Spatio-Temporal Variability of Spatio-Temporal Variability of the North American Monsoonthe North American Monsoon

Balaji Rajagopalan

Department of Civil, Env. And Arch. Engineering (CEAE) and CIRES

Katrina Grantz, Edie ZagonaCEAE/CADSWES

& Martyn Clark (CIRES)

Introduction

North American Monsoon– Dramatic increase in rainfall from an extremely dry June to a rainy July,

August and September– SW United States and NW Mexico – Shift in winds (from westerlies to southerlies) brings moisture over the land– Warm land + moisture = monsoonal precipitation– Afternoon thunderstorms

Introduction

Significant portion (30-50%) of annual precipitation falls in the summer months in Arizona and New Mexico

Water management perspective: important to predict the strength of the monsoon

– for water supply and environmental planning

Few studies on timing, streamflow, and implications for water management

– important to local communities in the monsoon region

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North American Monsoon Experiment Schematic

Schematic Illustrating the multi-tiered approach of the North American Monsoon Experiment (NAME). The schematic also shows mean (July-September 1979-1995) 925-hPa vector wind and merged satellite estimates and raingauge observations of precipitation (shading) in millimeters.

North American Monsoon Schematic

Past Studies

Spatial and temporal variability depends on:– Location of subtropical jet, topography, SSTs …

Moisture comes from Gulf of California and Gulf of MexicoWet winter Early monsoon onset and vice-versa (Higgins & Shi, 2000)

General negative correlation with previous winter’s precipitation (Gutzler, 2002; Higgins & Shi, 2000) – related to North Pacific SSTs

El Nino weaker/southward monsoon ridge (Castro, 2000)

Increased monsoonal precipitation with increased soil moisture (Toshi, et al. 2003; Small,2000)

Past StudiesHiggins et al. 1998

Composite evolution of the 30-day running mean area average precipitation (units: mm/day) over Arizona and New Mexico for wet monsoons (dotted line), dry monsoons (dot-dashed line) and all (1963-94) monsoons (solid line).

The average date of monsoon onset is July 1 for wet monsoons, July 11 for dry monsoons and July 7 for all monsoons (defined as day 0 in each case).

MotivationSeasonal cycle shifts in Western US hydroclimatology

(Regonda et al. 2005)

Regonda, S., B. Rajagopalan, M. Clark, and J. Pitlick, 2005: Seasonal Regonda, S., B. Rajagopalan, M. Clark, and J. Pitlick, 2005: Seasonal cycle shifts in hydroclimatology over the Western US, cycle shifts in hydroclimatology over the Western US, Journal of ClimateJournal of Climate..

Earlier Flow (#days) Later Flows (#days) Days Vs. Elevation (m)

50% annual flow of water year

Cont’dTemp. spell

(Days)

April 1 SWE(cm)

Winter Precip.(cm)

Annual flows

(100 cfs)

Decrease in quantity Increase in quantity

Motivation

What are the large-scale Ocean/Atmospheric/Land drivers of space-time (timing and amount) variability of N. American Monsoon (interannual / decadal)?Potential Long-lead Precitability?Implications for water management.

Need a systematic investigation of the monsoon rainfall attributes and also the streamflows.

Outline

Study area, dataThe research project– Precipitation diagnostics– Streamflow diagnostics (preliminary

results)

– Water management issues– Incorporating forecasts into water

management

Study Area

Monsoon Region (NAME II): Arizona and New Mexico

Water Management: Gila River– Flows from New Mexico through Arizona,

joins Colorado River at Yuma, Arizona

AZ NM

Data

Precipitation– Monthly climate division data (1948-2004)

• NM (8 divisions), AZ (7 divisions)– Daily NWS co-op data (1948-1999)

• 219 stations across AZ and NM

Temperature, PDSI– Monthly climate division data (1948-2004)

Large-scale climate variables– Monthly NOAA NCEP-NCAR reanalysis data (1948-2004)– SST, precipitable water, geopotential heights, vector winds

Streamflow– Daily HCDN streamflow (1948-1999)– USGS daily/monthly values (1948-2005)

Co-op Stations

The Research Project

Precipitation diagnosticsStreamflow diagnosticsWater management issuesIncorporating forecasts into water management

Precipitation Diagnostics

Monsoon cycleMonsoon rainfallPlausible hypothesisAntecedent land conditionsAntecedent ocean & Atmospheric conditions

Precipitation Diagnostics:Monsoon Cycle

Monsoon ‘timing’ at each co-op station• Calculate Julian day when 10, 25, 50, 75, and 90th percentile of the

monsoon season (July-Sept) precipitation has fallen (each station, each year)

-- Julian day time series for each threshold for each station

• Perform trend analysis on the Julian day time series

10th, 25th, 50th, 75th, and 90th percentiles capture the entire ‘monsoon cycle’ – onset, peak and recedence

Precipitation Diagnostics:Monsoon Cycle

Use Spearman rank correlation to detect trends A nonparametric (distribution-free) correlation statistic

– doesn’t require that data be normally distributed– similar to Pearson’s R, except that the values are converted to ranks

before computing the correlation coefficient.

where D is the difference of the rank numbers. (Spearman, 1904)– gives p-value and slope

Correlatate value with time to get trendResults similar for Pearson’s R

5th Percentile

25th Percentile

50th Percentile

75th Percentile

95th Percentile

TrendsClimatological

Julian day

= later= earlier

•Largest circle > 21 days

•Second largest ~ 15-21 days

•Second smallest ~ 10-15 days

•Smallest circle < 10 days

•Filled circles significant at 90%

< July 19th Jul 20th ~ 29th

Jul 20th ~ Aug 8th Aug 9th ~ Aug 18th Aug 19th ~ 28th > Aug 29th

•Entire Monsoon Cycle Shifted Later•Approx. 10~15 days shift

NM Division 3

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Precipitation Diagnostics:Monsoon Rainfall Amount

Monsoon rainfall amount at each station and climate division

– July, August, September, July - September

Spearman rank correlation to compute the trends

Precipitation Diagnostics:Monsoon Rainfall

Decrease in JulyIncrease in August and September (esp in NM)July- September: NM increase, AZ mixed/decreaseConsistent with the monsoon timing results

= precip

= precip

Precipitation Diagnostics:Rainfall Amount

Co-op station trends similar to climate division trends

Relative circle size indicates the magnitude of the trend (slope): 0.4 (largest circle), 0.3-0.4 (second largest), 0.2-0.3 (second smallest), less than 0.2 (smallest circle).

Precipitation Diagnostics:Monsoon Rainfall

-114 -112 -110 -108 -106 -104

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Shift in Strength of Monsoon- August (5yr moving avg)

Longitude

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Is the trend a steady increase, or jump, or … ?August precipitation over AZ and NM– 5 year moving windowEastern region (NM) gets wetter in the later periodWestern region (Arizona) trend not as distinctShift most apparent after a dry spell in the late 1970s

Precipitation Diagnostics:Monsoon Moisture

Trends in Palmer drought severity index (PDSI) and 850mb precipitable water Corroborate results seen in precipitation (more so with precipitable water)

July August September July-September

Precipitation Diagnostics:Plausible Hypothesis

What is driving the delay in the monsoon cycle?

Hypothesis:

Increased pre-monsoon (antecedent winter/spring) soil moisture longer summer heating to set up the land-ocean gradient delaying the monsoon cycleWetter antecedent winter / spring conditions in southwest driven by increased El Nino Southern Oscillation (ENSO) activity in recent decades

Precipitation Diagnostics:Antecedent Land Conditions

December- May precipitation and PDSI trends Increasing trend in southwest, decreasing trend in northwest– classic ENSO teleconnection pattern

= precip/moisture

= precip/moisture

Precipitation Diagnostics:Antecedent Land Conditions

Relate winter/spring hydroclimate to summer monsoon attributesPrincipal Component Analysis (PCA) to find the dominant modes of variability in summer timing and rainfall

– Leading modes can be thought of as spatial average• Timing PC1: 28% of variance• July precip PC1: 45% of variance • July-Sep precip PC1: 45% of variance

Correlate leading modes with antecedent land conditions

Precipitation Diagnostics:Antecedent Land Conditions

Correlate 50th and 10th percentile timing PC1 with antecedent precipitation/PDSI across western USSignificant positive (negative) correlations in southwest (northwest) Correlations are stronger for the 10th percentile timing PC onset of monsoon more strongly affected by antecedent soil conditions

= negative correlation

= positive correlation

Precipitation Diagnostics:Antecedent Land Conditions

Correlate July and Jul-Sep precipitation PC1 with antecedent precipitation/PDSI across western USSignificant negative (positive) correlations in southwest (northwest) This negative correlation between winter/spring precipitation and monsoon precipitation noted by Gutzler (2000)Correlations are stronger for July PC Early monsoon rainfall more strongly affected by antecedent soil conditions

= negative correlation

= positive correlation

Precipitation Diagnostics:Antecedent Ocean-Atmospheric Conditions

Large-scale Drivers of summer variability

Correlate leading modes (PCs) of monsoon rainfall and timing with antecedent Ocean & Atmospheric variables.

Timing:Antecedent Ocean Conditions

Correlate summer timing PCs with winter/spring SST and Z500Positive correlations in equatorial Pacific- ENSO pattern

– increased SSTs in winter/spring go with increased Julian day (i.e., delayed monsoon)

– Correlations slightly stronger for 10th percentile (onset of monsoon)50th percentile Timing PC 10th percentile Timing PC

Timing:Antecedent Atmospheric Conditions

Correlate summer timing PCs with winter/spring Z500PNA type pattern consistent with SST correlations

50% Timing PC

10% Timing PC

50th percentile Timing PC1 10th percentile Timing PC1

Rainfall amount:Antecedent Ocean Conditions

Correlate summer rainfall PCs with winter/spring SSTsJuly: negative correlations in equatorial Pacific – La Nina pattern

– Decreased SSTs in winter/spring go with increased July precipitation (La Nina typically goes with decreased winter/spring precip -> increased July precipitation

– Correlations flipped forAug, almost no patternfor Sep and Jul-Sep

– Early monsoon precipamount affected by SSTsbut later monsoon may have different drivers

Rainfall amount:Antecedent Ocean Conditions

Correlate monsoon rainfall PCs with winter/spring Z500Results consistent with SST correlationsJuly: PNA type pattern

– Correlations weaker and reversed sign for Aug,Sep and Jul-Sep.

– Early monsoon precipamount affected by pre-monsoon Pacific Oceanand Atmospheric features,but later monsoon may have different drivers

July Aug

Sep Jul-Sep

Rainfall Amount:High-Low Composites

Aug and Sep rainfall extremes impacted by the surrounding Ocean/Atmospheric status

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Winds Z500 SST

Precipitation Diagnostics:Conclusions

Entire Monsoon cycle shifted approx 10~15 days later in recent decadesConsequently, decreased rainfall in July and increase in Aug and SeptIncreased pre-monsoon precip/soil moisture

(driven largely by large-scale Pacific Ocean/Atmospheric features)Leading modes of Monsoon timing and early (July) rainfall strongly related to pre-monsoon Ocean/Atmospheric/Land featuresAug-Sep rainfall driven by local Ocean-Atmospheric conditions

Significant implications for long-lead Monsoon forecast

Proposed Hypothesis

Increased winter/spring wetness requires longer summer heating to set up adequate land-ocean gradient delayed monsoon cycle reduced early Monsoon rainfall.Large scale Ocean-Atmosphere conditions in Winter as main drivers.

LAND(pre-monsoon soil moisture)

Winter SST/ ENSO

MONSOON(timing and amount)

The Research Project

Precipitation diagnosticsStreamflow diagnosticsWater management issuesIncorporating forecasts into water management

Water Management Issues:Basin Selection

Significant summer streamflow componentAffected by large-scale and/or local-scale climate drivers (this is important for forecasting)Water management issues impacted by summertime streamflow (e.g., irrigation, M&I, hydropower, environmental needs)Policies or operations that rely on or could benefit from knowledge of the summer hydroclimateNatural flow data available, either from HCDN data set or computedIdeally, decision support tool already built and in use

Water Management Issues:Gila River

Gila River BasinArizona and New Mexico

Water Management Issues:Gila Basin

Inadequate surface water supplies to meet irrigation, grazing, and mining demandsConjunctive use between surface water and groundwater resourcesWater quality problems due to excessive turbidity, bacteria, total dissolved solids, ammonia and acid mine drainage

– some stretches of the river not useful for irrigation

Planning reservoir releases and diversions (4 major dams) to meet demandsIncrease/ decrease in demands depending on summertime precipitation

– E.g., more precip decreased demand lower priority water user getting water (this can affect planting)

Water Management Issues:Expected Outcomes

Detailed investigation of the Gila River basin – Key management issues (both supply and demand) – Operations and policies

Decision calendar: timeframe of when decisions are made about reservoir releases and diversionsIdentify attributes of the hydroclimate that need to be predicted for improved water resources management

– Forecast variable (precipitation or streamflow)– timing of the forecasted variable (spring values, summer values, or both), – the forecast issue date– amount to be forecasted (seasonal, monthly, etc.)

Preliminary Streamflow Analysis

Water Management Issues:Gila River

Gila River near RedRock, NM

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Significant summer streamflow component – 25% of annual flow comes in July-October

Streamflow Diagnostics:Volume Analysis (summer)

Gila River near Red Rock, NMJuly: decreasing trendAug, Sep, Oct: increasing trend Gila River Near Red Rock, NM

JULY

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Gila River Near Red Rock, NM AUGUST

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Gila River Near Red Rock, NM SEPTEMBER

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Streamflow Diagnostics:Volume Analysis (summer)

San Francisco River at Clifton, AZJuly, Aug: decreasing trendSep, Oct: increasing trend

San Francisco River at Clifton, AZ JULY

y = -0.4539x + 976.79

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San Francisco River at Clifton, AZ AUGUST

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San Francisco River at Clifton, AZ SEPTEMBER

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San Francisco River at Clifton, AZ OCTOBER

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Streamflow Diagnostics:Winter/Spring Flow

Winter/Spring Flows strongly related to winter/spring Pacific SST and Z500 (ENSO/PNA patterns)

Gila River -- Antecedent Flow Relationship

•High Spring flows Low Summer flows

•Consistent with Precipitation results

Streamflow Diagnostics:Precipitation – Streamflow Relationship

Precipitation – streamflow relationship is non-linearHigh rainfall very little infiltration high streamflowStreamflow can be forecast from precipitation.

Streamflow Diagnostics:Methodology

Streamflow stations with significant summer component (due to monsoon rains) ~ approx. 40 in the regionTiming analysis

– Trends in initiation, peak, and recedence of summer streamflowVolume analysis

– Trends in the summer and spring streamflow volumeDetermine the dominant modes of timing and volume variability

– Using PCAIdentify the land/ocean/atmospheric forcings that drive the streamflow variability

– correlate antecedent conditions with the leading modesDetermine the relationship between spring and summer streamflow; precipitation and streamflow in the monsoon season; and the role of subsurface flow

See how well the ‘Precipitation Hypothesis’ holds with streamflowImplications to Water Resources Management

Summary and Conclusions

Antecedent (winter/spring) Pacific Ocean-Atmospheric conditions and the continental (Western US) land conditions have a substantial influence on the following summer monsoon cycle and rainfall amount.

Streamflows in the region too exhibit similar connection

Enhanced prospects of long lead forecast of the monsoon hydroclimatology (i.e., timing, rainfall amount and streamflow)

Future Work

Further understanding the physical mechanisms of the proposed hypothesis via modeling experiments.

Develop hydroclimate forecasting framework incorporating the large-scale climate information.

Evaluate the utility in a water management context.

Acknowledgements

Funding provided by NOAA/GAPP (GEWEX Americas Prediction Project)Grantz, K., B. Rajagopalan, M. Clark, and E. Zagona, Spatio-Temporal Variability of the North American Monsoon (submitted), Journal of Climate, Special issue on the North American Monsoon, 2005.

http://civil.colorado.edu/~balajir/ publications

Questions / Comments ?

Precipitation Diagnostics:Rainfall Amount PCA

Percent of total variance captured by each leading PC of monsoonal precipitation in varying months and regions

State Month variance

NM and AZ July 45% NM and AZ August 53% NM and AZ September 58% NM and AZ July-September 43%

AZ July 80% AZ August 78% AZ September 75% AZ July-September 77% NM July 61% NM August 64% NM September 71% NM July-September 63%