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SENAMHI – Perú

Curso de Climatología Sinóptica en la Costa Oeste de América del Sur16-20 Abril 2007; Lima - Perú

Relator: Dr. René D. Garreaudwww.dgf.uchile.cl/rene

Departamento de GeofísicaUniversidad de Chile

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Presentación No. 1: Climatología de Sud América

En esta exposición se describe el clima actual de Sud América empleando observaciones instrumentales durante el siglo XX. La exposición comienza con una descripción de las bases de datos disponibles para efectuar tal climatología. Luego, se provee un entendimiento básico de los procesos físicos que explican la distribución promedio y el ciclo anual medio de las variables meteorológicas (precipitación, temperatura, vientos) sobre el continente Sud Americano y los océanos adyacentes. También se describen en forma sucinta los patrones

continentales de variabilidad climática asociados con ENSO, PDO y AAO.

• Fuentes de datos para estudios de climatología• Circulación global y contexto geografico• Patrones de circulación continental• Variabilidad interanual• Efectos de ENOS, PDO, AAO

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Surface and UpperAir Observations

Satellite Products

Assimilationsystem

Gridded Analysis

Griddingmethod

DATA SOURCES AND PRODUCTS

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Table 1. Main features of datasets commonly used in climate studies

Dataset Key referencesInput data -Variables

Spatial resolution -Coverage

Time span -Time resolution

Station GHCN

Peterson and Vose (1997)

Sfc. Obs Precip and SAT

N/A Land only

1850(*) – present Daily and Monthly

GriddedGHCN

Peterson and Vose (1997)

Sfc. Obs Precip and SAT

5° 5° lat-lon Land only

1900 – present Monthly

GriddedUEA-CRU

New et al. 2000Sfc. Obs Precip and SAT

3.75° 2.5° lat-lon Land only

1900 – present Monthly

GriddedUEA-CRU05

Mitchell and Jones (2005)

Sfc. Obs Precip and SAT

0.5° 0.5° lat-lon Land only

1901 – present Monthly

GridddedU. Delware

Legates and Willmott (1999a,b)

Sfc. Obs Precip and SAT

0.5° 0.5° lat-lon Land only

1950 – 1999 Monthly

GriddedSAM-CDC data

Liebmann and Allured (2005)

Sfc. Obs Precip

1° 1° lat-lon South America

1940 – 2006 Daily and Monthly

GriddedCMAP

Xie and Arkin (1987)Sfc. Obs.; Sat. data Precip

2.5° 2.5° lat-lon Global

1979 – present Pentad and Monthly

GriddedGCPC

Adler et al. (2003)Sfc. Obs.; Sat. data Precip

2.5° 2.5° lat-lon Global

1979 – present Monthly

NCEP-NCAR Reanalysis (NNR)

Kalnay et al. 1996Kistler et al. 2001

Sfc. Obs.; UA Obs; Sat. data Pressure, temp., winds, etc.

2.5° 2.5° lat-lon,17 vertical levels Global

1948 – present 6 hr, daily, monthly

ECMWF Reanalysis(ERA-40)

Uppala et al. (2005)Sfc. Obs., UA Obs, Sat. data Pressure, temp., winds, etc.

2.5° 2.5° lat-lon,17 vertical levels Global

1948 – present 6 hr, daily, monthly

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Surface (land/ocean) Synoptic Stations Met. Observations (T,Td,P,V,…) @ 0, 6, 12, 18 UTC are transmitted in real-time to WMO and Analysis Centers

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Red de Radiosondas (OMM, GTS)

Perfiles verticales (20 km) de T, HR, viento, presión, cada 12 / 24 hr

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Observaciones en altura IV:Info. Obtenida por Aviones comerciales

Perfiles verticales (0-10 km) de T, HR, viento, presión, y datos a nivel (10 km) a distintas horas. También se transmiten via GTS

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All stations (anytime, any length)

Century-long stations (Ti<1905, Tf>1995, missdata<20%)

Precipitation Mean Temperature

Global Historical Climate Network (GHCN)

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Because analysis are produced in real-time, some data is not assimilated, but it was archived. In the 90’s the NCEP-NCAR (USA) began a major project in which they re-run their assimilation system with all the available data.

The result is the widely used “Reanalysis” data, including many fields (air temperature, wind, pressure) on a regular 2.5°x2.5° lat-lon grid, from 1948 to present every 6 hours (also available daily, monthly and long-term-mean means). Fields are 2- or 3-Dimensional. Preferred data format: NetCDF. Freely available.

Reanalysis?!

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Reanalysis system also includes a meteorological model from which precipitation and other not-observed variables (e.g., vertical motion) are derived.

Reanalysis data is great for studying interannual and higher frequency variability. Interdecadal variability and trends are not so well depicted (we don’t trust much before the 70’s, particularly in the SH).

European Center (ECMWF) did a similar effort (ERA-15 and ERA-40). Higher horizontal resolution (1.25°x1.25°), but harder to get.

Reanalysis

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Jan

Jun

Climatology of 300 hPa winds(10-12 km) from NNR

Example of some daily fields from NNR

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Altiplano

SubtropicalAndes

Amazon basin

La Platabasin

Brazilianhighlands

Chaco

Pampas

Patagonia

EquatorialAndes

Llanos

Guinashighlands

Geographicalsetting

10°N

0°N

20°S

40°S

60°S

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Because of its long meridional extent, South America exhibits tropical, subtropical and extratropical climatic regimes

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15

1

23

45

6 7

8

1

9

10

5

6

1

2

3

4

1

2

3

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Precipitation features

1. ITCZ2. Continental convection3. Altiplano convection4. SACZ5. Pampas convection6. Midlatitude storm track7. Orographic precipitation8. Coastal desert9. NE Brazil semiarid 10. Patagonia dry zone11. Ocean desert

Circulation features

1. ITCZ2. Trade winds3. Subtropical high4. Midlatitude westerlies5. Low level jet6. SACZ7. Bolivian high8. NE Brazil trough9. Tropical easterlies10. Midlatitude westerlies11. Jet stream

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Annual Mean / Zonal Mean Annual Mean / Zonal asymmetry January - July Mean

Another Perspective of the Precipitation Field

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Convective rainfall also exhibits a pronounced diurnal cycle

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Modest annual cycle in the extratropics

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1

2

3

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The low-level air temperature field

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The Precipitation Variability (UdW data)

Which regions exhibit large year-to-year variability?

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Stronger than normal westerlies leads to rainy conditions over western Patagonia BUT drier conditions over eastern Patagonia (orographic effects)…not much elsewhere….

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Annual mean Precip/SAT regressed uponindex of large-scale modes (50 years of data)

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Seasonal correlation between Precip/SAT andMultivariate ENSO Index (50 years of data)