1. 1 Urban Climate Studies: applications for weather, air quality, and climate change Prof. Robert Bornstein Dept. of Meteorology San Jose State University San Jose, CA USA pblmodel@hotmail.com Presented at Tel Aviv University 8 Jan 2009 Funding sources: USAID-MERC, TCEQ, NASA, NSF, SCU

2. 2 OVERVIEW URBAN CLIMATE URBAN CLIMATE –WHY STUDY IT –ITS CAUSES –ITS IMPACTS CALIFORNIA COASTAL COOLING CALIFORNIA COASTAL COOLING –DATA –ANALYSIS URBAN ATMOSPHERIC MODELS URBAN ATMOSPHERIC MODELS –FORMULATION –APPLICATIONS (HOUSTON, ATLANTA, ISRAEL) FUTURE EFFORTS FUTURE EFFORTS

3. 3 URBAN WEATHER ELEMENTS: battles between conflicting effects VISIBILTITY: decreased VISIBILTITY: decreased TURBULENCE: increased (mechanical & thermal) TURBULENCE: increased (mechanical & thermal) PBL NIGHT STABILITY: neutral PBL NIGHT STABILITY: neutral FRONTS (synoptic & sea breeze): slowed FRONTS (synoptic & sea breeze): slowed TEMP: increased (UHI) or decreased TEMP: increased (UHI) or decreased PRECIP: increased (UHI) or decreased PRECIP: increased (UHI) or decreased WIND SPEED (V): increased or decreased WIND SPEED (V): increased or decreased WIND DIRECTION: con- or divergence WIND DIRECTION: con- or divergence THUNDERSTORMS: triggered or split THUNDERSTORMS: triggered or split

4. 4 HUMAN-HEALTH IMPACTS OF URBAN CLIMATE > UHI  THERMAL STRESS > UHI  THERMAL STRESS > PRECIP ENHANCEMENT  FLOODS > PRECIP ENHANCEMENT  FLOODS > URBAN INDUCED INVERSIONS  > URBAN INDUCED INVERSIONS  POLLUTED LAYERS > TRANSPORT & DIFF PATTERNS FOR > TRANSPORT & DIFF PATTERNS FOR –POLLUTION EPISODES –EMERGENCY RESPONSE (i.e., TOXIC RELEASES)

5. 5 NEW URBAN CLIMATE: CAUSES GRASS & SOIL  GRASS & SOIL  CONCRETE & BUILDINGS  ALTERED SURFACE HEAT FLUXES FOSSIL FUEL CONSUMPTION  FOSSIL FUEL CONSUMPTION  ATMOSPHERIC POLLUTION AND HEAT ATM POLLUTION  ATM POLLUTION  ALTERED SOLAR & IR ENERGY

6. 6 St. Louis nocturnal PBL: warm near-neutral, polluted urban-plume vs. rural stable surface-inversion urban-plume Clark & McElroy (1970): inversion F0FF0F T T min T T max

7. 7 Urban effects on wind speed FAST LARGE-SCALE (i.e., SYNOPTIC) SPEEDS  FAST LARGE-SCALE (i.e., SYNOPTIC) SPEEDS  SMALL UHI  URBAN SFC ROUGHNESS (Z 0 ) INDUCED DECELERATION SLOW SYNOPTIC SPEEDS SLOW SYNOPTIC SPEEDS LARGE UHI  INWARD-DIRECTED ACCELERATION CRITICAL SPEED ~ 3-4 m/s (FOR NYC & London) CRITICAL SPEED ~ 3-4 m/s (FOR NYC & London)

8. 8 NYC DAYTIME ∆V (z) urban rural

9. 9 URBAN EFFECTS ON WIND DIR FAST SYNOPTIC SPEED  WEAK UHI  FAST SYNOPTIC SPEED  WEAK UHI  URBAN BUILDING-BARRIER EFFECT  FLOW DIVERGES AROUND CITY SLOW SYNOPTIC SPEED  LARGE UHI  LOW-p  CONVERGENCE INTO CITY SLOW SYNOPTIC SPEED  LARGE UHI  LOW-p  CONVERGENCE INTO CITY MODERATE SYNOPTIC SPEED  CONVERGENCE-ZONE ADVECTED TO DOWNWIND URBAN-EDGE MODERATE SYNOPTIC SPEED  CONVERGENCE-ZONE ADVECTED TO DOWNWIND URBAN-EDGE

10. 10 NOCTURNAL UHI-INDUCED SFC-CONFLUENCE: otherwise-calm synoptic flow  confluence-center over urban center of Frankfurt, Germany

11. 11 Weak cold-frontal (N to S) passage over NYC Weak cold-frontal (N to S) passage over NYC a.Hourly positions (left) b.At 0800 EST (right): T, q, & SO z-profile-changes b.At 0800 EST (right): T, q, & SO 2 z-profile-changes showed lowest 250 m of atm not-replaced, as front “jumped” over city See 

12. 12 URBAN IMPACTS ON PRECIP INITATION BY THERMODYNAMICS (at SJSU) INITATION BY THERMODYNAMICS (at SJSU) –LIFTING FROM UHI CONVERGENCE UHI CONVERGENCE THERMAL & MECHANICAL CONVECTION THERMAL & MECHANICAL CONVECTION –DIVERGENCE FROM BUILDING BARRIER EFFECT AEROSOL MICROPHYSICS AEROSOL MICROPHYSICS –SLOWER SECONDARY DOWNWIND ROLE –METROMEX & PROF. D. ROSENFELD (HUJI)

13. 13 NYC two-summer daytime-average thunderstorm-precip radar-echoes (σ’s from uniform-distribution) for cases: all, convective, & moving splittingcase Formed over city Split by city

14. 14 Dispersion effects Vertical diffusion limited by urban-induced Vertical diffusion limited by urban-induced elevated inversions (next slide) Transport: 3-D effects of urban-induced flow- modifications Transport: 3-D effects of urban-induced flow- modifications Convergence-zones effects due to Convergence-zones effects due to –Urban effects –Sea breezes

15. 15  Urban-induced nocturnal elevated inversion-I traps home-heating emissions  Power plant plume is trapped b/t urban-induced inversions I & II  Inversion III is regional inversion  poor estimate of mixing depth Home-heating Sources Plume

16. 16 California Coastal-Cooling (to appear, J. of Climate, 2009) Global & CA observations generally show Global & CA observations generally show –asymmetric warming (more warming for T min than for T max ) (next graph) –acceleration since mid-1970s CA downscaled global-modeling (next map) CA downscaled global-modeling (next map) –done onto 10 km grids –shows summer warming that decreases towards the coast (but does not show coastal cooling)

17. 17 Not much change from mid- 40s to mid-70s, when values started to again rapidly rise

18. 18 Statistically down-scaled (Prof. Maurer, SCU) 1950-2000 Summer (JJA) IPCC temp-changes ( 0 C) show warming rates that decrease towards coast; red dots are COOP sites used in present study & boxes are study sub-areas

19. 19 Current Hypothesis INCREASED GHG-INDUCED INLAND TEMPS  INCREASED (COAST TO INLAND) PRESSURE & TEMP GRADIENTS  INCREASED SEA BREEZE FREQ, INTENSITY, PENETRATION, &/OR DURATION  COASTAL AREAS SHOULD SHOW COOLING SUMMER DAYTIME MAX TEMPS (i.e., A REVERSE REACTION) NOTE: NOT A TOTALLY ORIGINAL IDEA 

20. 20 Results 1: SoCAB 1970-2005 summer (JJA) T max warming/ cooling trends ( 0 C/decade); solid, crossed, & open circles show stat p-values < 0.01, 0.05, & not significant, respectively ? ? ?

21. 21 Results 2: SFBA & CV 1970-2005 JJA T max warming/cooling trends ( 0 C/decade), as in previous figure ? ? ?

22. 22 LOWER TRENDS FROM 1950- 70 (EXCEPT FOR TLOWER TRENDS FROM 1950- 70 (EXCEPT FOR T MAX ) Curve b: THAD FASTEST RISE (AS EXPECTED)Curve b: T MIN HAD FASTEST RISE (AS EXPECTED) Curve c: TCurve c: T MAX HAD SLOWEST RISE; IT IS A SMALL-∆ B/T BIG POS VALUE & BIG NEG-VALUE (AS IN ABOVE 2 GRAPHS) CURVE a: THUS ROSE AT MID RATECURVE a: T AVE THUS ROSE AT MID RATE Curve d: DTR (diurnal temp range) THUSCurve d: DTR (diurnal temp range) THUS DECREASED (AS T FALLS & T RISES) DECREASED (AS T MAX FALLS & T MIN RISES) Results 3: JJA Temp trends; all CA-sites

23. 23 Significance of these all-CA Trends HIGHER TRENDS FROM 1970-2005 HIGHER TRENDS FROM 1970-2005  FOCUS NEEDED ON THIS PERIOD THAS FASTER RISE  T MIN HAS FASTER RISE  ASSYMETRIC WARMING IN LITERATURE BUT T BUT T MAX HAS SLOWER RISE, BECAUSE IT IS A SMALL DIFFERENCE B/T BIG POS-VALUE & BIG NEG-VALUE (AS SEEN IN ABOVE SPATIAL PLOTS) DTR ARE ALSO THUS “CONTAMINATED” T AVE & DTR ARE ALSO THUS “CONTAMINATED” NEXT 2 SLIDES THUS SHOW SEPARATE TRENDS FOR CA COASTAL AND INLAND AREAS NEXT 2 SLIDES THUS SHOW SEPARATE TRENDS FOR CA COASTAL AND INLAND AREAS

24. 24 Result 4: JJA T, T, T, & DTR TRENDS FOR Result 4: JJA T ave, T min, T max, & DTR TRENDS FOR INLAND-WARMING SITES OF SoCAB & SFBA Curve b: T (EXPECTED) Curve b: T MIN INCREASED (EXPECTED) Curve c: T (UNEXPECTED), COULD BE DUE TO INCREASED UHIs OR INCREASED DOWN- SLOPE FLOWS Curve c: T MAX HAD FAST RISE; (UNEXPECTED), COULD BE DUE TO INCREASED UHIs OR INCREASED DOWN- SLOPE FLOWS CURVE a: THUS ROSE AT MID RATE CURVE a: T AVE THUS ROSE AT MID RATE Curve d: DTR THUS INCREASED (AS T FASTER THAN T ROSE Curve d: DTR THUS INCREASED (AS T MAX ROSE FASTER THAN T MIN ROSE b c a d

25. 25 Result 5: JJA T, T, T, & DTR TRENDS FOR Result 5: JJA T ave, T min, T max, & DTR TRENDS FOR COASTAL-COOLING SITES OF SoCAB & SFBA Curve b: TROSE (EXPECTED) Curve b: T MIN ROSE (EXPECTED) Curve c: T (UNEXPECTED MAJOR RESULT OF STUDY) Curve c: T MAX HAD COOL- ING (UNEXPECTED MAJOR RESULT OF STUDY) CURVE a: AS RISING T & FALLING T CURVE a: T AVE THUS SHOWED ALMOST NO CHANGE, AS FOUND IN LIT.), AS RISING T min & FALLING T max CHANGES ALMOST CANCELLED OUT Curve d: DTR THUS DE- CREASED, AS T T FELL Curve d: DTR THUS DE- CREASED, AS T MIN ROSE & T MAX FELL a b c d

26. 26 Note IPCC 2001 does show cooling over Central California!!

27. 27 Significance of above Coastal-Cooling and Inland-Warming trends CA ASSYMETRIC WARMING IN LITERATURE IS HEREIN SHOWN TO BE DUE TO T CONCURRENT T CA ASSYMETRIC WARMING IN LITERATURE IS HEREIN SHOWN TO BE DUE TO COOLING T MAX IN COASTAL AREAS & CONCURRENT WARMING T MAX IN INLAND AREAS PREVIOUS CA STUDIES PREVIOUS CA STUDIES –DID NOT LOOK SPECIFICALLY AT SUMMER DAYTIME COASTAL VS. INLAND VALUES HAVE –THEY THUS REPORTED CONTAMINATED T, T, & DTR VALUES –THEY THUS REPORTED CONTAMINATED T MAX, T AVE, & DTR VALUES –THEY, HOWEVER, ARE NOT INCONSISTENT WITH CURRENT RESULTS, THEY ARE JUST NOT AS DETAILED IN THEIR ANALYSES & RESULTS

28. 28 Result 6. JJA 1970-2005 2 m T max trends for 4 pairs of urban (red, solid) & rural (blue, dashed) sites Notes: 1.All sites are near the cooling-warming border 2.UHI-TREND (K/DECADE) = absolute sum b/t warming-urban & cooling-rural trends a. SFBA sites a. SFBA sites > Stockton > Stockton (0.38 + 0.17 = 0.55) (0.38 + 0.17 = 0.55) > Sac. (0.49) b. SoCAB sites b. SoCAB sites > Pasadena (0.26) > Pasadena (0.26) > S. Ana (0.12) > S. Ana (0.12)

29. 29 Notes on JJA daytime UHI-trend results Faster growing cities (not shown) had faster growing UHIs Faster growing cities (not shown) had faster growing UHIs As no coastal sites showed warming T values, calculations could only be done at these four pairs (at the inland boundary b/t the warming and cooling areas) As no coastal sites showed warming T max values, calculations could only be done at these four pairs (at the inland boundary b/t the warming and cooling areas) Coastal sites would have cooled even more w/o their (assumed) growing UHIs Coastal sites would have cooled even more w/o their (assumed) growing UHIs

30. 30 BENEFICIAL IMPLICATIONS OF COASTAL COOLING NAPA WINE AREAS MAY NOT GO EXTINCT (REALLY GOOD NEWS!) (next map) NAPA WINE AREAS MAY NOT GO EXTINCT (REALLY GOOD NEWS!) (next map) ENERGY FOR COOLING MAY NOT INCREASE AS RAPIDLY AS POPULATION (next graph) ENERGY FOR COOLING MAY NOT INCREASE AS RAPIDLY AS POPULATION (next graph) LOWER HUMAN HEAT-STRESS RATES LOWER HUMAN HEAT-STRESS RATES OZONE CONCENTRATIONS MIGHT CONTINUE TO DECREASE, AS LOWER MAX-TEMPS MEAN REDUCED OZONE CONCENTRATIONS MIGHT CONTINUE TO DECREASE, AS LOWER MAX-TEMPS MEAN REDUCED –ANTHROPOGENIC EMISSIONS –BIOGENIC EMISSIONS –PHOTOLYSIS RATES

31. 31 NAPA WINE AREAS MAY NOT GO EXTINCT DUE TO ALLEGED RISING T VALUES, AS PREDICTED IN NAS STUDY NAPA WINE AREAS MAY NOT GO EXTINCT DUE TO ALLEGED RISING T MAX VALUES, AS PREDICTED IN NAS STUDY

32. 32 Result 7: Peak-Summer Per-capita Electricity-Trends  Down-trend at cooling Coastal: LA (blue) & Pasadena (pink, 8.5%/decade) Up-trend at warming > Up-trend at warming inland Riverside (green) Up-trend at warming Sac & Santa Clara Need:  detailed energy-use data for more sites  to consider changed energy efficiency

33. 33 Future Coastal-Cooling Efforts (PART 1 OF 2) EXPAND (TO ALL OF CA & ISRAEL?) EXPAND (TO ALL OF CA & ISRAEL?) –ANALYSIS OF OBS (IN-SITU & GIS) –URBANIZED MESO-MET (MM5, RAMS, WRF) MODELING SEPARATE INFLUENCES OF CHANGING: SEPARATE INFLUENCES OF CHANGING: – LAND-USE PATTERNS RE AGRICULTURAL IRRIGATION AGRICULTURAL IRRIGATION URBANIZATION & UHI-MAGNITUDE URBANIZATION & UHI-MAGNITUDE –SEA BREEZE: INTENSITY, FREQ, DURATION, &/OR PENETRATION DETERMINE POSSIBLE “SATURATION” OF SEA- BREEZE EFFECTS FROM DETERMINE POSSIBLE “SATURATION” OF SEA- BREEZE EFFECTS FROM FLOW-VELOCITY & COLD-AIR TRANSPORT FLOW-VELOCITY & COLD-AIR TRANSPORT AND/OR STRATUS-CLOUD EFFECTS ON LONG- & SHORT-WAVE RADIATION AND/OR STRATUS-CLOUD EFFECTS ON LONG- & SHORT-WAVE RADIATION

34. 34 POSSIBLE FUTURE EFFORTS (PART 2 OF 2) DETERMINE CUMULATIVE FREQ DISTRIBUTIONS OF T VALUES, AS DETERMINE CUMULATIVE FREQ DISTRIBUTIONS OF T MAX VALUES, AS –EVEN IF AVERAGE T DECREASES, –EVEN IF AVERAGE T MAX DECREASES, –EXTREME VALUES T MAY STILL INCREASE (IN INTENSITY &/OR FREQUENCY) –EXTREME VALUES T MAX MAY STILL INCREASE (IN INTENSITY &/OR FREQUENCY) DETERMINE CHANGES IN LARGE-SCALE ATM FLOWS: DETERMINE CHANGES IN LARGE-SCALE ATM FLOWS: –HOW DO GLOBAL CLIMATE-CHANGE EFFECT POSITION & STRENGTH OF: PACIFIC HIGH & THERMAL LOW? –THESE TYPES OF CLIMATE-CHANGES ARE THE ULTIMATE CAUSES OF TEMP AND PRECIP CHANGES

35. 35 OUR GROUP’S MESO-MODELING EXPERIENCE SJSU (MM5 & uMM5) SJSU (MM5 & uMM5) – –Lozej (1999) MS: SFBA winter wave cyclone – –Craig (2002) MS: Atlanta UHI-initiated thunderstorm (NASA) – –Lebassi (2005) MS: Monterey sea breeze (LBNL) – –Ghidey (2005) MS: SFBA/CV CCOS episode (LBNL) – –Boucouvula (2006a,b) Ph.D.: SCOS96 episode (CARB) – –Balmori (2006) MS: Tx2000 Houston UHI (TECQ) –) –Weinroth (2009) PostDoc: NYC-ER UDS urban-barrier effects (DHS) SCU (uRAMS) SCU (uRAMS) – –Lebassi (2005): Sacramento UHI (SCU) – –Lebassi (2009) Ph.D.: SFBA & SoCAB coastal-cooling (SCU) – –Comarazamy (2009) Ph.D.: San Juan climate-change & UHI (NASA) Altostratus (uMM5 & CAMx) Altostratus (uMM5 & CAMx) – –SoCAB (1996, 2008): UHI & ozone (CEC) – –Houston (2008): UHI & ozone (TECQ) – –Central CA (2008): UHI & ozone (CEC) – –Portland (current): UHI & ozone (NSF) – –Sacramento (current): UHI & ozone (SMAQMD)

36. 36 SJSU IDEAS ON GOOD MESO-MET MODELING MUST CORRECTLY REPRODUCE: –UPPER-LEVEL Synoptic/GC FORCING FIRST: pressure (“the” GC/Synoptic driver)  Synoptic/GC winds –TOPOGRAPHY NEXT: min horiz grid-spacing  flow-channeling –MESO SFC-CONDITIONS LAST: temp (“the” meso-driver) & roughness  meso-winds

37. 37 Case 1: ATLANTA UHI-INITIATED STORM: OBS GOES & PRECIP (UPPER) & MM5 w’s & precip (LOWER)

38. 38 Recent Meso-met Model Urbanizations Need to urbanize momentum, thermo, & TKE Need to urbanize momentum, thermo, & TKE –Surface & SfcBL Diagnostic-Eqs. –PBL Prognostic-Eqs. (not done in NCAR uWRF) Start: veg-canopy model (Yamada 1982) Start: veg-canopy model (Yamada 1982) Veg-param replaced with GIS/RS urban-param/data Veg-param replaced with GIS/RS urban-param/data –Brown and Williams (1998) –Masson (2000) –Martilli et al. (2001) in TVM/URBMET –Dupont, Ching, et al. (2003) in EPA/MM5 –Taha et al. (‘05, ‘08a,b,c) [& Balmori et al. (‘06)]: his uMM5 uses improved urban dynamics, physics, parameterizations, & inputs

39. 39 From EPA uMM5: Mason + Martilli (by Dupont) Within Gayno-Seaman PBL/TKE scheme New

40. 40 But, uMM5 needs extra GIS-derived inputs as f (x, y, z, t)  land-use (38 categories)  roughness heights z 0 (see next slide)  anthropogenic heat  building heights  paved-surface 2-D fractions  building H to W, wall-plan, & impervious-area 2-D ratios  building frontal, plan, & rooftop 3-D area densities

41. 41 S. Stetson: Houston GIS/RS z o inputs Values up to 3 m But, values are too large, as they were f(h) & not f(ơ h ) h = building height

42. 42 uMM5 for Houston: Balmori (2006) Goal: Accurate urban/rural temps & winds for Aug 2000 O 3 episode via –uMM5 –Houston LU/LC & urban morphology parameters –TexAQS2000 field-study data –USFS urban-reforestation scenarios  UHI & O 3 changes

43. 43 uMM5 Simulation period: 22-26 August 2000 Model configuration Model configuration –5 domains: 108, 36, 12, 4, 1 km –(x, y) grid points: (43x53, 55x55, 100x100, 136x151, 133x141 –full-  levels: 29 in D 1-4 & 49 in D-5; lowest ½  level=7 m –2-way feedback in D 1-4 Parameterizations/physics options Parameterizations/physics options > Grell cumulus (D 1-2)> ETA or MRF PBL (D 1-4) > Grell cumulus (D 1-2)> ETA or MRF PBL (D 1-4) > Gayno-Seaman PBL (D-5) > Simple ice moisture, > Gayno-Seaman PBL (D-5) > Simple ice moisture, > urbanization module NOAH LSM > RRTM radiative cooling > urbanization module NOAH LSM > RRTM radiative cooling Inputs Inputs > NNRP Reanalysis fields, ADP obs data > NNRP Reanalysis fields, ADP obs data > Burian morphology from LIDAR building-data in D-5 > Burian morphology from LIDAR building-data in D-5 > LU/LC modifications (from Byun)

44. 44 1-km grid, uMM5 Houston UHI: 8 PM, 21 Aug MM5 UHI (2.0 K) uMM5 UHI (3.5 K) uMM5 UHI (3.5 K) UHI Bay Gulf

45. 45 UHI-Induced Convergence: obs vs. uMM5 Krieged Obs uMM5 output C C C C

46. 46 Base-case (current) veg-cover (0.1’s)  urban min (red)  rural max (green) Modeled changes of veg-cover (0.01’s)  Urban-reforestation (green)  Rural-deforestation (purple) min max increase

47. 47 Run 12 (urban-max reforestation) minus Run 10 (base case)  near-sfc ∆T at 4 PM shows that: reforested central urban-area cools & surrounding deforested rural-areas warm warmer cooler

48. 48 D UHI(t): Base-case minus Runs 15-18 D UHI(t): Base-case minus Runs 15-18 UHI = Urban-Box minus Rural-Box UHI = Urban-Box minus Rural-Box Runs 15-18: Urban re-forestation scenarios Runs 15-18: Urban re-forestation scenarios D UHI = Run-17 UHI minus Run-13 UHI  D UHI = Run-17 UHI minus Run-13 UHI  max effect (green line) Reduced UHI  lower max-O 3 (not shown)  Reduced UHI  lower max-O 3 (not shown)  EPA emission-reduction credits  $ saved  Max-impact of –0.9 K on a 3.5 K noon-UHI, of which 1.5 K was from uMM5 URBAN RURAL

49. 49 RAMS, MM5, & CAMx SIMULATIONS OF MIDDLE-EAST O 3 TRANSBOUNDARY TRANSPORT E. Weinroth 1,2, S. Kasakseh 1,3 M. Luria, R. Bornstein 1 M. Luria 2, R. Bornstein 1 1 San Jose State Univ. 2 Hebrew Univ. Jerusalem, Israel 3 Applied Research Institute Jerusalem (ARIJ), Bethlehem, West Bank In Atmos. Environ. (2008)

50. 50 USAID-MERC project (2000-) Involves scientists from Palestinian Territories, Israel, USA (& now Jordan and Lebanon) Involves scientists from Palestinian Territories, Israel, USA (& now Jordan and Lebanon) Objectives accomplished: Objectives accomplished: – Installation of environmental stations in West Bank & Gaza (and now Jordan & Beirut) – Preparation of environmental databases (SJSU web page) – Field campaigns during periods of poor air quality (Prof. Luria) – Application of numerical models for planning RAMS & MM5 meso-met RAMS & MM5 (Kasakseh 2007) meso-met CAMx photochemical air-quality (Weinroth et al. 2007 in Atmos. Environ.) CAMx photochemical air-quality (Weinroth et al. 2007 in Atmos. Environ.)

51. 51 Flow Dir: weak down-slope off coastal-mountains at  Coastal plain: offshore (to W) from W-facing slopes  Haifa Pen. (square): offshore (to E ) from E-facing slopes  Inland sites: directed inland (to E) from E-facing slopes Low-O 3  generally <40 ppb)  Haifa still at 51 ppb Night obs of sfc flow: 3-AM LST (00 UTC) L L H

52. 52 Winds:  Reversed  Stronger: up 6 m s -1  Coastal plain: Onshore/upwind, from SW  Inland sites: Channeling (from W) in corridor (box; focus of modeling) from Tel-Aviv to J. area (at Modiin site). Higher daytime O 3  max at Mappil, 66 ppb  2 nd max at Modiin, 63 ppb Day Obs: 1200 NOON LST L H H L

53. 53 MM5 Configuration  Version 3.7  3 domains – 15, 5, 1.67 km Grid Spacings – 59 x 61, 55 x 76, 58 x 85 Grid Points  32 σ-levels – up to 100 mb – first full σ-level at 19 m  Lambert-conformal map projection (suitable for mid lat regions)  Two-way nesting  5-layer soil-model  Gayno-Seaman PBL  Simulations – End: 00 UTC, 3 Aug – Start: 00 UTC, 29 July  Single CPU, LINUX

54. 54 MM5 Domain-3 winds (m/s) at 1100 LST on 1 Aug ‘97 red lines = topo heights (m); yellow line = sea breeze front; note reverse upslope-flow & channeling to J. Max Max Sea J.

55. 55 Same, but at 2300 LST; where yellow line = land breeze front; note down-slope flow; still inland directed flow in inland areas & still channeling to J. Max Max Sea J.

56. 56 Mid-east Obs vs. MM5: 2 m temp (Kasakech ’06 AMS) July 29August 1August 2 July 31 Aug 1 Aug2 Standard-MM5 summer night-time min-T, But lower input deep-soil temp  better 2-m T results  better winds  better O 3 obs Run 1 MM5:Run 4 Obs Run 4: Reduced Seep-soil T First 2 days show GC/Syn trend not in MM5, as MM5-runs had no analysis nudging

57. 57 Obs vs. MM5: wind speed (m/s) July 31 August 1August 2 OBS Run 3

58. 58 Jerusalem 0 0-20 20-40 40-60 60-70 70-80 80-90 90-95 95-105 105-120 O 3 ppb 1 Aug, 1500 LST RAMS/CAMx (left) O 3 vs. airborne obs (right) at 300 m: > Secondary-max: over J. in obs; due to coastal N-S highway > Primary-max: in Jordan (no obs); due to Hadera Irbid, Jordan Hadera Power  Plant. Airborne obs

59. 59 Overall Modeling Lessons > Models can’t be > Models can’t be –assumed to be perfect (i.e., model user vs. modeler) –used as black boxes > Need good large-scale forcing model-fields > Need good large-scale forcing model-fields > If obs are not available, OK to make reasonable educated estimates, e.g., for rural > If obs are not available, OK to make reasonable educated estimates, e.g., for rural –deep-soil temp –soil moisture > Need data to compare with simulated-fields > Need data to compare with simulated-fields > Need good urban > Need good urban –morphological data –urbanization schemes

60. 60 Thanks for listening! Questions?