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Long-Range Operational Military Forecasts for Iraq and Afghanistan

Long-Range Operational Military Forecasts for Iraq and Afghanistan. Capt Christopher Hanson, USAF 1Lt Sarah Moss, USAF Naval Postgraduate School Advisors: Prof. Tom Murphree Lt Col Karl Pfeiffer, USAF. Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007

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Long-Range Operational Military Forecasts for Iraq and Afghanistan

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  1. Long-Range Operational Military Forecasts for Iraq and Afghanistan Capt Christopher Hanson, USAF 1Lt Sarah Moss, USAF Naval Postgraduate School Advisors: Prof. Tom Murphree Lt Col Karl Pfeiffer, USAF Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  2. Overview • Motivation • Background • Data and Methods • Results • Recommendations • References Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  3. Motivation • DoD uses long-term mean (LTM) based climatological products almost exclusively • LTM climatology does not adequately account for climate extremes • Recent studies have identified teleconnections between large-scale climate variations and anomalous conditions in regions of DoD interest. • LTM based products are not forecasts but are applied as such • State-of-the-art climate analysis/forecasting techniques exist in the civilian sector; DoD can definitely adopt these techniques/products to military planning and operational forecasting • Some of these techniques are easily adaptable and effective for military use, such as Composite Analysis Forecasting (CAF) Method • Goal of our study: • Apply known teleconnection indices to develop a viable long-range climatological forecast technique for the use of military planners and commanders in southwest Asia (SWA) and other regions • Afghanistan • Iraq Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  4. Background • Researchers have demonstrated predictability of known climate variations on intraseasonal to interannual timescales • El Nino-La Nina (ENLN) • North Atlantic Oscillation (NAO) • The civilian community is on the leading edge of long-range climate forecasting (leads out to 1 yr) • Climate Prediction Center (CPC) • International Research Institute for Climate and Society (IRI) • Most applications are for developed regions (e.g., U.S., Europe, etc.) Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  5. ENLN and SWA • Mariotti and Zeng (2002) and Mariotti et al. (2005) found: • During autumn, SWA tends to receive above normal precip during EN years, and below normal precip during LN periods • The opposite anomalies are observed during winter • During periods of above normal precip there was anomalous onshore moisture flux from the Arabian Sea into SWA • During periods of below normal precip there was anomalous offshore moisture flux • No mechanisms proposed to explain the observed anomalies; they were primarily focused on Europe and the Med • NPS thesis work complete by Vorhees (2006) confirmed and extended the above findings to other climate variations and proposed dynamical mechanisms • These studies did not address all seasons • Nobody has addressed neutral states of NAO, ENLN, etc. Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  6. OND 2004 Pre-Satellite LTM 8.16 mm/day Afghanistan/Pakistan Autumn 2004 Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  7. NAO and SWA • Previous research (Cullen et al. 2002; Trigo et al. 2002) suggests there is a positive correlation between the NAO and SWA precip during the autumn, and a negative correlation during winter • Research focused primarily on Med, not SWA NAO Positive Phase NAO Negative Phase Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu Figures courtesy of: http://www.ldeo.columbia.edu/NAO/].

  8. Data and Methods • Dataset: NCEP/NCAR Reanalysis • Monthly data at a 2.5° x 2.5° resolution • Period of record from 1970-2006 (37 years) • Predictands: • Afghanistan and Iraq precipitation rate • 850hPa (Afghanistan) and sfc temperature (Iraq) for all seasons (JFM, AMJ, JAS, and OND) • Predictors: • ENLN: Used the Nino 3.4 Index to classify years as either EN/Neutral/ LN • NAO: Used CPC NAO index to classify years as positive/negative/neutral • Adapted NOAA CAF Method to provide a seasonal forecast based on: • (1) historical analysis of Afghanistan and Iraq temp and precip during each phase, including neutral phases, of ENLN and NAO • (2) Probabilistic forecasts of ENLN and NAO Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  9. NINO3.4 Index Timeseries (3-month running mean) EN + 0.5 Neutral - 0.5 LN NAO Index Timeseries (3-month running mean) Positive + 0.5 Neutral - 0.5 Negative Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu Figures generated at: http://www.cdc.noaa.gov/Timeseries/

  10. Data and Methods (cont’d) • Why use CAF Method? • Established forecasting method • Potential to automate the procedure • Easy to integrate into DoD weather operations • Why use temperature and surface precipitation rate as forecasted variables? • Very broad impacts on missions • Why investigate ENLN and NAO? • Vorhees (2006) and Mariotti (2005) findings link these climate variations to climate anomalies in SWA • Well documented and understood • Established and maintained monitoring indices Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  11. Select spatial scale (Country, region, state, etc) 1 Select temporal scale (Seasonal, monthly, etc) 2 Select climate/index predictors (ENLN, NAO, etc) 3 Select variable predictands 4 Select data set for Variable predictands 5 Perform Historical Composite Analysis 6 7 Determine statistical significance, if any (≥90%) No Yes 8a 8b Base Forecast on LTM Apply forecast of predictors To develop CAF of predictands Composite Analysis Forecast Method Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu 11

  12. Data and Methods (cont’d) • Used NCEP/NCAR reanalysis to average all grid points and generate seasonal timeseries of predictands: Afghanistan: 29.5N to 39N Iraq: 29N to 37.5N 60E to 75E 38.5E to 48.5E Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu Figures generate at: http://www.cdc.noaa.gov/Timeseries/

  13. JAS OND Pre-Satellite Pre-Satellite Seasonal Timeseries for Afghanistan Surface Precipitation Rate (1948-2007) JFM AMJ Pre-Satellite Pre-Satellite LTM Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu Figures generate at: http://www.cdc.noaa.gov/Timeseries/

  14. Pre-Satellite Pre-Satellite JFM AMJ Pre-Satellite JAS Seasonal Timeseries for Afghanistan 850hPa Temperatures (1948-2007) Pre-Satellite OND Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu Figures generate at: http://www.cdc.noaa.gov/Timeseries/

  15. Pre-Satellite Pre-Satellite Pre-Satellite Pre-Satellite Seasonal Timeseries for Iraq Surface Temperatures (1948-2007) AMJ JFM JAS OND Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu 15 Figures generate at: http://www.cdc.noaa.gov/Timeseries/

  16. Seasonal Timeseries for Iraq Surface Precipitation Rate (1948-2007) JFM AMJ Pre-Satellite Pre-Satellite OND JAS Pre-Satellite Pre-Satellite Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu 16 Figures generate at: http://www.cdc.noaa.gov/Timeseries/

  17. Historical (1970-2006) NAO Composite Analysis of Average Precipitation Rate (mm/day) for Iraq 50.0 40.0 • The example to the right is a composite with no statistical significance • In this example, the LTM is used as the forecast Probability (%) 30.0 20.0 10.0 0.0 Postive Neutral Negative 31.8 25.0 42.9 Above 36.4 37.5 28.6 Near 31.8 37.5 28.6 Below RESULTS: Relative Frequency Distributions (Steps 1-7 of Method) Results w/ statistical significance (Determined in separate analysis) % Occurrence of event category Climate Variation Phase Event Category Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  18. Relative Frequency Distribution of Afghanistan Seasonal 850hPa Temperature Events Phase Event JFM AMJ JAS OND AN 28.6 20 37.5 28.6 + NAO NN 35.7 20 0 14.3 BN 35.7 60 62.5 57.1 AN 27.8 46.2 31.8 47.6 Neutral NN 33.3 30.8 36.4 38.1 BN 38.9 23.1 31.8 14.3 AN 60 0 42.9 11.1 • NAO NN 40 33.3 42.9 27.2 BN 0 66.7 14.3 66.7 Forecasting Method to Apply? LTM CAF CAF CAF 4 1 2 3 NAO Statistically Significant Results • There were more BN temps during this phase. Statistical significance w/ BN temps during +NAO phases (99.7%). • There were more AN temps during this phase. Statistical significance w/ AN temps during Neutral NAO phases (93.4%). • There were fewer AN temps during this phase. Statistical significance w/ AN temps during -NAO phases (92.3%). • There were more BN temps during this phase. Statistical significance w/ BN temps during -NAO phases (96.2%).

  19. Relative Frequency Distribution of Afghanistan Seasonal Surface Precipitation Events Phase Event JFM AMJ JAS OND AN 30.0 77.8 14.3 69.2 EN NN 40.0 11.1 14.3 23.1 BN 40.0 11.1 71.4 7.7 AN 37.5 25 34.8 16.7 Neutral NN 43.8 45 43.5 41.7 BN 18.8 30 21.7 41.7 AN 27.3 0 42.9 8.3 LN NN 18.2 37.5 28.6 41.7 BN 54.2 62.5 28.6 50 Forecasting Method to Apply? CAF CAF CAF CAF 1 2 3 ENLN Statistically Significant Results • There were more AN precip events during EN. Statistical significance associated with AN precipitation observed during EN phases (99.99%). • There were fewer BN precip events during EN. Statistical significance associated with BN precipitation observed during EN phases (98.1%). • There were fewer AN precip events during LN. Statistical significance associated with AN precipitation observed during LN phases (96.9%).

  20. Afghanistan CA Results Summary • Seasonal 850hPa Temperatures • 7 statistically significant NAO signals (All seasons except JFM) • OND registered most statistically significant events (4) • 1 statistically significant ENLN signal (OND only) • Seasonal Precipitation Rates • 4 statistically significant NAO signals (All seasons except JAS) • 9 statistically significant ENLN signals (All seasons) • NAO registered the most statistically significant signals overall (11) as compared to ENLN (10) • NAO registered more statistically significant signals during its negative phase (5 events) • ENLN registered 4 statistically significant events during both the EN and LN phases Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  21. Relative Frequency Distribution of Iraq Seasonal Surface Temperature Events During NAO phases Phase Event JFM AMJ JAS OND + NAO AN 22.7 41.7 8.3 30.0 NN 22.7 16.7 41.7 30.0 BN 54.5 41.7 50.0 40.0 Neutral AN 62.5 25.0 26.7 18.8 NN 37.5 37.5 33.3 43.8 BN 0.0 37.5 40.0 37.5 - NAO AN 28.6 33.3 70.0 54.5 NN 71.4 55.6 30.0 27.3 BN 0.0 11.1 0.0 18.2 Forecasting Method to Apply? CAF LTM CAF CAF 2 1 3 4 NAO Statistically Significant Results • All BN temp events occurred during the positive phase of the NAO. • During neutral NAO phase more AN temp events occurred while • No BN were observed during neutral NAO. • No BN were observed during – NAO The CAF Method was used for JFM, JAS, and OND Iraq seasonal temp forecasts

  22. Relative Frequency Distribution of Iraq Seasonal Precipitation Rate Events During ENLN Phases Phase Event JFM AMJ JAS OND El Nino AN 40.0 55.6 57.1 53.8 NN 30.0 33.3 28.6 30.8 BN 30.0 11.1 14.3 15.4 Neutral AN 31.3 20.0 21.7 8.3 NN 37.5 40.0 39.1 41.7 BN 31.3 40.0 39.1 50.0 La Nina AN 27.3 37.5 42.9 33.3 NN 36.4 25.0 28.6 33.3 BN 36.4 37.5 28.6 33.3 Forecasting Method to Apply? LTM CAF CAF CAF 1 2 ENLN Statistically Significant Results • During EN a high frequency of AN temp occurred 2. During neutral NAO phase very low frequency of AN temp events occurred • The CAF Method was used for AMJ, JAS, and OND Iraq seasonal temp forecasts

  23. Iraq CA Results Summary • Seasonal Surface Temperatures: • 6 SS NAO signals: All seasons except AMJ • All 3 phases of NAO had SS signals in JFM; JFM had the highest SS (All JFM BN temp events occurred during + NAO) • 1 SS ENLN signal: During OND • Seasonal Precipitation Rates: • 2 SS NAO signals: Both during AMJ • 5 SS ENLN signals: All seasons except JFM • All together NAO was associated to 8 SS signals where as ENLN had 6 • - NAO had 4 SS signals; + NAO had 3; Neutral only 1 • Neutral phase of ENLN had 4 SS signals; EN had 2 and LN had 0 Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  24. To develop a CAF for OND: • Determine number of events observed during each phase of the variation during preceding season • Calculate the percentage of time that the phase either stayed the same, changed to neutral or changed to negative • Here, there were 7 pos. events in JAS and all 7 events remained positive. This provides the following TOF: • - 100% chance of EN • - 0% chance of Neutral phase • - 0% chance of LN Phase JAS phase Events OND Phase Events Percentage Positive 7 Positive 7 100.00% Neutral 0 0.00% Negative 0 0.00% Neutral 23 Positive 6 26.09% Neutral 12 52.17% Negative 5 21.74% Negative 7 Positive 0 0.00% Neutral 0 0.00% Negative 7 100.00% 24 Method: Predictor Indices and Forecast • A probabilistic forecast of the variation index is needed • CPC currently provides both a Nino 3.4 index and a Nino 3.4 probabilistic forecast • In the absence of an operational probabilistic forecast of the predictor, one may be able to develop a tendency forecast of the oscillation (TOF) • TOF developed by determining the percentage of time the initial phase of the oscillation either stays the same or changes to another phase in the following season • Example: During the JAS season, the 7 observed EN events (over the period of the data set) remained in the EN phase 100% of the time into OND. Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  25. AN probability forecast = [(AN% for EN) * (EN TOF %)] + [(AN% for Neu)*(Neu TOF %)] + [(AN% for LN)*(LN TOF %)] (55.6% * 100%) + (10% * 0%) + (33.3% * 0%) = 55.6% of AN event • Strong EN and Neutral phase signals • Proceed with CAF for OND OND Precip Rate Composite Analysis for OND 1970-2000 Probability TOF for ENLN in OND 2002: EN: 100%, Neutral: 0%, LN: 0% NN probability forecast = 33.3% of a NN event BN probability forecast = 11.1% of a BN event

  26. Iraq Precip Rate HINDCAST for OCT-NOV-DEC 2002 Below 11% Above 56% Normal 33% Above Normal ≥ 7.97 mm/day Below Normal ≤ 5.89 mm/day *Mean OND Climate Variable = 7.157 mm/day Forecast issued: 01 Oct 2002 Lead time: 0 days • How can a forecaster apply CAF? • Forecaster can provide substantiated statements of how a upcoming season may behave • This information can be used on multiple levels of DoD weather that would utilize a long-range forecast What would this have meant to Military operations at the time? Actual OND 2002 average precip rate: 9.13 mm/day SCENARIO: OND 2002 - On going Operations NORTHERN/SOUTHERN WATCH - Air patrols and continued ISR operations over Iraq - Theater commanders and planners would have been made aware of relatively high potential of for increase precip rate over Iraq. Thus, they could expect increased cloud cover. The increase cloud cover would result in negative effects to ISR and on going flight operations.

  27. Recommendations to DoD • What are the potential requirements? • Implementation • Establish a working relationship between DoD and NOAA/CPC • Allow for capitalization of NOAA’s experience with method • Revise tasking and increase funding to Seasonal Prediction Working Group • Develop semi-automated system to generate similar products to NOAA/CPC products but on a Global/Unified MAJCOM scale. • Refine a verification method (can be automated) • Training Requirements • Suggested: 2 days of instruction and 4 days of application • Automation will decrease training time • Maintenance Requirements • Seasonal verifications and updates Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  28. Recommendations to DoD • Utilizing similar methods with other datasets, large-scale climate variations, timescales and regions • Capitalize on AFCCC vast data resources (station obs, cloud cover reports, ACMES, etc.) • Apply method to IOZM, MJO, etc. • Composite climate variations on other time scales (i.e. monthly, weekly, daily) with time lags • Apply to politically significant regions (Africa, Iran, China, Korea, etc.) • Need for further verification • Preliminary verification results were promising • All Heidke Skill Scores were positive and beat the LTM • Applying a trend adjustment to the same procedures may yield an improved forecast skill • Composite climate variations with operational thresholds instead of the AN,NN,BN • Development of a regional/MAJCOM seasonal composite analysis outlook product similar to the ones developed by IRI and CPC • A regional seasonal outlook based on ISR/Line-of-sight threshold could prove invaluable to theater commanders and planner. • Cost analysis would demonstrate the potential lose/save of resources from a correct/incorrect seasonal forecast • Due to long-range operational time scales of Defense Logistic Agency the METOC community has only a minor role in developing DLA’s operations. CA methods would supply beneficial climate information at desired lead-times. Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  29. Summary • Developed a step-by-step process outlined in flow chart form • Presented potential for CAF method to be integrated into DoD weather operations • Presented statistically significant ENLN and NAO signals on Iraq and Afghanistan temps and precip • Developed TOF to mitigate issue of probabilistic forecasts • Demonstrated CAF usefulness in military operations • Made recommendations for integration into DoD weather operations Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  30. References • Climate Prediction Center (CPC), cited 2006. [Accessed online at http://www.cpc.ncep.noaa.gov]. Accessed March 2006 • Creating a Local Climate Product Using Composite Analysis, cited 2007. [Accessed online at http://www.nws.noaa.gov/om/csd/pds/OpsCourse/PCU4.3Part2.htm]. Accessed March 2007. • Cullen, H.M., A. Kaplan, and P.B. deMenocal, 2002: Impact of the North Atlantic Oscillation on Middle Eastern climate and streamflow. Clim. Change., 55, 315-338. • Earth Systems Research Laboratory (ESRL), cited 2007. [Accessed online at http://www.cdc.noaa.gov/cgi-bin/PublicData/getpage.pl]. Accessed March 2007 • Kalnay, E., and Coauthors, 1996: The NCEP/NCAR 40-year reanalysis project. Bull. Amer. Meteor.Soc., 77, 437-471. • Mariotti, A., J. Ballabrera-Poy, and N. Zeng, 2005: Tropical influence on Euro-Asian autumn rainfall variability. Climate Dyn., 24, 511-521. • Mariotti, A., N. Zeng, and K.-M. Lau, 2002: Euro-Mediterranean rainfall and ENLN—a seasonally varying relationship. Geophys. Res. Lett., 29, 59-1-4. • Murphree, T., 2006: MR3610 Course Module 6: Smart Climatology: Concepts and Products. Department of Meteorology, Naval Postgraduate School, Monterey, California, 46 pp. • North Atlantic Oscillation webage (IRI), cited 2007. [Accessed online at http://www.ldeo.columbia.edu/NAO/]. Accessed March 2007. • Trigo, R. M., T. J. Osborn, J. M. Corte-Real, 2002: The North Atlantic Oscillation influence on Europe: climate impacts and associated physical mechanisms. Clim. Res., 20, 9-17. • Vorhees, D., 2006: The impacts of global scale climate variations on Southwest Asia. M.S. Thesis, Dept. of Meteorology, Naval Postgraduate School Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

  31. Contact Information Capt Christopher Hanson, USAF chanson@gimail.af.mil 1Lt Sarah Moss, USAF sarah_moss6@yahoo.com Prof. Tom Murphree murphree@nps.edu Lt Col Karl Pfeiffer, USAF kdpfeiff@nps.edu Thesis Brief, Capt Chris Hanson and 1Lt Sarah Moss, USAF, March 2007 Naval Postgraduate School, murphree@nps.edu

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