# Presentation: 940833

## Drought Causes,Prediction/LS processes

**Role of Land Surface Processes**

**Randall M. Dole**
**NOAA-CIRES Climate Diagnostics Center**** **

- Drought in Great Plains, ca. 1935

- Fires in the West, 2002

## Drought scene

**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.

**Notes:**

Drought can have profound human and environmental impacts - they are not just “cliimate phenomena”. This complicates the communication of what is or is not a drought.

## Drought characteristics - definitions and types

**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/ .

**Notes:**
A drought -- too many beautiful days, uninterrupted ...

Abnormal is key - what is a July without rainfall in Sacramento? What would a July without rainfall be in Des Moines?

## Drought time scales

**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**

**Notes:**
Droughts span an enormous range of time scales. This suggests that a broad array of physical mechanisms contribute to droughts, from internal atmospheric variability on the shortest time scales, to interactions with oceans and land surface at seasonal-to-interannual and longer time scales. There continues to be evidence for an impact of solar variability on drought frequencies.

## Drought Indices

**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.**** **

## SPI

**Examples: Standardized Precipitation Index**
**(June 2002)**

**Notes:**
Think of the values of the SPI as representing standardized departures; for example, a value of 2 corresponding to a a +2 sigma event. A very low one-month SPI requires that there be almost no rainfall. Short-term SPI’s relate better to CMI (ag. Impacts), while 9-12 month SPI’s correspond better to PDSI (hydrological idrought).

## SPI time series

- (created from WRCC website - 08/02)

**Normal**

**Drought**

**Severe**

**Standardized Precipitation Index**
**Northern New Mexico**

**Current**

**1**

**2**

**3**

**4**

**6**

**5**

- Years before present

**Notes:**
This is both a severe and sustained drought. It has been particularly severe over the past 1-2 years.

## CMI

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

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

**Notes:**
CMI is based on top soil moisture deficiencies - most relevant to ag. Interests. It can respond fairly rapidly to short-term variations in rainfall and temps. May be less relevant for ag. In the West, where much land is irrigated, although may give indications for dryland farming, range land conditions, etc.

## PDSI

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

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

**Notes:**
PDSI considers moisture deficiencies over a deeper soil layer, using a simple water balance model developed in the Midwest. It therefore may not be appropriate for other regions, although it is generally indicative of prolonged drought conditions, usually with hydrological impacts.

## PDSI time series -area coverage

- PDSI- area coverage of severe drought

**Notes:**
One of the valuable aspectts of the PDSI is that it has been calculated or reconstructed over long time periods.. Hence, it is a useful measure of the relative magnitude and duration of long-term hydrological drought. For the western U.S., the current extent of areal coverage of severe or greater drought is greater than at any time since the dust bowl years. Despite concurrent severe drought in the SE U.S., the areal coverage for the U.S. as a whole has been reached before (e..g., at the maximum extent of the 1988 drought and in the 1950s), and was substantially greater during the dust bowl period.

## U.S. Drought Monitor.

**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.**
**	**

**Notes:**
This is a large-scale product - it may not apply to Farmer Jones lower 40. There is a drought monitor “exploder site” for communicating local impacts, which factors into decisions on categories. You can join too (and double your email volume!)

## U.S. Drought Monitor - current.

**(4/22/2003)**

**	**
**	**

**Notes:**
This is a large-scale product - it may not apply to Farmer Jones lower 40. There is a drought monitor “exploder site” for communicating local impacts, which factors into decisions on categories. You can join too (and double your email volume!)

## Current CMI

**(for period ending 4/26/2003)**

**	**
**	**

**Notes:**
This is a shorter-term product. It has most relevance to agriculture, an probably links more directly to public perceptions of drought.

## Monitoring -Key web resources.

**	**

- Monitoring -Key web 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 - 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 - intro.

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

**And your answer is ...**

**Notes:**
Oh sage weather guru, we humbly beseech, you tell us what is the cause of this drought?

## Anticyclone effects

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

**G****eneral 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.**

**Notes:**
Not a bad answer, for the proximate cause. The bigger question (thankfully, not often asked!) is what is the cause of this unusually persistent high pressure system?

## Role of Moisture transports

**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)

**Notes:**
The anomalous moisture flux almost directly opposes the mean moisture flux from the Gulf of Mexico into the continental interior. The anomalous flux is related to an anticyclonic circulation anomaly centered near the Great Lakes and an anomalous trough offshore . The long-term balance is approximately moisture convergence = P - E; if E is approximately constant, then decreased moisture convergence will directly correspond to decreased P.

## What causes the anticyclone?

**Next question:**

- What is the cause of this unusual flow pattern?

**Notes:**
The bigger question (thankfully, not often asked!) is what is the cause of this unusually persistent high pressure system? But enquiring minds want to know ...

## Drought causes - spatial patterns

**The recent drought was part of a larger global pattern**

**Notes:**
The U.S. drought appears as part of a larger pattern. Note the strong suppression of precipitation in the central and eastern equatorial Pacific, a signature of La Niña conditions, and enhanced rainfall over the western Pacific.. Note also the drought across much of SW Asia, including Afghanistan. It is clear that both have had major consequences for the U.S.

## Perfect Ocean for Drought

**During the same time - persistent tropical SST anomalies**

***Multi-year La Niña ***
***       ******since 1998***

-  ***Unusual warmth***
***of the “warm pool***

**Pacific sea surface temperature on the equator, 1998-2002**

**Notes:**
There are two key features: 1) sustained warming in the west Pacific/Indian Ocean, which is part of a long-term trend in this region, and may be related to anthropogenic warming, and 2) a prolonged La Niña event in the central and eastern Pacific, which finally terminated in the past year, with a weak-moderate El Niño event now developing. A few clues:
1. Sustained - suggests persistent forcing; app. Onset 4 years ago (1998).
2. Large scale - origins likely to be non-local (droughts can produce feedbacks that are self-perpetuating).
3. Pattern - strongly suggestive of a tropical forcing component.
Consider the role of the tropical oceans in producing this drought.

## La Nina impacts

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

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

**Notes:**
This is an empirical composite, based on 7 La Niña events. La Niña events make drought more likely in the SW and SE U.S. in the cold season.

## Oct.-May P-T anomalies

**	****Averaged for the Period 1998-2002**

-              **Temperature  ****	****	****	****Precipitation**

**>+2°F**

**<-10”**

**<-5”**

**Notes:**
Over this period, temperatures have been above normal over almost the entire U.S., despite La Niña conditions. There have been sustained, exceptionally dry conditions over much of the West and east coast - part, but not all of this, resembles a signal we would expect to see from La Niña conditions.

## Simulating Joint Impacts 

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

** ****Temperature                                         Precipitation**

**>+3°F**

**<-7”**

- Simulating Joint Impacts

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

**Notes:**
Based on CCM3 ensemble runs with 20 members, using positive anomaly of 1C in the Indian Ocean centered on the equator, and negative 1C over the east Pacific (canonical La Nina pattern, as identified by first PC).

## 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**

**Notes:**
Departure from usual practice - tackling an ongoing climate event - the widespread drought that encompassed large portions of the NH midlatitudes, including much of the U.S, the Mediterranean, southern Europe and SW and central Asia. 

Used 51 model simulations with specified SST - used ECHAM model from IRI (24) , NCEP climate model (18), and NASA NSIPP model (9).

Results make a compelling case that the unusual SST conditions of the tropical oceans, particularly the prolonged La Nina event and unusually warm SSTs in the western Pacific - Indian ocean warm pool region, played a fundamental role in producing the severe climate anomalies experienced during this period.

Some other key points regarding decadal research:
There is a long-term tendency toward warming of the global oceans, but the spatial distribution is quite non-uniform. This spatial structure can have huge implications for regional climate variability.

A significant component of the observed trends in the extratropical atmospheric circulation is due to trends in the global oceans.

Additional predictability likely exists through various mechanisms (e.g., atmospheric bridge; recurrence mechanisms).

Results indicate that global ocean observations (subsurface, Indian Ocean) and climate models that can replicate details of SST patterns will be necessary to enhance NOAA’s capability for regional climate predictions or projections.

## 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.

**	**

- 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 - Causes of Drought

**	**

** ****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 predictions - basis

**	**

**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.**

**Notes:**
Note that there are users who are interested in all of these time scales. Short-term - fire risks, CMI - ag., longer term, water supply.
T effects evapotranspiration and water demand.

## El Nino vs. La Nina

**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**

**Notes:**
This is one look toward a future product. Although the distributions are generated from large ensembles of model runs, together with statistical resampling techniques, their characteristics agree quite well with the observed behavior in these and other ENSO cases.

## Extreme event risks

**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**

**Notes:**
This is another experimental guidance product, this time derived from an extended data record, and calculated based on the number of extreme events occurring with observed El Nino and La Nina. Note the general consistency between the model results for New Mexico, shown on the previous slide, with the empirical results. The above were computed from 100 years of Climate Division data, with the 20 strongest El Nino and 20 strongest La Nina events. “Extremes” are defined here as much above or much below normal seasons, corresponding to the top and bottom 20% of the seasonal precipitation distributions, respectively.

## Drought  predictions - outlook

**The  current drought outlook (Climate Prediction Center):**

**Notes:**
Forecasts are made by considering a variety of factors, including empirical and model predictions.

## Causes of drought -Key web resources.

**	**

- Causes of drought -Key web 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.

## 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.**
**	**

## Land Surface Intro.

** ****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**
**	**

**Notes:**
Duststorm during 1930s drought.

## Land Surface Complexity

** **** ****Land surface effects involve physical and biological processes.**
**	**

- (courtesy T. Warner)

**Notes:**
The complexity is greater than for SST - depends in important ways on soild conditions, character of vegetation, even human influences.

## Land Surface Memory

** **** ****Memory resides mainly in soil moisture:**
**	**

- Lag correlation: SM leads T
- (1932 - 2000)

- SM spectra vs. depth
- From Wu et al (2002)

-  Main impacts of SM in summer
-  SM memory increases with depth

**Notes:**
 Temperature is negatively correlated with soil moisture, with peak negative values in July and August. The effects vary regionally. Strongest impacts are in the midsection of the country. Because deep soil moisture is effectively an integrator of precipitation, it has a much stronger low-frequency response than precipitation itself.

## LS impacts vary regionally

** **** ****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**

**Notes:**
Regions favored for a significant response tend to have large soil moisture variability, high evaporation (or potential evaporation) rates, I.e., abundant sunshine, and a dominance of convective over stable precipitation processes. This includes most of the midsection of the U.S.

## Physical processes - T

**Consider the surface energy balance:**
**	****	*****R******N******  = R******L******  +  SH  +  LH  +  G***
**Where:**
***R******N***** is the net downward radiation (SW + LW)**
***R******L***** is the net upwelling LW radiation **
***SH***** is the upward sensible heat flux**
***LH***** is the upward latent heat flux**
***G***** is the downward heat transport into soil (usually small)**
- Assuming *R**N* 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** *****R******L***** + *****SH  through surface warming.***

## Role of land surface processes

**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? **

**Notes:**
Initially ET between case and control are comparable. ET in drought case becomes much lower as drought continues, soil dries and vegetation is increasingly stressed.

## Role of land surface processes - 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.

**Notes:**
Effects of LS are more problematic and harder to detect for P than for T. Local moisture recycling is most likely in regions of weak flow, where advective time scales are long; e.g., southern plains in mid to late summer.

## Lag correlations - w to P

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

**Notes:**
Cf. The earlier slide from Koster and Suarez.

## Varying effects of remote forcing during drought

-  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)

**Notes:**
Local moisture recycling most likely in regions of weak flow, where advective time scales are long; e.g., southern plains in mid to late summer.

## How is soil moisture predicted?

-  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)

**Notes:**
Soil moisture is not directly observed, so it must be diagnosed from other data; I.e., P, T. The key parameters for this “leaky bucket” model were estimated using Oklahoma data alone and applied to the entire model domain

## How is soil moisture predicted: NOAH model

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

**Notes:**
NOAH grid is 1/8 degree resolution over North America. It includes dominant and subdominant vegetation classes (13 types) and uses high resolution analyses of soil characteristics . A 51-year retrospective LDAS reanalysis has been performed using as input air temperature, humidity, surface pressure, wind, surface downward shortwave radiation and surface downward longwave radiation from the NCEP/NCAR global reanalysis together with an improved precipitation data set from CPC.

## LS -Key web resources.

**	**

- LS -Key web resources.

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

## Take home points - LS 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 End

***The End***