Investigating soil moisture-climate
interactions in a changing climate:
A review
Sonia I. Seneviratne ⁎, Thierry Corti, Edouard L. Davin, Martin Hirschi,
Eric B. Jaeger, Irene Lehner, Boris Orlowsky, Adriaan J. Teuling
Institute for Atmospheric and Climate Science, ETH Zurich, Switzerland
Many complex land processes and feedbacks!
Some Preliminaries
• “Evapotranspiration” = net effect of ground
evaporation and plant transpiration
(mostly the latter)
• More than half of solar radiation used for land
evapotranspiration
• Soil Moisture controls the partitioning of
sensible and latent fluxes (Bowen Ratio) with
implication on meteorology.
Clouds due to Plant Transpiration
• Dry Season in the Amazon Basin
• Plants more active in Dry Season!
Role of Soil Moisture is 2-fold:
Coupled through evapotranspiration term
dS/dt = P – E – Rs – Rg
dH/dt = Rn – λE – SH – G
Soil-Moisture affects climate through Δ
Evapotranspiration (Latent heat flux)
Classic Conceptual Framework : 2 regimes
EF independent of soil moisture
(e.g. Amazon in Summer)
No evaporation
(e.g. Sahara)
SM only affects climate in these transitional
“hot spots” regions
1. strong SM-EVAP coupling
2. large mean EVAP
DRY : EVAP
controlled By Soil
moisture, but
mean too small
WET: large EVAP, but not
controlled by SM
*AGCM ensemble simulations from GLACE
OBS evidence for different SM regimes
“SM limited”
Dry Mediterranean
“ Transitional ”
Temperate Forest
“Energy Limited”
Artic tundra
*Different Drivers of SM conspire to make similar EVAP in summer,
despite different climates / land cover
Soil Moisture – Temperature Coupling
Potential
Positive feedback
Regions of strong SM-TEMP coupling
Transitional
“hot spots” zones
Where temperature
Depends on
Soil-moisture
Radiation limited regimes
SM limited regimes
Soil Moisture – Precip Coupling
?? Don’t even know the
Correct sign here!
Regions of strong SM-Precip coupling
• In GLACE models, EVAP sensitivity appears to control both T
and P coupling
• BUT significant inter-model variability
• GLACE models may not be able to simulate negative SMPrecip feedbacks found in CRM, RCM, and OBS
Other SM–climate interactions
• Persistence (“memory”) of soil moisture anomalies
– SM acts as both water and energy storage
– Potential implications for subseasonal/seasonal forecasting
– Again depends on “hot spot” regions where coupling is strong
•
Non-local and Large scale impacts
- e.g. Advection of dry/hot air over negative SM anomalies
- Apparently relevant for spread of European heat waves
• Soil Moisture – Albedo interaction
– Soil moisture anomalies affect both bare-soil and vegetative albedo
• Interaction with Biogeochemical cycles
– CO2 uptake by plants coupled with water loss via transpiration
– Less water  Less productive plants  More CO2
Δ Soil Moisture in a warming world
Projected Decrease
In precipitation in
mid-Lat and sub-arid
Regions
Drives SM decrease
* Note no change in
SM in wet places in
spite of increased
Precip (“energylimited” regime)
- Again Mediterranean
Hot Spot Clear
-Changes in
Climate Variability
Cannot be simply
Derived from changes
In mean climate
How SM can affect Climate Variability
Seasonal cycle
“Radiation-Limited”
Wet regime
“SM-limited”
Transitional regime
If a region shifts to a SM-limited regime and becomes a coupling “hot spot”
 then EVAP variability depends highly on SM and
 then SM is an important driver of TEMP (via Bowen Ratio)
Projected changes in SM-Temp coupling
Red = Soil moisture limited regime
Blue = Radiation limited regime
* Projected decrease in Precip causes Central Europe
to switch from Blue to Red
Does SM-climate interactions amplify
or damp Climate Variability?
• Wet soil moisture regime
- EVAP is insensitive to soil moisture and has no
effect on CLIVAR
• Transitional soil moisture regime
- EVAP very sensitive to soil moisture and
significantly impacts climate
• Dry soil moisture regime
– EVAP very sensitive to soil moisture, but very
limited
If Climate changes from :
Wet  Transitional = Increased Climate Variability
Transitional  Dry = Decreased Climate Variability
Challenges and uncertainties
• Significant divergence among models regarding SM–
Precipitation feedbacks
– Still don’t know what sign is here, let alone magnitude!
• Evap sensitivity to soil moisture highly variable among LSMs
Challenges and uncertainties (cont.)
• Better Diagnostics to validate models
• Coupling of key processes often more important
to climate prediction than absolute values of
temp, evap, etc..
• How to assimilate disparate land data sets
• More comprehensive ground network given land
heterogeneity