023

Decadal Prediction

(Courtesy: Joe Tribbia, NCAR)

But there are challenges …

• Initialization

o Many different global reanalysis products, but significant differences existOcean observing not yet global or comprehensive

Tropical Upper Ocean T Anomalies (Upper 300 m)

Pacific

12m-rm seasonal anom: EQIND Averaged temperature over the top 300m

1950 1960 1970 1980 1990 2000Time

-1.5

-1.0

-0.5

0.0

0.5ukdpukoicfcs2cfas2ecco50y

gfdlsodaecmfaecmfcukgs

ingvmri-eccoSIOcfasamct2

mct3eccoJPLaeccoJPLceccoMITGMAO

sdv ensm = 0.136s/n ensm = 0.619

sdv all = 0.220s/n all = 0.998

spread = 0.220

12m-rm seasonal anom: EQPAC Averaged temperature over the top 300m

1950 1960 1970 1980 1990 2000Time

-1.0

-0.5

0.0

0.5

1.0

1.5

ukdpukoicfcs2cfas2ecco50y

gfdlsodaecmfaecmfcukgs

ingvmri-eccoSIOcfasamct2

mct3eccoJPLaeccoJPLceccoMITGMAO

sdv ensm = 0.272s/n ensm = 1.139

sdv all = 0.337s/n all = 1.411

spread = 0.239

Indian

Decadal Prediction

(Courtesy: Joe Tribbia, NCAR)

Progress with imperatives (CLIVAR JSC31)

Decadal variability and predictability• Some key questions

− To what extent is decadal variability in the oceans and atmosphere predictable? − What are the mechanisms of variability?− Does oceanic variability have atmospheric consequences?− Do we have the proper tools to realize the predictability?

Need for (coupled) data assimilation systems to initialize modelsAre models “good enough” to make skillful predictions?Adequacy of climate observing system?

Global number of temperature observations per month as a

function of depth

1980-2006

Timescales of Variability in Observations

Temperature

25%

13%

62%

Precipitation

1%

25%

74%

e.g. Climate Variability & Change in CO

What value is there in interannual signal of long-term forecast?

Suppose we have these decadal forecasts which will have interannual variability.

The value of the interannual variability part of the forecast would not be forecasting for a specific year several years in the future. Value would be in characterizing the statistics of the variability over some multi-decadal period.

1. What is the probability of JFM rain that exceed some threshold?

2. What is the probability of increases in extreme events of heat, cold, rain?

This type of information would be useful to infrastructure planning.

Will we be able to do this? No one knows…but this is the type of information governments are now asking scientists to produce.

Coordinated Decadal Prediction for AR5Basic model runs:

1.1) 10 year integrations with initial dates towards the end of 1960, 1965, 1970, 1975, 1980, 1985, 1990, 1995 and 2000 and 2005 (see below).- Ensemble size of 3, optionally increased to O(10)- Ocean initial conditions should be in some way representative of the observed anomalies or full fields for the start date.- Land, sea-ice and atmosphere initial conditions left to the discretion of each group.

1.2) Extend integrations with initial dates near the end of 1960, 1980 and 2005 to 30 yrs.- Each start date to use a 3 member ensemble, optionally increased to O(10)- Ocean initial conditions represent the observed anomalies or full fields.

Current and Historical State of the Ocean Observing System

1. SST: First satellite based products (global coverage) starting in 1982. So, only about 30 years of data. Clever people have constructed EOF (SVD) reconstruction techniques to go back earlier in time when only had ship data. How well do the two types of products compare for the common era?

2. Sub-surface:

TAO/TRITON Array in Pacific (1990-present (sometimes))*

RAMA: Indian Ocean (

PIRATA: Atlantic

ARGO:Everywhere (starting around 2000 and filling in till now)

• Why sometimes? (Wear and tear)

Buoys in “cold tongue” are good surface for phytoplankton.

Small fish eat the phytoplankton. Big fish eat the small fish.

Fisherman come to catch the big fish and do all sorts of interesting things to the buoys. None of which are good.

Consistency of Observed Data Sets

O. Alves

Why is TAO/TRITON Array Arranged that way

1. Buoys are expensive so want a minimal set that can do the job.

2. The delayed oscillator theory and importance of equatorial waves was used to establish the need for the array.

3. Ocean currents are meridionaly confined.

Equatorial Pacific Temperature AnomalyTAO GODAS

TAO climatology used

- Note the differences between GODAS

and TAO temperature are as large as 2-3C

in the eastern equatorial Pacific near the

thermocline since mid Jan 2010.

- The large departures from observations

might be related to the failure of the three

eastern most equatorial buoys

(http://tao.noaa.gov).

25

26http://tao.noaa.gov/tao/status/

TAO/TRITON Observing Status in July

- TAO moorings had massive failures at 95W and 110W near the equator.

27

Equatorial Pacific Temperature

TAO Temp Anom GODAS-TAO - Equatorial temperature

decreased at the surface

and near the thermocline,

probably forced by easterly

wind anomalies in the

central-eastern Pacific

(slide 14).

- Temperature differences

between GODAS and TAO,

GODAS and Coriolis, were

above 1C near the

thermocline east of 135W.

- Those positive biases

were consistent with warm

biases in the control

simulation in which

observations were not

included.

- Therefore, TAO mooring

data at 95W and 110W

played critical roles in

constraining model biases.

GODAS-Coriolis

http://tao.noaa.gov/tao/status/http://www.coriolis.eu.org/cdc

28

The Future is Much Brighter for Ocean Observations

ARGO array: Automatic buoys that dive to 1000 meters then

back to surface. Periodically at the surface they transmit

their data to satellite.

Nearly global coverage of the worlds oceans.

Measure temperature and salinity.

Multi-national: Many countries bought buoys.

A. Weigel

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

The Centre for Australian Weather and Climate Research A partnership between CSIRO and the Bureau of Meteorology

Indian OceanIndian Ocean

Temperature Salinity

Pre- Argo

Argo

O. Alves

Example of ODA Estimates of Observed State

In data sparse regions (everywhere outside the Tropical Pacific) and for fields that are not observed solutions from ODA products can show substantial variability. This makes their use as a verification product for models more ambiguous.

Different Realities Simulated by ODA Systems

• Comparison of 20 year climatology of January surface zonal current from 3 state of the art ODA systems: Rather large differences especially near the equator.

Coupled Model Bias (Systematic Error)

1. SST errors are frequently same order of magnitude as the

Seasonal variability (standard deviation).

2. SST errors are similar across models.

3. Equatorial SST variability has wrong spatial structure and amplitude.

4. Precipitation structure becomes distorted and too symmetric about the equator: “Double ITCZ Syndrome”

Use of Multi-Model Ensembles (MME) in Seasonal Forecasting

1. Consists of different system each with its own ensemble members.

2. Has been found that taking more than one forecast system and combining with other systems produces more skillful forecasts.

3. Has also been shown (fewer comparisons) that using the same number of ensemble members from the best model as for the MME still has MME with higher skill scores.

4. Most MME systems are equal weight, i.e. no difference in weights for good versus bad models. Reason for this is that with 30 years of data and cross validated weights it is hard to beat equal weights.

1-tier 2-tier

RPSS: temp

1-tier 2-tier

RPSS: pcp

MME SST is Generally Better Than Any Model

ECHAM4.5-GMLcfsSST

ECHAM4.5-MOM3 (DC2)NCEP CFS

ECHAM4.5-caSST

Intercomparison of GCM precipitation seasonal forecast skillAnomaly correlation

ECHAM4.5-EC3_SST

ECHAM4.5-MOM3 (AC1)JAMSTEC SINTEX-F

June–Sept Seasonal totalfrom May 1 (1982–08)

Seasonal Forecasting: Marginal Skill Problem

Show skill scores for actual forecasts over the last 11 years from IRI. 2-tiered (uncoupled system). Order of 7 models most with

about 10 ensemble members. Forecasts are terciles.

Skill metrics used:

1. Ranked Probability Skill Score (RPSS)

2. Liklihood score.

3. Generalized Relative Operating Characteristics (ROC)

Verificationmeasurecomparison:

All-seasontemperature,0.5-monthlead time

Verificationmeasurecomparison:

All-seasonprecipitation,0.5-monthlead time

Use of Forecast System Including ODA to evaluate importance of observation networks

Observing System Simulation Experiments (OSSE):

Run ODA with full data stream and then cases removing select data types.

Run forecasts from each of these cases and examine difference in forecasts, especially skill.

Impact of Ocean Observations

O. Alves