Causes and Prediction of Drought/Role of Land Surface Processes

Randall M. DoleNOAA-CIRES Climate Diagnostics Center

Drought in Great Plains, ca. 1935 Fires in the West, 2002

North Platte river, May 22, 2002 Mean flow: 1310 cu. ft/sec Observed flow: 0 cu. ft/sec.

Droughts have profound human and environmental impacts - they are not simply climate phenomena.

Drought characteristics

a. Definition(s)

No unique definition. National Drought Policy Commission:“ a persistent and abnormal moisture deficiency having adverse impacts on vegetation, animals, and people”.

Types:

Meteorological - rainfall deficit (better: P - E ( supply-demand))Agricultural - topsoil moisture deficit; crop impacts.Hydrological - surface or sub-surface water supply shortage.

Typically, meteorological drought onset is first, followed by agricultural, then hydrological, with a similar sequence for recovery.

For more info, see National Drought Mitigation Center website:http://www.drought.unl.edu/index.htm/ .

b. Time scales

Droughts span a broad range of time scales, from short-term “flash droughts” that can have significant agricultural impacts, to multi-year or even decadal droughts (1930s, 1950s, etc.) Paleoclimate evidence suggests that in the last 1000 years parts of the U.S. have experienced “mega-droughts” that persisted for nearly 40 years.

Droughts

c. Drought indices

Numerous drought measures; all have strengths and shortcomings. Some of the most common examples:

• Percent of normal precipitation (problem: non-normal dist.)

• Standardized precipitation index, or SPI (only considers P)

• Crop Moisture Index, CMI - simple water balance, top layer

• Palmer Drought Severity Index (PDSI) - responds slowly

• Deciles or quintiles - lowest quintile “much below normal”, need fairly long, stable climate record.

Examples: Standardized Precipitation Index(June 2002)

SPI local time history(created from WRCC website - 08/02)

Normal

Drought

Severe

Standardized Precipitation IndexNorthern New Mexico

Current 1 2 3 4 65Years before present

Crop Moisture Index (CMI)(from CPC - 24 August 2002)

The CMI is most useful for monitoring short term conditions (e.g., for ag.)

Palmer Drought Severity Index (PDSI)(from CPC - 24 August 2002)

The PDSI is more useful for monitoring long-term, hydrological drought.

Percent area under severe or worse drought in western US and for Contiguous US. Black curve based on 5-month running mean of monthly PDSI values. Red curve shows 5-year running mean values.

PDSI- area coverage of severe drought

Drought Monitor (08/20/2002) - represents synthesis of inputs

Most direct impacts on water supply (and demand), agriculture, fire risk. But manifold indirect impacts as well, e.g., on recreation, energy production, water quality, air quality, ecosystems, endangered species.

Current Drought Monitor (4/22/2003)

Current Crop Moisture Index (for period ending 4/26/2003)

Drought monitoring - some key resources

• Climate Prediction Center - http://www.cpc.ncep.noaa.gov/

• NDMC - http://www.drought.unl.edu/index.htm

• Climate Diagnostics Center - http://www.cdc.noaa.gov/

• Western Region Climate Center - http://wrcc.sage.dri.edu/

Take home points - Drought Monitoring• There is no unique definition of drought, nor is there a “best” drought index - all have strengths and limitations.

• Consider impacts - the “human dimension”.

• Keep in mind the types of drought, and their lag relationships.

• Be wary of calling a premature end to the drought; hydrological impacts may persist well after precipitation has returned to near normal.

• Key factors to monitor in drought include severity, longevity, spatial pattern and scale. Impacts will vary regionally and depending on time of year.

•There are a number of excellent web resources available.

Drought Causes

You are asked, “ What is the cause of this drought?”

And your answer is …

“An unusually persistent upper-level ridge over the region….”

General contributing factors are: anomalous subsidence, changes in horizontal moisture transports, and shifts in the storm tracks.

Persistent upper-level ridges are often identified as the proximate cause of drought conditions. Subsidence occurs downshear of ridge axis. Suppresses precipitation in several ways:

• Adiabatic warming inhibits large-scale condensation• Mid-tropospheric warming produces static stabilization• Low-level divergence inhibits moisture convergence, is frontolytical.

Even relatively weak subsidence can strongly suppress precipitation.

Role of Anomalous Moisture transports:1988 Drought

850 mb Mean Moisture Flux(April - June)

850 mb Anomalous Flux(April-June, 1998)

(from Lyon and Dole, 1995)

Next question:

What is the cause of this unusual flow pattern?

An important diagnostic clue: spatial pattern

The recent drought was part of a larger global pattern

During the same time - persistent tropical SST anomalies

Multi-year La Niña since 1998

Unusual warmthof the “warm pool

Pacific sea surface temperature on the equator, 1998-2002

La Niña Effects on U. S. Precipitation

La Niña Composite (Oct.-May) (created off CDC web site)

La Niña’s effects on precipitation are most robust in SW and SE.

U.S. Oct-May Precipitation and Temp AnomaliesAveraged for the Period 1998-2002

Temperature Precipitation

>+2°F

<-10”<-5”

Simulating the Joint Impacts of Warm Indian Ocean and La Niña U.S. Oct-May Temperature And Precipitation

Temperature Precipitation

>+3°F

<-7”

Idealized experiments to test sensitivity to tropical SSTs(Hoerling and Kumar, 2003)

Observed and AGCM anomalies - specified SSTs(Hoerling and Kumar, 2003)

Observed Temperature and Precipitation anomalies(June 1998 - May 2002)

Model-simulated Temperature and Precipitation Anomalies given observed SSTs over this period

Other factors

• Random component - “droughts happen”.

• Land surface/veg. feedbacks, esp. for drought - heat waves

• Other oceans

• Other large scale modes of variability

• Solar variations

Understanding the links between “climate” and “weather” (precipitation) variability is vital to identifying the causes of droughts.

Causes of drought - key web resources

• Climate Prediction Center - http://www.cpc.ncep.noaa.gov/ -- See expert assessments, discussions, long-lead briefings, etc.

• Climate Diagnostics Center - http://www.cdc.noaa.gov/

-- Applications of diagnostic tools for interpretations.

Take home points - Drought Causes

• To understand droughts, it is vital to know the processes that produce precipitation and how they are influenced by climate variability. This will vary regionally and seasonally.

• Time scales and spatial patterns provide important clues on drought causes.

• Several factors are likely contribute to severe and sustained droughts, such as tropical SST forcing, land surface processes, etc. Major factors contributing to the 1998-2002 drought were the persistent La Niña conditions and a record warm Indian Ocean.

Drought Prediction

For operational purposes, the drought prediction problem is to forecast the probability distribution of some quantitative drought measure or index over a given region and time period.

Relationship to other operational products:

• Very short term - apply weekly outlooks and hazards assessments.

• CMI responds relatively rapidly - weekly to monthly forecasts relevant.

• PDSI responds slowly - monthly to seasonal and longer - seasonal forecasts.

• Consider T forecasts as well as P forecasts.

Key point: Climate forecasts are always probabilistic.

Model-derived Seasonal Precipitation probabilities for New Mexico

The model results illustrate how “wet” La Niña conditions or “dry” El Niño conditions in New Mexico are both possible, but unlikely.

El Niño

La Niña

Empirical estimates of changes in risks of seasonal precipitation extremes

March-May(constructed from CDC website:

http://www.cdc.noaa.gov/Climaterisks/)

El Niño La Niña

Drought OutlookThe current drought outlook (Climate Prediction Center):

Drought prediction - key resources

• Climate Prediction Center - http://www.cpc.ncep.noaa.gov/Hazards assessment, monthly and seasonal forecasts, drought outlooks, experimental prediction of PDSI, etc.

• International Research Institute for Climate Prediction (IRI) - http://iri.ldeo.columbia.edu/

• Climate Diagnostics Center - http://www.cdc.noaa.gov/

Experimental forecast products week two to multi-season, model-intercomparisons, ENSO-extreme event risks, etc.

Drought prediction - take home points

• Climate forecasts are intrinsically probability forecasts.

• Beyond a few weeks, the major source for predictive skill is related to changes in the distribution of tropical heating, particularly over the Pacific and Indian Oceans. Most models do not simulate this well.

• Users are interested in weather/climate information and predictions across a broad range of time scales. For up to a few weeks out, use model forecasts (consider ensembles, not just control) plus CPC’s hazard assessment product; CMI (ag. impacts) responds on these time scales.

•For longer-term conditions, use Drought Outlook and monthly and seasonal forecasts. Look at IRI and CDC’s experimental forecast pages to compare similarities and differences among seasonal forecast models.

Part II: Land Surface effects• Why consider?

• Like SSTs, LS has a “memory” beyond synoptic scales• After SSTs, it is most likely source for seasonal climate predictability.

• Approach

• Background• Influence on T• Influence on P• NCEP products

Background

• Land surface effects involve physical and biological processes.

(courtesy T. Warner)

Land Surface Memory

• Memory resides mainly in soil moisture:

-40

-30

-20

-10

0

10

correlation (X100)

0 1 2 3 4 5 6 7 8 9 1011 1213target month

lag 1 lag 2

Month-to-Month CorrelationSoil Moist to Temperature US 1932-2000

Lag correlation: SM leads T(1932 - 2000)

SM spectra vs. depthFrom Wu et al (2002)• Main impacts of SM in summer

• SM memory increases with depth

Regional variations in SM impacts• Soil moisture initialization experiments (Koster and Suarez 2002)

• Spatial extent of impacts larger for T than P• Most significant impacts for U.S. over mid-continent• Regions susceptible to large SM variance, high E, convection

Land surface effects on T

Consider the surface energy balance:

RN = RL + SH + LH + G

Where:

RN is the net downward radiation (SW + LW)RL is the net upwelling LW radiation SH is the upward sensible heat fluxLH is the upward latent heat fluxG is the downward heat transport into soil (usually small)

Assuming RN is nearly constant and G can be neglected, for surface energy balance reductions in LH associated with reduced soil moisture (evapotranspiration, ET) must be compensated by increases in RL + SH through surface warming.

Example: Changes in Evapotranspiration Rates - Southern Plains, Summer 1980 drought

(Lyon and Dole, 1995)

ET decreases during drought. Estimated anomalous heating rates: +1-2 C/ day. Increases likelihood of heat waves. High T’s increase drought impacts (demand side). A corresponding development of cold bias in MOS Tmax forecasts?

Effects of LS on P

- Numerous studies suggest a link between precipitation and anomalous LS conditions, especially for major droughts and floods, such as the 1980 and 1988 droughts and the 1993 floods.

- LS conditions do not initiate droughts. However, they may help to perpetuate drought conditions and certainly increase impacts. They may also bias the likelihood toward drought recurrence.

- Main impacts are likely to be in warm season (convection).

- Mechanisms are elusive: local moisture recycling, non-local effects.

- Non-local effects: T’ induces PV’: flow, moisture transport, convergence,stability change; changes in the elevated mixed layer.

Lag correlations - SM to Precip.

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0

10

20

30

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correlation (X100)

0 1 2 3 4 5 6 7 8 9 10111213target month

lag 1 lag 2

Month-to-Month CorrelationSoil Moist to Precip US 1932-2000

Only very weak positive correlations for P with SM leading. However,area average over U.S. as a whole is somewhat misleading. Main signalis over the central U.S. in summer.

Varying roles of remote and LS processes

Studies of various droughts, and mechanistic experiments suggest that the role of remote and land surface processes varies during the course of drought evolution.

(From Hong and Kalnay, 2002, for 1998 drought)

How is soil moisture predicted at NCEP? 1. Through a Constructed Analogue (CA) method, with initial soil moisture conditions diagnosed from a simple one-dimensional water balance model (Huang et al. 1996)

(From CPC)

2. Through more sophisticated LS modeling, incorporating relevant physical and biological processes - NOAH LS model.

Land surface: key web resources

• Climate Prediction Center - http://www.cpc.ncep.noaa.gov/

Summary - land surface processes• LS processes directly impact weather and climate through the surface heat and moisture budgets. A key connecting variable is evapotranspiration. ET is strongly modulated by soil moisture, which at present is poorly observed and must be estimated via models.

•The strongest and most direct LS impacts are on T, through changes in the surface energy budget. Impacts on P are weaker, and may be due to local moisture recycling or more subtle non-local effects.

• LS climate influences are most evident in warm season, when dynamics are relatively weak. LS processes are likely increase the probability of summer heat waves and may increase the duration of droughts.

• Regions that are characterized by large soil moisture variance, high ET rates, and a dominance of convective precipitation are most likely to be sensitive to LS processes (e.g., much of the southern and central U.S. in summer).

• There is increasing evidence that deep soil moisture may be significant in maintaining multi-year droughts over the Great Plains (e.g., in the 1930s).

The EndThe End