Exploring the Possibility to Forecast Annual Mean Temperature with IPCC and AMIP Runs

Exploring the Possibility to Forecast Annual Exploring the Possibility to Forecast Annual Mean Temperature with Mean Temperature with

IPCC and AMIP RunsIPCC and AMIP Runs

Peitao PengPeitao PengArun KumarArun Kumar

CPC/NCEP/NWS/NOAACPC/NCEP/NWS/NOAA

Acknowledgements: Bhaskar Jha and Yun Fan

Background: Warming TemperaturesBackground: Warming Temperatures

Annual Mean/Global Mean Land Temperature (Hoerling et al, 2008)

Background: Warming TemperaturesBackground: Warming Temperatures

- GISS

- GHCN (NCDC)

- Reynolds merged land/sea

- Reanalysis

- CRU

Hoerling et al., 2008: Reanalysis of Historical Climate Data for Key Atmospheric Features: Implications for Attribution of Causes of Observed Change. CCSP SAP1.3

Background: Warming TemperaturesBackground: Warming Temperatures

Background: How is Warming Trend Affecting S/IBackground: How is Warming Trend Affecting S/I Predictions?Predictions?

AA NN BB totaltotal

AA 29.729.7 17.017.0 11.711.7 58.458.4

NN 7.87.8 5.55.5 4.24.2 17.517.5

BB 10.810.8 7.57.5 5.85.8 24.124.1

totaltotal 48.348.3 30.030.0 21.721.7 100100

OBS

Fo

reca

st

Contingency Table for a performance evaluation of CPC 1-month lead seasonal temp forecast ; 168 seasons over 1995-2008 period and 232 grid points over CONUS give 38976 cases.

About 50% are above normal in OBS, 60% of them are correctly forecastedAbout 70% skill are from warm cases.

Background: How is Warming Trend Affecting S/I Predictions?Background: How is Warming Trend Affecting S/I Predictions?

Cai et al. 2009: The role of long-term trend in seasonal predictions: Implications of global warming in the NCEP CFS. Weather and Forecasting

Cold Bias due to the underestimated GHG

ObjectivesObjectives

Use available model simulations to understand the Use available model simulations to understand the predictability and prediction skill of land predictability and prediction skill of land temperatures;temperatures;

Examine the predictability and prediction skill of Examine the predictability and prediction skill of trends in land temperature;trends in land temperature;

Procedures of the StudyProcedures of the Study

1.1. Examine the performance of IPCC and AMIP data in Examine the performance of IPCC and AMIP data in simulating land temperature variations, in particular the simulating land temperature variations, in particular the warming trend;warming trend;

2.2. Make empirical hindcasts with IPCC and AMIP data;Make empirical hindcasts with IPCC and AMIP data;3.3. Compare the model data based empirical hindcasts with Compare the model data based empirical hindcasts with

that based on analysis data.that based on analysis data.

Data SetsData Sets

– AR4 CMIP3AR4 CMIP3 Climate of 20Climate of 20thth Century (with the observed evolution Century (with the observed evolution

of external forcing; 1880-1999)of external forcing; 1880-1999) Special Emission Scenario (SRES) A1B (2000-2008)Special Emission Scenario (SRES) A1B (2000-2008) 48 simulations from 22 different models48 simulations from 22 different models

– AMIP AMIP Forced with SSTsForced with SSTs Ensemble of simulations Ensemble of simulations Three AGCMs (1950-2008)Three AGCMs (1950-2008)

– OBS OBS CPC Merged GHCN-CAMS analysis (Fan and Huug) CPC Merged GHCN-CAMS analysis (Fan and Huug)

(1949-current)(1949-current)

Data of their common period (1950-2008) are used for our analysisData of their common period (1950-2008) are used for our analysis

Empirical Methods in Hindcast:Empirical Methods in Hindcast: Persistent:Persistent: one year persistent extension of bias corrected one year persistent extension of bias corrected

model data;model data;

Sloped ExtensionSloped Extension:: make a linear regression over the make a linear regression over the latest n year model data, then do a sloped extension to the latest n year model data, then do a sloped extension to the target year.target year.

Flat Extension (OCN)Flat Extension (OCN):: take the latest n (n>1) year take the latest n (n>1) year average as the forecast for the target yearaverage as the forecast for the target year

Note: Note: 1.1. Flat extension is only applied to analysis data and the result will be used as a bench mark to Flat extension is only applied to analysis data and the result will be used as a bench mark to

evaluate other methods applied to model data.evaluate other methods applied to model data.

2.2. Optimal n corresponds to the highest forecast skill over the verification period 1970-2008Optimal n corresponds to the highest forecast skill over the verification period 1970-2008

Annual and Global Mean Land Temperatures: OBS vs ModelsAnnual and Global Mean Land Temperatures: OBS vs Models

Total Quantities

Anomalies w.r.t. 50-69 climate

00.050.1

0.150.2

0.250.3

0.350.4

0.450.5

Global Tropics NA

CMIP3

AMIP

Simulation Skill of CIMP3 and AMIP for Land Temp Indices

00.10.20.30.40.50.60.70.80.9

1

Global Tropics NA

CMIP3

AMIP

ACC

RMS

AMIP is superior to CMIP3 in simulation

Annual and Global Mean Land Temperature Forecasts with CMIP3Annual and Global Mean Land Temperature Forecasts with CMIP3

CMIP3 based forecast has the almost the same skill as the analysis based OCN

Annual and Global Mean Land Temperature Forecast with AMIPAnnual and Global Mean Land Temperature Forecast with AMIP

The skill of AMIP based forecast is lower than the OCN

0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

Global Tropics NA

OCN

SLP_CMIP3

SLP_AMIP

PST_CMIP3

PST_AMIP

Forecast Skill Comparison

0

0.1

0.2

0.3

0.4

0.5

0.6

Global Tropics NA

OCN

SLP_CMIP3

SLP_AMIP

PST_CMIP3

PST_AMIP

ACC

RMS

Simulation Skill: CIMP vs AMIPSimulation Skill: CIMP vs AMIP

CMIP3 AMIP

Forecast Skill: CMIP3 Persistent vs OCNForecast Skill: CMIP3 Persistent vs OCN

CMIP3 Persistent OCN

Forecast Skill: Sloped Extension of CMIP3 vs OCNForecast Skill: Sloped Extension of CMIP3 vs OCN

Sloped Ext of CMIP3 OCN

Summary and Future WorkSummary and Future Work

Warming trend is an important source of CPC S/I forecast skill in last Warming trend is an important source of CPC S/I forecast skill in last couple of decades;couple of decades;

CMIP3 well catches the warming trend of land temperature in CMIP3 well catches the warming trend of land temperature in observations;observations;

Empirical extension of CMIP3 data has a potential to provide Empirical extension of CMIP3 data has a potential to provide compatible or better forecast than the flat extension of observational compatible or better forecast than the flat extension of observational data (OCN) for annual mean temperature.data (OCN) for annual mean temperature.

AMIP runs are generally better than CMIP3 runs in simulation, but their AMIP runs are generally better than CMIP3 runs in simulation, but their empirical extension is not as good as that of CMIP3 in forecast due to empirical extension is not as good as that of CMIP3 in forecast due to bigger “noise”.bigger “noise”.

The analysis will be extended to seasonal mean and longer lead The analysis will be extended to seasonal mean and longer lead forecast, and a further study will be toward a statistical-dynamical tool forecast, and a further study will be toward a statistical-dynamical tool to project warming trend and other LF components onto S/I forecast.to project warming trend and other LF components onto S/I forecast.

Optimal Window lengthOptimal Window length

Background: How is Warming Trend Affecting SI Predictions?Background: How is Warming Trend Affecting SI Predictions?

Official CPC Sfc. Temp Forecasts