Predictability of Monthly Mean Temperature and Precipitation: Role of Initial Conditions

Predictability of Monthly Mean
Temperature and Precipitation: Role of
Initial Conditions
Mingyue Chen, Wanqiu Wang, and Arun Kumar
Climate Prediction Center/NCEP/NOAA
Acknowledgments: Bhaskar Jha for providing the AMIP simulation data
33Rd Annual Climate Diagnostics & Prediction Workshop
October 20-24, 2008, in Lincoln, Nebraska
Monthly outlook is one of CPC’s official products
Temperature - Sep 2008
0.5 Lead (Official)
0 lead (update)
Observation
Off.
All Stations 2.4
Non-EC:
14.9
% Cov:
16.0
Up. Change
7.3
+4.9
17.4 +2.5
42.2 +26.2
How is current monthly outlook produced?
(Ed O’ Lenic et al. 2008)
– 0.5-month lead 1-month outlook
CCA, OCN, SMLR, and CFS
– 0-lead 1-month outlook
CCA, OCN, SMLR, CFS, and GFS 1-14 day daily
forecasts, etc.
Sources of predictability
– Initial atmospheric and land conditions, and
SSTs
– An initialized coupled atmosphere-land-ocean
forecast system, such as CFS, is needed to
harness this predictability
Issues to be discussed
– What is the predictability (prediction skill) because of
initialized observed conditions?
– What is the lead-time dependence?
– How does the predictability due to atmospheric/land
initial conditions compare with that from SSTs?
Analysis method
– Assess lead-time dependence of prediction skill of
monthly means in CFS hindcasts
– Compare CFS with the simulation skill from the
AMIP integrations to assess predictability due to
SSTs, and to assess on what time scale influence
of initial conditions decays
Models and data
• Retrospective forecast
• CFS (5 member ensemble)
• AMIP simulations
•
•
•
•
•
GFS (5 member ensemble)
CCM3 (20 member ensemble)
ECHAM (24 member ensemble)
NSIPP (9 member ensemble)
SFM (10 member ensemble)
• Variables to be analyzed
• T2m
• Precipitation
• The analysis is based on forecast and simulations for
1981-2006
Assessment of CFS monthly mean
forecast skills with different lead times
Definition of forecast lead time
30-day-lead
20-day-lead
10-day-lead
0-day-lead
1st day
11th day
21st day
1st day
Target month
CFS T2m monthly
correlation skill
• High CFS skill at 0-day
lead time
• Dramatic skill decrease
with lead time from 0-day
lead to 10-day lead and
more slow decrease
afterwards
• Large spatial variation
CFS T2m monthly correlation skill (global mean)
• High CFS skill at 0-day lead time
• Dramatic skill decrease with lead time from 0-day lead to 10-day lead and
more slow decrease afterwards
CFS T2m monthly forecast skills with different lead time
(zonal mean)
20
50
40 30
10
0
• Little change with
lead time over
tropics
• Quick decrease in
high latitudes
CFS T2m monthly forecast skills with different lead time
(zonal mean, DJF, MAM, JJA, & SON)
• CFS forecast skill decays vary seasonally
• Skills are higher in winter & spring over N. high latitudes
• Less changes over tropics
CFS Prec monthly forecast
skills with different lead time
• The monthly prec useful
skills are at 0-day-lead
forecast
• No useful skill at lead time
long than 10 day for most
regions
• Prec skill much lower than
T2m skill
Question: What is the source of
remaining skill for longer lead-time
forecasts?
A comparison of CFS hindcasts with
GFS AMIP simulations
CFS T2m monthly correlation skill vs. GFS AMIP
• The AMIP skill in high-latitudes is
low
• The GFS AMIP is similar to CFS in
the tropics.
CFS T2m monthly correlation skill vs. GFS AMIP
(global mean)
• Globally, the AMIP skill is comparable to CFS skill at
20-30-day lead
T2m monthly correlation skill (CFS vs. GFS AMIP)
(zonal mean)
10
50
40 30
0
20
GFS AMIP
• Similar skills in CFS & GFS
AMIP near the equator
• In N. lower latitudes (5N35N), CFS skill higher at
lead time shorter than 20
days
• Over N. high latitudes
(35N-80N), CFS skill
higher at lead time shorter
than 20-30 days
CFS T2m monthly forecast skills vs. AMIPs & MME
• The skills are different among 5 AMIPs
• GFS AMIP is comparable to 20-30 lead CFS
• The AMIP MME is almost comparable to 10-day lead CFS
Similar to AMIP MME, coupled MME may have potential to improve.
CFS T2m monthly forecast skills vs. AMIP GFS & MME
zonal mean
AMIP MME
AMIP GFS
• The AMIP MME skills are better than the single GFS over all the latitudes.
• Similar to AMIP MME, coupled MME may have potential to improve.
Conclusions
• For monthly forecasts, contribution from the
observed land and atmospheric initial conditions
does lead to improvements in skill.
• The improvement in skill, however, decays
quickly, and within 20-30 days, skill of initialized
runs asymptotes to that from SSTs.
• A simple average of multi-model AMIP runs
shows a positive increase of the skill of monthly
simulation, indicating room for further
improvements with the MME coupled forecasts.
Thanks!