The relative impact of local connections vs distant teleconnections on a regions climate

(or on hydrologic predictability)

Jason Evans

Outline• What do we need for hydrologic

predictability• Large scale Climate Modes• Local connections• Local vs distant influences• Future directions

What do we need for hydrologic predictability?

• Know characteristics of – Land surface

• Topography• vegetation

– Sub-surface• Soil types, horizons, macropores,….

– State of water in land-surface and subsurface

What do we REALLY need for hydrologic predictability?

We need to be able to predict the climate, particularly precipitation over land.

Predictability of ClimateTime scale• Weather (1-2 weeks)• Seasonal (2 weeks – 12 months)• Decadal (1-10 years)• Climate (10-100+ years)

IPCC AR5

Predictability of PrecipitationAt all time scales...• Depends on climate system processes with

persistence that affect precipitation (episodic) at a place– Large scale climate modes (ENSO, NAO,…)(slowly varying SSTs + distant teleconnections)– Local land-atmosphere feedbacks(slowly varying soil moisture & vegetation)

Large Scale Climate Modes - ENSO

• Is ENSO predictable?– ENSO predictable at least 6 months in advance

(IPCC AR3 2001)

Large Scale Climate Modes - ENSO

• Is ENSO predictable?– ENSO predictable at least 6 months in advance

(IPCC AR3 2001)• How strongly does ENSO influence precipitation?

Langenbrunner B, Neelin JD (2013) Analyzing ENSO Teleconnections in CMIP Models as a Measure of Model Fidelity in Simulating Precipitation. J Climate 26:4431–4446. doi: 10.1175/JCLI-D-12-00542.1

How strongly do large scale climate modes influence precipitation?

Risbey JS, Pook MJ, McIntosh PC, et al (2009) On the remote drivers of rainfall variability in Australia. Monthly Weather Review 137:3233–3253.

Highest Correlation

How strongly do large scale climate modes influence precipitation?

Risbey JS, Pook MJ, McIntosh PC, et al (2009) On the remote drivers of rainfall variability in Australia. Monthly Weather Review 137:3233–3253.

Highest Correlation Percent variance explained

By what mechanism does ENSO impact Australian Rainfall?

Bureau of Meteorology, Australia

By what mechanism does ENSO impact Australian Rainfall?

Bureau of Meteorology, Australia

Is it the nearby SSTs that matter?

Evans JP, Boyer-Souchet I (2012) Local sea surface temperatures add to extreme precipitation in northeast Australia during La Niña. Geophys Res Lett 39:L10803. doi: 10.1029/2012GL052014

Locally high SSTs responsible for significant proportion of flooding rains in December 2010

Is it the nearby SSTs that matter?

van Rensch P, Gallant AJE, Cai W, Nicholls N (2015) Evidence of local sea surface temperatures overriding the southeast Australian rainfall response to the 1997–1998 El Niño. Geophys Res Lett 42:2015GL066319. doi: 10.1002/2015GL066319

Predictability of PrecipitationLarge Scale Climate Modes

• May be predictable….• Precipitation may care more about nearby

SSTs than distant climate modes• Perhaps: Climate Modes nearby SSTs precipitation

Need to understand key atmospheric vapour flux pathways ???????

Local Land-atmosphere Feedbacks

Local Land-atmosphere Feedbacks

• When wet• Where dry• All relative

Guillod BP, Orlowsky B, Miralles DG, et al (2015) Reconciling spatial and temporal soil moisture effects on afternoon rainfall. Nat Commun. doi: 10.1038/ncomms7443

Local Land-atmosphere Feedbacks

• When wet• Where dry• All relative

What about spatial scale?

What about wind speed & direction?

Taylor CM (2015) Detecting soil moisture impacts on convective initiation in Europe. Geophys Res Lett 42:2015GL064030. doi: 10.1002/2015GL064030

Taylor CM, Gounou A, Guichard F, et al (2011) Frequency of Sahelian storm initiation enhanced over mesoscale soil-moisture patterns. Nature Geosci 4:430–433. doi: 10.1038/ngeo1173

Pielke RA (2001) Influence of the spatial distribution of vegetation and soils on the prediction of cumulus Convective rainfall. Rev Geophys 39:151–177. doi: 10.1029/1999RG000072

Ford TW, Rapp AD, Quiring SM (2014) Does Afternoon Precipitation Occur Preferentially over Dry or Wet Soils in Oklahoma? J Hydrometeor 16:874–888. doi: 10.1175/JHM-D-14-0005.1

Land-atmosphere feedbacks in models

Koster RD, Dirmeyer PA, Guo ZC, et al (2004) Regions of strong coupling between soil moisture and precipitation. Science 305:1138–1140.

Land-atmosphere feedbacks in models

Seneviratne SI, Wilhelm M, Stanelle T, et al (2013) Impact of soil moisture-climate feedbacks on CMIP5 projections: First results from the GLACE-CMIP5 experiment. Geophys Res Lett 40:2013GL057153. doi: 10.1002/grl.50956

Land-atmosphere feedbacks in models

Hirsch AL, Pitman AJ, Seneviratne SI, et al (2014) Summertime maximum and minimum temperature coupling asymmetry over Australia determined using WRF. Geophys Res Lett 41:1546–1552. doi: 10.1002/2013GL059055

Land-atmosphere feedbacks in models

Hohenegger C, Brockhaus P, Bretherton C, Schar C (2009) The Soil Moisture-Precipitation Feedback in Simulations with Explicit and Parameterized Convection. Journal of Climate 22:5003–5020. doi: 10.1175/2009JCLI2604.1

Depends on spatial scale / convection parametrisation

Modelled land-atmosphere coupling strength is poorly constrained.

Model results pertain to the characteristics of that model (at that resolution etc) and are

unlikely to reflect the real world!

Local feedback vs distant influence on drought

• Local soil moisture feedback accounts for ~15% - 50% of drought precipitation deficit

– Hong S-Y, Kalnay E (2000) Role of sea surface temperature and soil-moisture feedback in the 1998 Oklahoma–Texas drought. Nature 408:842–844. doi: 10.1038/35048548

– Wu L, Zhang J (2013) Role of land-atmosphere coupling in summer droughts and floods over eastern China for the 1998 and 1999 cases. Chin Sci Bull 58:3978–3985. doi: 10.1007/s11434-013-5855-6

– Zaitchik BF, Santanello JA, Kumar SV, Peters-Lidard CD (2012) Representation of Soil Moisture Feedbacks during Drought in NASA Unified WRF (NU-WRF). J Hydrometeor 14:360–367. doi: 10.1175/JHM-D-12-069.1

• Local vegetation/albedo/roughness feedback accounts for up to ~30% of drought precipitation deficit

– Meng XH, Evans JP, McCabe MF (2014) The influence of inter-annually varying albedo on regional climate and drought. Clim Dyn 42:787–803. doi: 10.1007/s00382-013-1790-0

– Meng XH, Evans JP, McCabe MF (2014) The Impact of Observed Vegetation Changes on Land–Atmosphere Feedbacks During Drought. J Hydrometeor 15:759–776. doi: 10.1175/JHM-D-13-0130.1

– Zaitchik BF, Santanello JA, Kumar SV, Peters-Lidard CD (2012) Representation of Soil Moisture Feedbacks during Drought in NASA Unified WRF (NU-WRF). J Hydrometeor 14:360–367. doi: 10.1175/JHM-D-12-069.1

– Zaitchik BF, Evans JP, Geerken RA, Smith RB (2007) Climate and vegetation in the Middle East: Interannual variability and drought feedbacks. Journal of Climate 20:3924–3941.

– Ozdogan M, Salvucci GD, Anderson BT (2006) Examination of the Bouchet–Morton Complementary Relationship Using a Mesoscale Climate Model and Observations under a Progressive Irrigation Scenario. J Hydrometeor 7:235–251. doi: 10.1175/JHM485.1

Future of improving hydrologic predictability is in land-atmosphere (possibly ocean)

coupled models.

It is not enough to have good off-line hydrological models – one must understand how the surface affects the evolution of the Planetary Boundary Layer (PBL), and how

this influences precipitation causing mechanisms.

Going forward...• We need to find a way to use observations

to constrain modelled land-atmosphere coupling… to improve our models!

• This coupling needs to be scale & wind direction/speed aware…..

• Getting this right has the potential to yield substantial improvements in hydrologic predictability!

If you want to be a good hydrologistlearn atmospheric science!