This is an overview of forecast skill related to land surface initialization in prediction systems. It is provided as "general interest" to the USCLIVAR PPAI (Predictability, Predictions, and Applications Interface) community

Koster, Randal

English

NASA Technical Reports Server

Contribution of Land Surface States 
to Sub-seasonal Predictability
Randal Koster
Global Modeling and Assimilation Office
NASA/GSFC
Greenbelt, MD USA
https://ntrs.nasa.gov/search.jsp?R=20200001940 2020-05-24T04:15:05+00:00Z
Theoretical Underpinning
An initialized land state can affect a forecast if the following two 
things happen:
Time 0 1 month 2 months
a. The initialized 
anomaly is 
remembered into 
the forecast period. 
(“memory”)
b. The remembered 
anomaly is able to 
affect the atmosphere 
at the later date. 
(“feedback”)
(Image stolen 
from 
internet!)
Which land states might have usable memory?
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
(Image stolen 
from 
internet!)
For which land states has an impact of initialization on forecasts been demonstrated?
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
(Image stolen 
from 
internet!)
For which land states has an impact of initialization on forecasts been demonstrated?
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
Soil moisture memory is well-established; estimated time-scales range from weeks 
to months.
Vinnekov and Yeserkepova, J. Climate, 4, 66-79, 1991
“empirical autocorrelation function”
Seneviratne et al., J. Hydromet., 7, 1090-1112, 2006
obs
~1-month-lagged autocorrelations of soil moisture 
(boreal summer)
Time 0 1 month 2 months
a. The initialized 
anomaly is 
remembered into 
the forecast period. 
(“memory”)
b. The remembered 
anomaly is able to 
affect the atmosphere 
at the later date. 
(“feedback”)
So for soil moisture, we 
have this part. What about this part?
Conventional wisdom regarding control of soil moisture on 
evapotranspiration (and thereby on climate, forecasts)
E/Rnet
Mean soil moisture (degree of saturation)0 1Evapotranspiration, 
normalized by 
incoming energy
For drier conditions, 
evapotranspiration 
varies with soil moisture
For wetter conditions, 
evapotranspiration largely 
independent of soil moisture
Relevant observations 
are rare, but where they 
exist, they support this 
idea….
Dirmeyer et al., J. Hydromet., 7, 1177–1198, 2006
8
Shown here: results from the 
multi-model GLACE experiment.  
Indicated is where soil moisture 
variability helps guide short-term 
boreal summer rainfall variability.
Koster et al., Science, 305, 1138–1140, 2004
Because of this relationship, the connection between soil moisture and 
the atmosphere (through the former’s effect on evapotranspiration) is 
strongest in the transition zones between dry and wet areas.
Why are the transition 
areas important?  See next 
slide, which focuses on 
North America…
9
Explanation for why soil moisture feedback on the 
atmosphere is strongest in transition zones
E/Rnet
Mean soil moisture (degree of saturation)0 1
When it is really 
dry, E is too small 
(and varies too 
little) to have an 
effect.
Koster, R. Soil Moisture in the Climate System
Explanation for why soil moisture feedback on the 
atmosphere is strongest in transition zones
E/Rnet
Mean soil moisture (degree of saturation)0 1
When it is really wet, E does 
not vary with soil moisture, 
so precipitation and 
temperature cannot, either. 
11Koster, R. Soil Moisture in the Climate System
When it is really 
dry, E is too small 
(and varies too 
little) to have an 
effect.
Explanation for why soil moisture feedback on the 
atmosphere is strongest in transition zones
You mainly get an 
impact in the 
“sweet spot” in 
between:
E/Rnet
Mean soil moisture (degree of saturation)0 1
When it is really wet, E does 
not vary with soil moisture, 
so precipitation and 
temperature cannot, either. 
When it is really 
dry, E is too small 
(and varies too 
little) to have an 
effect.
…in the transition zone, where 
E does vary with soil moisture 
and E is significantly large.
12Koster, R. Soil Moisture in the Climate System
Time 0 1 month 2 months
a. The initialized 
anomaly is 
remembered into 
the forecast period. 
(“memory”)
b. The remembered 
anomaly is able to 
affect the atmosphere 
at the later date. 
(“feedback”)
So, for soil moisture, we seem to have both of these parts, at least in 
some areas.
So, for soil moisture, we seem to have both of these parts, at least in 
some areas.
Time 0 1 month 2 months
a. The initialized 
anomaly is 
remembered into 
the forecast period. 
(“memory”)
b. The remembered 
anomaly is able to 
affect the atmosphere 
at the later date. 
(“feedback”)
Estimations of forecast skill associated with 
soil moisture initialization
The second phase of the 
Global Land-Atmosphere Coupling Experiment
(an international, multi-institution project)
Koster et al., J. Hydromet., 12, 804-822, 2011
-2:
Gist of experiment: 
1. Perform two sets of forecast simulations:
(i) with accurate soil moisture initial conditions (ICs)
(ii) without accurate soil moisture ICs
2. Compare forecasted P, T to obs.
3. Compute soil moisture 
contribution to forecast skill:  
Skill from 
simulations 
w/ accurate 
land ICs
Skill from 
simulations 
w/o 
accurate 
land ICs
Skill due 
to land ICs
11
Koster, R. Soil Moisture in the Climate System
Baseline: 100 Forecast Start Dates
1986
1987
1988
1989
1990
Each ensemble consists of 10 simulations, each running for 2 months.
1000 2-month simulations.
1991
1992
1993
1994
1995
16
Skill measure: r2 when regressed against observations
Observed temperature
(standard normal deviate)
Forecasted temperature
(standard normal deviate)
Compute r2 from N points in 
scatter plot, one point for each of 
the N independent forecasts.
(N=100 for MJJAS; N=60 for JJA)
17
We focus here on multi-model “consensus” view of skill.
We focus here on JJA, the period when N.H. evaporation 
is strongest.
We focus here on the U.S., for which:
-- models show strong inherent predictability
associated with land initialization (GLACE-1!)
-- observations are reliable over the forecast
period
18
Temperature forecasts:  Increase in skill due to land initialization (JJA)
(conditioned on strength of local initial soil moisture anomaly)
Extreme 
tercilesall points
Extreme 
quintiles
Extreme 
deciles
16-30 days
31-45 days
46-60 days
19
Forecast skill: r2 with land ICs vs r2 w/o land ICs
Dates for conditioning vary w/location
“Weaker” models 
are averaged in 
with “stronger” 
ones.
Precipitation forecasts:  Increase in skill due to land initialization (JJA)
(conditioned on strength of local initial soil moisture anomaly)
Extreme 
tercilesall points
Extreme 
quintiles
Extreme 
deciles
16-30 days
31-45 days
46-60 days
20
Forecast skill: r2 with land ICs vs r2 w/o land ICs
Dates for conditioning vary w/location
“Weaker” models 
are averaged in 
with “stronger” 
ones.
For example:
Wet soil a higher evap., lower sensible heat flux 
This can affect local air temperature: 
a more evaporative cooling
a lower air temperature
It can also affect local precipitation:
a boundary layer modification
a conditions more conducive
(or perhaps less conducive)
to onset of moist convection
E
H
Wet Soil
Local vs. Remote Soil Moisture Impacts on the Atmosphere
3
1.  Consider local effects.
2. Now consider potential remote effects:
Consider the possibility 
that a soil moisture 
anomaly in one location…
… can “phase-
lock” an overlying 
planetary wave 
into position…
E
H
Wet Soil
… that in turn can 
affect conditions at 
some other location.
V250
Experimental Design
Control: Ensemble (768 members) of April-July simulations using atmosphere-land 
components of the GEOS-5 system, at 1°×1° resolution.
Experiment: Same as control, except:
(a) Smaller ensemble size (192 or 96 members)
(b) Precipitation in a selected region is not 
allowed to hit the surface during April-June, 
forcing the surface to become dry there.
dryness imposed
The dry surface anomaly does (on average) 
induce a wave pattern in June-July…
dryness imposed
The dry surface anomaly does (on average) 
induce a wave pattern in June-July…
… that does lead to remote, 
wavelike patterns in T2M and 
precipitation anomalies.
Important consideration: Given the large number of ensemble members needed 
to extract the signals of interest from the AGCM, we are talking here about 
shifts in PDFs.  These shifts are subtle, and their relevance (e.g.) to forecasting 
large-scale dryness are yet to be demonstrated. 
Precipitation Anomaly
0
climatology
induced by dry surface 
anomaly somewhere 
in US interior
(Image stolen 
from 
internet!)
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
Enough about soil moisture.  
How about snow initialization 
in forecasts?
Jaison Thomas and Aaron Berg performed two sets of forecasts initialized on April 1 for 
each year in 1986-2005:
1) With realistic April 1 initializations of snow water equivalent, frozen soil moisture, and 
liquid soil moisture. 
2) Without these realistic initializations.
Forecasted 15-day-average 2m temperatures were compared to observations (reanalysis).
As before,
Skill (r2) from 
simulations 
w/ accurate 
land ICs
Skill (r2) from 
simulations 
w/o accurate 
land ICs
Skill due to 
land ICs
r2 differences
(15-day lead)
r2 differences
(30-day lead)
r2 differences
(45-day lead)
Snow and soil water contributions to skill:
Ambadan et al., Climate Dynamics, 47, 49-65, 2016
Another study:  Peings et al. (Clim. Dyn., 37, 985-1004, 2011) performed an analysis 
evaluating the contribution of snow initialization to temperature and pressure forecast skill.
Increase in anomaly correlation 
coefficient due to snow initialization:    
2-m air temperature Snow initialization led to 
improvements in the 2-m 
temperature skill, mostly in the first 
2 months following the March 1 
initialization.  The initialization had 
little impact on the large scale 
circulation, however, as indicated by 
predicted sea level pressure patterns.
(With thanks to Herve Douville, Meteo-France)
Lin et al. (GRL, 43, 
doi:10.1002/2016GL07
0966) examined the 
additional forecast skill 
achievable through 
the use of remote 
sensing in the 
initialization process.
Streamflow forecasting via snow and/or soil moisture initialization is also a 
subseasonal-to-seasonal forecast topic.
Obvious:  Larger 
snowpack a Increased 
streamflow during 
snowmelt season.
Less obvious: Impact of 
soil moisture…
Snow (or rainfall) 
over wet soil: most of 
the meltwater runs off 
into streams, 
reservoirs
Snow (or rainfall) over 
dry soil: most of the 
meltwater infiltrates the 
soil and is lost to water 
resources
Knowledge of winter snow, soil moisture a streamflow forecast skill
Performed experiments; estimated contribution to 3-month streamflow 
forecast skill from snow and soil moisture ICs:
Mahanama et al., J. Hydromet., 13,189-203, 2012
From both 
snow and soil 
moisture ICs
From snow ICs
From soil 
moisture ICs
(Image stolen 
from 
internet!)
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
Vegetation phenology ICs (e.g., 
how “leafy” the vegetation is at 
the start of a given forecast) may 
provide some skill…
Vegetation state.  An experiment 
similar to GLACE-2, but focusing on 
the impacts of initialized vegetation 
state on monthly forecast skill (using 
a land model with dynamic 
phenology) was recently performed. 
In fact, the effects of both soil 
moisture and vegetation initialization 
were quantified with the same 
framework and compared side-by-
side.
Koster and Walker, J. Hydromet., 16, 1456-1465, 2015
(Monthly forecasts)
(Monthly forecasts)
Skill (r2) from 
simulations 
w/ accurate 
land ICs
Skill (r2) from 
simulations 
w/o accurate 
land ICs
Skill due 
to land ICs
(Monthly forecasts)
(Monthly forecasts)
Note: some differences 
between this pattern (for 
single model) and that for 
multi-model GLACE-2 
results
Many indications of 
positive impact, but with 
magnitudes smaller than 
that for soil moisture
(Image stolen 
from 
internet!)
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
Land heat content initialization 
was examined r cently in a 
study by Yongkang Xue.
1) LST: land surface temperature
2) The dotted areas denote
statistical significance less than
α=0.1 level of t-test values.
c.
b.a.
Observed differences between 9 coldest years and 9 warmest years
(based on N.W. U.S. & S. E. Canada LST)
May Observed LST and SST                                  June Observed Precipitation
June Observed LST and SST
Xue et al., 2012 (JGR), 2016 (ERL)
Observed/WRF-NMM simulated anomaly/difference of  2011 June Precipitation (mm day-1)
a. b.
Observed SUBT Effect
SUBT: Subsurface 
temperature.
The dotted areas 
denote statistical 
significance at the 
α=0.01 level of t-test 
values. 
Xue et al., Env. Res. Lett., 11, 044018, 2016.
(Image stolen 
from 
internet!)
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
Before we mention some ongoing challenges, here’s a brief summary
Lots of work already performed to address impacts 
on subseasonal/seasonal forecasts: impacts in warm 
season are reasonably large for temperature, much 
more modest for precipitation
(Image stolen 
from 
internet!)
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
So, before we discuss some ongoing challenges, here’s a brief summary
Some work performed to address 
impacts on subseasonal/seasonal 
forecasts; some positive impacts for 
temperature.
(Image stolen 
from 
internet!)
snow amount
vegetation 
phenology
near-surface 
soil moisture deeper soil moisture 
(e.g., groundwater)
land heat 
content
miscellaneous
(lake levels, …)
So, before we discuss some ongoing challenges, here’s a brief summary
Not much done 
here.  Impact 
presumably small??
Some current challenges
❑ Quantifying the skill contributions further, with a large complement of models (soil 
moisture analyses relatively mature, but not other variables)
❑More thorough theoretical analysis of memory and feedback mechanisms; 
characterizing “nature’s” land-atmosphere coupling strength.
❑ Inclusion of additional variables into operational forecast systems (e.g., phenology)
❑ Taking advantage of the potential for conditional forecasts
❑ Need for better data for initialization: optimizing use of limited measurement 
resources to maximize impact on forecast skill, and tapping into as-yet-unused data 
sources
Thank you.
Questions?
Extra Slides
Skill levels 
(extreme deciles)
16-30 days
31-45 days
46-60 days
46
Forecast skill: r2 with land ICs vs r2 w/o land ICs
temperature precipitation
Note the contradiction between diagnosed coupling strength 
locations (from earlier) and locations where skill appears:
Coupling strength
Reasons for the discrepancy are somewhat 
unclear but may be related to:
-- different set of models, with different 
biases (different transition zones)
-- spatial differences in memory
-- ability to produce a feedback loop 
(“coupled mode”) in the forecast system
Skill levels 
(extreme deciles)
16-30 days
31-45 days
46-60 days
47
Forecast skill: r2 with land ICs vs r2 w/o land ICs
temperature precipitation
Note the contradiction between diagnosed coupling strength 
locations (from earlier) and locations where skill appears:
Coupling strength
Reasons for the discrepancy are somewhat 
unclear but may be related to:
-- different set of models, with different 
biases (different transition zones)
-- spatial differences in memory
-- ability to produce a feedback loop 
(“coupled mode”) in the forecast system
perhaps not the primary reason, but scientifically exciting – worth a quick look!
1-month air temperature forecasts
Global averages of contributions over areas with adequate rain gauge density
dryness imposed
This, along with a suite of additional “dry surface” experiments, suggests a feedback loop:
streamfunction
dryness imposed
streamfunction
precipitationair temperature
precipitationair temperature
Notice drying and 
warming even 
outside of original 
selected region
precipitationair temperature
Dryness in these 
outside regions can in 
turn induce additional 
streamfunction 
changes


Contribution of Land Surface States to Sub-Season Predictability

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