Outline of presentation

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Summer and Winter Season Reforecast Experiments
with the NCEP Coupled Forecast System (CFS) using
Different Land Models and Different Initial Land
States
This development is sponsored by CPPA Program of
the NOAA Climate Program Office
Acknowledgment to S. Saha, S. Moorthi, W. Wang,
C. Thiaw
33rd Annual Climate Diagnostics and Prediction
Workshop 21 October 2008
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Objective of this project

• Upgrade the land physics and initial land states
  of the NCEP Climate Forecast System (CFS) and
  assess the impact on CFS summer and winter season
  reforecasts.
• Motivation While SST anomalies are believed to
  be the foremost source of seasonal predictability
  in coupled global models, land surface anomalies
  are generally believed to be the second most
  important source of seasonal predictability (e.g.
  anomalies of soil moisture, snowpack, vegetation
  cover).

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Land Model Upgrade in CFS experimentsNoah LSM
(new) versus OSU LSM (old)

• Noah LSM
• 4 soil layers (10, 30, 60, 100 cm)
• Frozen soil physics included
• Surface fluxes weighted by snow cover fraction
• Improved seasonal cycle of vegetation cover
• Spatially varying root depth
• Runoff and infiltration account for sub-grid
  variability in precipitation soil moisture
• Improved soil snow thermal conductivity
• Higher canopy resistance
• More

• OSU LSM
• 2 soil layers (10, 190 cm)
• No frozen soil physics
• Surface fluxes not weighted by snow fraction
• Vegetation fraction never less than 50 percent
• Spatially constant root depth
• Runoff infiltration do not account for subgrid
  variability of precipitation soil moisture
• Poor soil and snow thermal conductivity,
  especially for thin snowpack and moist soils

Noah LSM replaced OSU LSM in operational NCEP
medium-range Global Forecast System (GFS) in late
May 2005
Many Noah LSM upgrades assessments were result
of collaborations with CPPA PIs
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Initial Land States Two Sources GLDAS/Noah
Global Reanalysis 2 (GR2/OSU)

• GLDAS an uncoupled land data assimilation
  system driven by observed precipitation analyses
  (CPC CMAP analyses)
• Executed using same grid, land mask, terrain
  field and four-layer Noah LSM as in experimental
  CFS forecasts
• Non-precipitation land forcing is from GR2
• Executed retrospectively from 1979-2006 (after
  spin-up)
• GR2 a coupled atmosphere/land assimilation
  system wherein land component is driven by model
  predicted precipitation
• applies the OSU LSM with two soil layers
• nudges soil moisture based on differences between
  model and CPC CMAP precipitation

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Monthly Time Series (1985-2004) Area-average
Illinois 2-meter Soil Moisture mm Observations
(black), GLDAS/Noah (purple), GR2/OSU (green)
The climatology of GLDAS/Noah soil moisture is
higher and closer to the observed climatology
than that of GR2/OSU.
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GLDAS/Noah (top ) versus GR2/OSU (bottom)
2-meter soil moisture ( volumetric) May 1st
Climatology 01 May 1999 Anomaly
Observed 90-day Precipitation Anomaly (mm) valid
30 April 99
GLDAS/Noah
GLDAS/Noah
GR2/OSU
GR2/OSU
Left column GLDAS/Noah soil moisture climo is
generally higher then GR2/OSU Middle column
GLDAS/Noah soil moisture anomaly pattern agrees
better than that of GR2/OSU with observed
precipitation anomaly (right column top)
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CFS Experiment Design four configurations
Four configurations of T126 CFS A) CFS/OSU/GR2
- OSU LSM, initial land states
from GR2 (CONTROL)? B) CFS/Noah/GR2
- Noah LSM, initial land states from GR2 C)
CFS/Noah/GLDAS - Noah LSM, initial
land states from T126 GLDAS/Noah D)
CFS/Noah/GLDAS-Climo - Noah LSM, initial land
states from GLDAS/Noah climo
Summer CFS Experiments all 4 configurations
above (A, B, C, D) 25-year (1980-2004) summer
reforecasts (10 member ensembles) from mid April
and early May initial conditions
Winter Land Related Experiments top 2
configurations in table (A C) 24-year
(1981-2004) winter reforecasts (10 member
ensembles) from late Nov and Dec initial
conditions
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Summer Results
25-year (1980-2004) CFS summer reforecasts (10
members) from mid April and early May initial
conditions
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Partition 25 summers (80-04) intoENSO Neutral
Non-neutral samplesusing MJJ Nino3.4 SST
anomaly0.7C as a threshold magnitude
15 neutral summers80,81,84,85,86,89,90,94,95,96,
98,00,01,03,04
10 non-neutral summers 82,83,87,88,91,92,93,97,99
,02 (red warm, blue cold)
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10 non-neutral ENSO years JJA precipitation AC
score
Worst Case
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15 neutral ENSO years JJA precipitation AC
score
Next Worst Case
Worst Case
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CONUS-average JJA precipitation AC score
Non-Neutral Years
Significance test (T-statistic) shows
differences wrt third bar are not significant at
90 confidence.
Neutral Years
0.04
Significance test (T-statistic) shows differences
wrt third bar are significant at 90 confidence.
-0.06
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Winter Results
24-year (1981-2004) winter reforecasts (10
members) from late Nov and Dec initial
conditions

• Only two of four configurations were
  executed-- OSU/GR2 (Control)-- Noah/GLDAS

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Partition 24 winters (1981-2004) intoENSO
Neutral Non-neutral samplesusing JFM Nino3.4
SST anomaly0.5C as a threshold magnitude
10 neutral winters 81, 82, 84, 90, 91, 93, 94,
97, 02, 04
14 non-neutral winters 83, 85, 86, 87, 88, 89,
92, 95, 96, 98, 99, 00, 01, 03
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14 Non-neutral Years JFM Precipitation AC
Score
10 Neutral Years JFM Precipitation AC Score
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CONUS-average JFM precipitation AC score
Non-neutral Years
0.2
Significance test (T-statistic) shows
differences are not significant at 90
confidence
0
Neutral Years
0.2
Significance test (T-statistic) shows
differences are not significant at 90
confidence
I
0
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Conclusions

• When upgrading the land surface model of the CFS,
  it is imperative to upgrade to the same land
  surface model in the supporting data assimilation
  system
• Positive impact of land surface upgrade on CFS
  seasonal forecast skill for precipitation is
  modest
• Significant only for summer season and only in
  neutral ENSO years (and then only small positive
  impact)
• Essentially neutral impact for winter season and
  non-neutral ENSO summers
• Differences in CFS precipitation skill over CONUS
  between neutral and non-neutral ENSO years is
  larger than the skill differences between two
  different land configurations for same sample of
  years
• Indicates that impact of SST anomaly is greater
  than impact of land surface configuration