The Climate of Patagonia: Glaciers, Flashes and Shadows

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Chile Deputy Director, Center for Climate and Resilience

1 The Climate of Patagonia: Glaciers, Flashes and Shadows René D. Garreaud Professor, Geophysics Department, Universidad de Chile Deputy Director, Center for Climate and Resilience Research Visiting professor at Yale University New Haven, April 26, 2016

Acknowledgements Faculty, staff and students at Yale Geology and

2 Outline Patagonia 101 Large-scale control of the regional climate Synoptic, orographic and convective precipitation Outlook Acknowledgements Faculty, staff and students at Yale Geology and Geophysics Department, specially Ron Smith, Mark Brandon, Larry Bonneau, Pam Buonocore and Aida Rodriguez.

We all love Patagonia.. Large area, complex terrain Current climate of Patagonia supports glaciers, ice fields, rain forests, and massive rivers in the western side.

3 We all love Patagonia.. Large area, complex terrain Current climate of Patagonia supports glaciers, ice fields, rain forests, and massive rivers in the western side. Biodiversity hotspot Gas, oil and dinosaurs in the eastern side Contemporaneous climate-driven environmental changes Numerous paleorecords (lakes, glaciers, tree-rings) Meteorological data clearly insufficient to address climate change/variability

4 The big picture 0 S Continental Low Level Jet S. Atlantic Anticyclone 30 S SE Pacific Anticyclone Midlat. Precip. Tropical rainfall SCu & Cold SST Midlatitude Storm track 60 S 120 W 90 W 60 W 30 W

5 Annual mean Long term mean zonal wind at 700 hpa (best predictor of precipitation over the extratropical Andes) June-Feb Winter-Summer February June Max at 55S

6 One (typical) storm simulation (WRF) Hourly results during a 3 day period. Resolved precipitation (colors), Convective rainfall (contours) and topography Salient features: Rainfall enhancement over the Andes windward slope, Rain shadow, Convective rainfall along the coast

7 Patagonia 101 Mean Annual Rainfall (everybody guess) Height (max, 90%) 38 S 42 S 46 S m ASL > 1800 > mm <400 mm S >6.000 mm mm/year > S W 72 W 68 W 64 W km

8 850 hpa (1500 m ASL) Wind roses for all days (grey) and rainy days (color) at selected locations in Patagonia Upstream Downstream Atlantic seaboder SE SW SE SW SE SW 50S NW flow NE NW NE NW NE NW U D A

FIG. 4. Local (point-to-point) correlation map between daily precipitation (P) and 850-hPa zonal and meridional wind components (U850; V850) using PRECIS- DGF results from 1980 90.

9 FIG. 4. Local (point-to-point) correlation map between daily precipitation (P) and 850-hPa zonal and meridional wind components (U850; V850) using PRECIS- DGF results from At each grid point the correlation was calculated for the sample of days with P >1 mm. Colors indicate the P U850 correlation. Vectors are constructed using r(p, U850) and r(p,v850) (scale at the bottom) and only shown where absolute value exceeds 0.3.

10 MODIS Vegetation Precipitation gradient leads to other two biophysical contrasts: vegetation and isotopes Rain forest Barren soil Stable isotopes Coastal Leeside Smith and Evans 2007

11 Not much people or weather stations but plenty of Paleo records in the only SH land mass extending into the core of the westerly wind belt Kilian and Lamy 2012

12 Large Scale Control of the Patagonia Climate (Garreaud 2007; Garreaud et al. 2013) The hydroclimate variability in Patagonia can hardly be described on the basis of few in-situ records so we attempt linking local climate variability ( SAT and P) with largescale circulation anomalies (e.g., U aloft ). That will allow: (a) downscale large-scale signals (b) upscale local environmental changes.

13 Co-variability of zonal wind and precipitation Point-to-point correlation between U850 (NNR) and precipitation (CMAP) Both data sets 2.5º 2.5º lat-lon, annual means, Baroclincity Baroclincity + Topography Stronger westerlies / More precipitation Garreaud 2007 PAGES 2006

PRECIS-DGF Simulation Period: 1978-2001 (avail: 1958-2001) Hor.

14 PRECIS-DGF Simulation Period: (avail: ) Hor. Resolution: 25 km Area: Southern South America L x LBC: ERA-40 z L y y BBC: HadISST L z x

15 PRECIS-DGF variability against observations good enough C Puerto Montt (SAT) C 7 6 mm PRECIS-DGF Station data Punta Arenas (SAT) 5 Puerto Montt (Precip) Punta Arenas (Precip) Years

16 Wind-precipitation and Wind-SAT covariability at annual timescale (year-to-year) a. r(u850,p) b. r(u850,sat) (a) R(P,U 850 ) Anual (a) R(T 2m,U 850 ) Anual prescribed SST influences SAT

17 Wind-SAT covariability at annual timescale Strong westerlies cold summer Strong westerlies Warm winter

18 Stability of the Wind-P/SAT relationship IPSL GCM r(u850,p) annual r(u850,sat) summer r(u850,sat) winter LGM Run MC Run

19 Stronger westerlies/more Precip. up to 50 km downstream of the Mnts. SPI Torres del Paine area r(p,u850) 0.6 Annual mean Precipitation [mm] > Punta Arenas Terrain height [m ASL] 1500 r(p,u850) based on PRECIS

for 40-50% of the variance r(pc1,aao) 0.7 (a) DJF -1-0.

20 Leading modes of U850 interannual variability EOF analysis performed each month using NNR & ERA40 First mode accounts for 40-50% of the variance r(pc1,aao) 0.7 (a) DJF r(pc1,aao) 0.2 (b) JJA 0 60 E 120 E W 60 W 0

Downscale the U-P, U-SAT relationships (a) ERA-40 (b) NNR -0.8-0.6-0.4-0.2 +0.2 +0.4 +0.

level using the (a) ERA-40 and (b) NCEP-NCAR reanalysis.

21 Downscale the U-P, U-SAT relationships (a) ERA-40 (b) NNR ms -1 /decade Linear trends in the annual mean zonal wind at the 850 hpa level using the (a) ERA-40 and (b) NCEP-NCAR reanalysis. Shading indicates the change between 1968 and 2001 of a linear least squares trend fit calculated at each grid-box

22 Wind-congruent precipitation trends( ) P* = β U 850 (a) Congruent Precip. Trend [mm/dec] (b) Relative Precip. Trend [%mm/dec]

23 Observed (U.Delaware) Precip trend ( ) Aravena & Luckman 2010

Multimodel average SLP and sfc wind difference betweena2 (2070-2100) and BL (1970-2000) Annual mean

24 Multimodel average SLP and sfc wind difference betweena2 ( ) and BL ( ) Annual mean Over open ocean v in geostrophic balance with SLP. Near the coast v more controlled by along-coast SLP

25 Southern SA Climate Change Projections Towards the end of century under A2 (RCP8.5) Estudio DGF/UCh-CONAMA 2007 empleando PRECIS

26 Evidencia de paleo-incendios en Patagonia Oeste sugiere ocurrencia de rayos en esta región

Cloud electrification 101 Cloud electrification in convective clouds due to elctrostatic induction and depends on microphysical factors (q droplet, q

27 Cloud electrification 101 Cloud electrification in convective clouds due to elctrostatic induction and depends on microphysical factors (q droplet, q ice > 0 in a deep layer between 0 and -40 C) and dynamical factors (strong (w 5 ms -1 ) and sustained updraft in the mix-phased layer). C. Price 2012

28 The Lightning Imaging Sensor (LIS) LIS is on board of the Tropical Rainfall Measuring Mission (TRMM) detecting the discrete optical pulses associated with changes in cloud brightness at each pixel.

29 World Wide Lightning Location Network (WWLLN) It monitors the VLF radio waves (sferics) emitted by lightning and uses a time of group arrival technique to locate lightning strokes within 5 km and <10 µs. Online data available at: Virts et al. 2013

January 6, 2013 1800 UTC GOES-13 Visible (BW) and IR4 (light shading) + WWLLN Lighting (stars) 3 1 2

30 January 6, UTC GOES-13 Visible (BW) and IR4 (light shading) + WWLLN Lighting (stars) Postfrontal air mass with high clouds and non-zero CAPE L Warm front Coldest (highest) clouds in light blue shading

Spatial Distribution (2008-2012) Año 2011 40 S 44

31 Spatial Distribution ( ) Año S 44 S 48 S 52 S m ASL > S 0 78 W 74 W 70 W

Spatial Distribution (2008-2012) Lightning density, 0.1 0.

2 lat-lon boxes 42 S Chiloe 6-7 4-5 2-3 0-1 WMO Thunder days Chiloe 42 S 4.8 10 46 S 3.6 1.

32 Spatial Distribution ( ) Lightning density, lat-lon boxes Number of lightning-days, lat-lon boxes 42 S Chiloe WMO Thunder days Chiloe 42 S S Taitao Peninsula Golfo de Penas >5 NPI 6 2 >4 >10 NPI 46 S 50 S >3.5 SPI Madre de Dios Is. SPI 50 S 54 S m ASL > m ASL > WP Box 54 S W 74 W 70 W 78 W 74 W 70 W

33 Spatial Distribution ( )

34 Lightning distribution Spatial distribution of lightning density and number of days with lightning: clustering at the coastline but some flashed offshore as well. No flashes inland! Small annual/diurnal cycle. Little interannual variability (may be affected by network efficiency) Lightning days cluster in 1-4 day events. Many storms (rainfall events) without lightning.

35 A case of study ( ) Let s consider the event 30-Apr 03-May The first day has the highest number of flashes over WP on record. A slow moving, mature midlatitude cyclone over the south Pacific. Cold front intersect the Chilean coast at about 40 S. Highest precip. over the Andes. Cold advection at low and mid levels over relatively warm waters create weakly unstable environment off WP (CAPE>50). Shallow convective clouds evident in GOES-VIS and CloudSAT collocated with area with flashes.

April 30, 2012 1800 UTC GOES-13 Visible (BW) and IR4 (light shading) + WWLLN Lighting (dots) +

36 April 30, UTC GOES-13 Visible (BW) and IR4 (light shading) + WWLLN Lighting (dots) + Starnet Lightning location Forced subsidence L Cloud Top Temp. less than -60 C 85 W 80 W 75 W 70 W 65 W

37 April 30, UTC Análisis sinóptico (CFSR) SLP (black contours) Z500 (white contours) T925 (colors) Temperature at 700 hpa along 47.5 S H L Coastline 75 W

38 Electric activity in a slightly unstable environment and strong Westerly flow Mid level cooling stronger and before than at surface

39 MODIS/Terra Calibrated Radiances L1B Swath 1km April 30, UTC Courtesy of Larry Bonneau Visible (Bands 1,2,3) Brightness Temp. (11.03 µm) + WWLLN 100 km C

40 Punta Arenas Observations at 12Z 30 Apr 2012 Freezing level

kms) but deep ( 8 km) convective cores producing

41 Cloud top heights derived from MODIS Tb agree well with CloudSat data and suggest horizontally small ( 10 kms) but deep ( 8 km) convective cores producing lightning. km 30 CloudSat Radar Data Low Reflectivity High 15 0

42 Accumulated precipitation (08 UTC, 29 April 23 UTC, 30 April, 2012) simulated by WRF (12 km inner domain, KF-Cu Scheme) (a) Non convective precipitation (b) Convective precipitation (mm/hr, 1/3 RNC)

43 One (typical) storm simulation (WRF) Hourly results during a 3 day period. Resolved precipitation (colors), Convective rainfall (contours) and topography Salient features: Rainfall enhancement over the Andes windward slope, Rain shadow, Convective rainfall along the coast

44 Climatology We inspected several episodes and found synoptic conditions similar to the case study. This is synthesized using a compositing analysis of the days with more than 50 flashes Strong winds and weakly unstable conditions are necessary conditions for lightning events. Weak temporal relationship between CAPE and number of flashes Area of high frequency of non-zero CAPE off WP (collocated with maximum of flash density) linked to warm SST anomaly there. Coastal topography provides the strong updrafts

Compositing analysis for days with more than 50 flashed in WP

L 1. Depresión Extratropical en etapa madura.

45 Compositing analysis for days with more than 50 flashed in WP Box (89 days) (a) T925 (shaded) & SLP(contours) (c) at925 (shaded) & at500 (contours) (d) Sfc. LI (shaded) & apw>0 (contours) C +30 H C +4 K L -4 L -6 L 1. Depresión Extratropical en etapa madura. PO en masa postfrontal 2. Enfriamiento más pronunciado en troposfera media 3. Ambiente ligeramente inestable sobre WP

46 Joint distribution of LI-U850 over WP for all days (contours), rainy days (gray dots) and lightning days (colors dots) Lightning quadrant Stronger Westerlies Number of strokes per day in WP Box > strokes but rain >0 Less stable More stable

47 Coastal topography forces updraft under strong westerlies. What about unstable conditions? Frequency of days with CAPE > 0 (unstable conditions) %?

48 The higher frequency of unstable conditions near Patagonia maybe associated with its warmer coastal waters. Annual mean SST eq Coastal SST down to 5 C colder than offshore 30 S Coastal SST up to 3.5 C warmer than offshore 60 S 90 W

49 Back to Topographic Control 48 hr Accumulated Precip - Control Simulation Resolved Convective

50 48 hr Accumulated Precip No Topo Simulation Resolved Convective

51 48 hr Accumulated Diff Precip (CTR-Ntopo) Resolved Convective

52 WRF (full) WRF (no-topo) 100 km Area mean precipitation [mm] Upstream sector Downstream sector Topographic factor (%)

53 Outstanding questions * How do the upwind enhancement / leeside rain shadow scale with Andes height (implications for isotope derived paleo-elevations)? Linear or non-linear? * What is the differential role of the coastal mountains and Andes cordillera on both stratiform and convective precipitation distribution? * What is the role of warm coastal zone in producing convective precipitation? Long term changes?

Cut-off lows in the Southern Hemisphere Climatology and two cases of study

Cut-off lows in the Southern Hemisphere Climatology and two cases of study René D. Garreaud Departament of Geophysics Universidad de Chile Santiago, Chile March 7, 2016 University at Albany - SUNY Outline