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

Investigating soil moisture-climate interactions in a changing climate:

A review

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:

dS/dt = P – E – Rs – Rg dH/dt = Rn – λE – SH – G

Coupled through evapotranspiration term

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)

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SM only affects climate in these transitional “hot spots” regions

1. strong SM-EVAP coupling2. large mean EVAP

*AGCM ensemble simulations from GLACE

WET: large EVAP, but not controlled by SM

DRY : EVAP controlled By Soil moisture, but mean too small

OBS evidence for different SM regimes

“SM limited” “ Transitional ” “Energy Limited”

Dry Mediterranean Temperate Forest Artic tundra

*Different Drivers of SM conspire to make similar EVAP in summer, despite different climates / land cover

Soil Moisture – Temperature Coupling

PotentialPositive feedback

Regions of strong SM-TEMP coupling

Transitional “hot spots” zonesWhere temperatureDepends on Soil-moisture

Radiation limited regimes

SM limited regimes

Soil Moisture – Precip Coupling

?? Don’t even know theCorrect 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 SM-

Precip 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 (“energy-limited” regime)

-Changes in Climate Variability Cannot be simply Derived from changesIn mean climate

- Again Mediterranean Hot Spot Clear

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 VariabilityTransitional 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

• 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

Challenges and uncertainties (cont.)