SAC Meeting – 12 April 2010 Land Surface Impact on Sub-seasonal Prediction Zhichang Guo and Paul...

SAC Meeting – 12 April 2010

Land Surface Impact on Sub-seasonal Prediction

Zhichang Guo and Paul Dirmeyer


Second Global Land-Atmosphere Coupling Experiment (GLACE-2)

• GLACE-2 is a project jointly sponsored within WCRP by GEWEX and CLIVAR. It is designed to evaluate the land surface contribution to sub-seasonal and seasonal prediction.

• This project is being completed with a large number of state-of-the-art global forecasting systems in a coordinated, comprehensive, and systematic manner.


Motivation for GLACE-2

For soil moisture initialization to add to sub-seasonal or seasonal forecast skill, two criteria must be satisfied:

1. An initialized surface anomaly must be “remembered” into the forecast period, and

2. The atmosphere must be able to respond to the surface anomaly.

Addressedby GLACE

Addressedby GLACE2:the fullinitializationforecast problem


GLACE-2: Experiment Overview

Perform ensembles of retrospective

seasonal forecasts

realistic initial land surface states

Prescribed, observed SSTs

realistic initial atmospheric states

Evaluate forecasts against observations;

Evaluate signal-to-noise ratio

Series 1:

Perform ensembles of retrospective

seasonal forecasts

realistic initial land surface states

Prescribed, observed SSTs

realistic initial atmospheric states

Evaluate forecasts against observations;

Evaluate signal-to-noise ratio

Series 2:

“Randomize” land

Initialization!

Step 1

Step 2


GLACE-2: Measures of predictability and forecast skill

Step 3: Compare skill and predictability in two sets of forecasts; isolate contribution of realistic land initialization.

Forecast skill,Series 1

Forecast skill, Series 2

Forecast skill due to land initialization

Signal-to-noise ratio,Series 1

Signal-to-noise ratio, Series 2

Predictability due to land initialization


Baseline: 100 Forecast Start Dates

Apr 01

19861987198819891990

Each ensemble consists of 10 simulations, each running for 2 months.

1000 2-month simulations for each series (realistic and random ICs).

19911992199319941995

Apr 15May 01

May 15Jun 01

Jun 15Jul 01

Jul 15Aug 01

Aug 15

10Years


GLACE-2 COLA AGCM Experiments, 250 Forecast Start Dates

Apr 01

198219831984

• Each ensemble consists of 10 simulations, each running for 3 months.• 2500 3-month simulations for each series (realistic and random ICs).• Atmospheric initial states: NCEP-NCAR Reanalysis.• Land surface initial states: SSiB offline simulations (GOLD, driven by Princeton

meteorology force data, monthly observations + reanalysis synoptic and diurnal cycle).

….……….200420052006

Apr 15

May 01May 15

Jun 01Jun 15

Jul 01Jul 15

Aug 01Aug 15

…….…….25

Years


Participant List

Group/Model Points of Contact# models

S. Seneviratne, E. Davin

E. Wood, L. LuoZ. Guo, P. Dirmeyer

R. Koster, S. Mahanama2

B. van den Hurk

T. GordonJ.-H. JeongT. Yamada

2111

111

13 models(10 finished)

1 G. Balsamo, F. Doblas-Reyes

M. Boisserie11 B. Merryfield

01. NASA/GSFC (USA): GMAO seasonal forecast system (old and new) NSIPP

02. COLA (USA): COLA GCM, NCAR/CAM GCM03. Princeton (USA): NCEP GCM04. IACS (Switzerland): ECHAM GCM05. KNMI (Netherlands): ECMWF06. ECMWF07. GFDL (USA): GFDL system (1/2 completed)08. U. Gothenburg (Sweden): NCAR09. CCSR/NIES/FRCGC (Japan): CCSR GCM10. FSU/COAPS11. CCCma

Green: Finished baseline forecasts


Key notes for experiment and data analysis• Baseline simulations: 10 years (1986-1995), 10 member ensembles, 10

start dates (1st and 15th of Apr-Aug), 2-month forecast.

• COLA AGCM: 25 years (1982-2006), 10 member ensembles, 10 start dates (1st and 15th of Apr-Aug), 3-month forecast.

• 2 cases (Realistic Land IC minus Random gives impact of initial soil state on forecast).

• Focus on land surface IC contribution to the predictability and forecast skill of temperature and precipitation.

• Focus on sub-seasonal: examine daily and 15-day periods.

• Global simulations - here we concentrate on results over North America.


Measures for Predictability:

STR =variability of ensemble mean

total variability

SNR =variability of ensemble mean

variability about ensemble mean

Measures for Land Impacts on Predictability:

variability of ensemble mean for realistic IC

variability of ensemble mean for random IC

Realistic IC Random IC

Month: June Lead:31-45 Solid lines: Ensemble mean

Assume same noises for both realistic and random cases, this is equivalent to the ratio of SNR. We use the following metric to evaluate land impact on predictability

signal for realistic IC

signal for random ICLog

10


Land Impacts on Air Temperature Potential PredictabilityCOLA AGCM

Regions above 95% significance level are dotted.

Land impacts are stronger in June and July, weaker in May and August, and weakest in April.

Land impacts on potential predictability persists through the 2-month forecast periods.


Land Impacts on Precipitation Potential PredictabilityCOLA AGCM

Similar figure for precipitation, impacts on precipitation predictability are weaker than air temperature.

Land impacts are relatively stronger in June and July, weaker in other months.

Land impacts on potential predictability persists through the 2-month forecast periods.


Land Impacts on Air Temperature Potential PredictabilityNCEP AGCM

COLA AGCM

Land has impact for all months (April-August) with comparable strength.

But the response for impacts are slower than COLA AGCM (Realistic Land IC in NCEP AGCM has no significant impacts on temperature predictability for the first 15 days).

Geographic patterns of land impacts change with lead time and month (Impacts for COLA AGCM tend to be locked in certain areas)


Land Impacts on Air Temperature Potential PredictabilityECMWF AGCM

COLA AGCMNCEP AGCM

Similar to COLA AGCM: land impacts have seasonal dependence, and persists through the 2-month forecast periods (weaker for the first 15 days).

ECMWF: stronger in April and May, and weaker in JJA.

COLA: stronger in JJA, and weaker in April and May.

NCEP: comparable strength for all months (AMJJA), but the impacts are slower than COLA AGCM.


Forecast Skill measure: r2 when regressed against observations

COLA AGCM - 25 years.

Compute r2 from N points in scatter plot, one point for each of the N independent forecasts.(N=25*3*2=150 for MJJ)

Forecast skill,

Series 1

Forecast skill,

Series 2

Forecast skill gain due to realistic

land initialization


Land Impacts on Air Temperature Forecast SkillMulti-model Analysis

The multi-model average of air temperature forecast has been correlated against observations for series 1 and 2.

The r2 difference indicates where the air temperature forecast can get benefits from realistic land IC (common to most models)

Overall, land IC has significant positive impacts for at least 45 days.


Land Impacts on Precipitation Forecast Skill

Multi-model Analysis

Impacts of land IC on precipitation forecast skill are weaker than air temperature.

But, in general, land IC still has positive impacts on precipitation forecast skill.


Land Impacts on Air Temperature Forecast SkillWeighted Multi-model Analysis

Multi-model Analysis

Using prior knowledge of individual model’s forecast skill, the weighted multi-model average of forecasted air temperature has been calculated, and correlated against observations.

The geographic pattern of land impacts is similar to that of multi-model analysis. It did improve the forecast skills for both series 1 and 2, though it did not make further improvement on r2

differences.


Areal average of weights used for the weighted multi-model analysis has been computed over North America for both series 1 and 2.

The figures show inter-model differences of forecast skill.

Weights for AGCMs


Inter-model Comparison

Models appear to differ in their ability to extract skill from land initialization.

For most AGCMs, there exists certain common areas where land IC tends to have significant impacts on temperature forecast skill.


Motivation for GLACE-2

For soil moisture initialization to add to subseasonal or seasonal forecast skill, two criteria must be satisfied:

1. An initialized surface anomaly must be “remembered” into the forecast period, and

2. The atmosphere must be able to respond to the surface anomaly.

Addressedby GLACE

Addressedby GLACE2:the fullinitializationforecast problem


Forecast Skill, Coupling Strength, and Soil Moisture Memory

Impacts of land surface IC on air temperature forecast skill are highly related to the soil moisture memory.

Impacts of land surface IC on precipitation forecast skill are related to both of the soil moisture and land-atmosphere coupling strength.


Temperature Forecast Skill and Soil Moisture Memory

Areas with longer soil moisture memory tend to have stronger ability to extract skills from realistic land surface initialization.


Land Impacts on Air Temperature Forecast SkillTemporal Variability of Land Impacts

With COLA-AGCM, GLACE-2 experiments have been extended to 25 yrs (1982-2006).

This animation shows the land impacts on air temperature forecast skill with 10-year moving window.

It indicates that impacts of land IC on forecast skill have temporal variability.


Land Impacts on Precipitation Forecast SkillTemporal Variability of Land Impacts

Similar animation for precipitation forecast skill.

For some years, impacts of land surface IC on precipitation forecast skill are much stronger than other years.


Air temperature forecast in the realistic series has been replaced with forecasted air temperature in the random series during dry, neutral, and wet years, respectively.

Degradation indicates the relative importance of land surface initialization during dry, neutral, and wet years.

Asymmetry impacts of land surface on sub-seasonal prediction for dry and wet years

Dry Years vs. Wet Years


Summary• Contribution of land surface initialization to sub-seasonal predictability and

forecast skill is highly model-dependent.• Multi-model analysis reveals the regions where realistic land surface

initialization could contribute to sub-seasonal forecast skill (western and northern parts of the USA for air temperature, and northern parts of the USA for precipitation).

• Significant contribution of land surface initialization to sub-seasonal air temperature prediction is found over areas where soil moisture has longer memory.

• Moderate contribution of land surface initialization to seasonal precipitation prediction is found over limited areas where soil moisture has longer memory as well as it exhibits large land-atmosphere coupling strength.

• Asymmetry impacts of land surface on sub-seasonal forecast (impacts during dry years are much stronger than wet years).


Thank You!


Inter-model Comparison

Models appear to differ in their ability to extract skill from land initialization.

For most AGCMs, there exists certain common areas where land tends to have significant impacts on precipitation forecast skill.


Realistic IC

Realistic IC

Random IC

Random IC

Decay of skill with time of GLACE-2 forecasts over the region 124.25-96.25W, 18.0-46.0N. 15-day running means are shown for runs with realistic land initialization (solid), random land initialization (dashed) and the difference (dotted). Horizontal line shows the 95% confidence threshold for significance.

Decay of Skill


Land Impacts on Precipitation Potential Predictability

NCEP AGCM

COLA AGCM

Similar figure for precipitation.

Land has impact for all months (April-August) with comparable strength.

Weaker impacts for the first 15 days.

SAC Meeting – 12 April 2010 Land Surface Impact on Sub-seasonal Prediction Zhichang Guo and Paul...

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