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Climate models
René D. Garreaud
Departement of GeophysicsUniversidad de Chile
www.dgf.uchile.cl/rene
Modelación Numérica de la Atmósfera
• ¿Por que empleamos modelos climáticos?• ¿En que consiste un modelo climático?• ¿Qué son variables y parámetros?• ¿Qué son los GCM, AOGCM y RCM?• Aplicaciones y limitaciones
¿Porque empleamos modelos climáticos?
• Suplemento de observaciones climáticas (interpolación física)
• Experimentos de gran escala (cambio en forzantes) y análisis de sensibilidad.
• Generación de escenarios climáticos pasados y futuros.
• Dynamical downscaling (escalamiento hacia abajo basado) de climas pasados y futuros.
¿Porque me gustan tanto las modelos?
• Aunque son una representación incompleta del mundo real, un buen modelo captura gran parte de la dinámica del sistema climático terrestre.
• Método alternativo (modelos conceptuales cualitativos) aguanta cualquier cosa…
• Los números que entrega un modelo pueden no ser “exactos” pero dan un rango plausible de cambio climático pasados o futuros.
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
From where do we get climate data? Almost all climate data is initially meteorological data, acquired to assist weather nowcast and forecast (especially for aviation)
Red de Radiosondas (OMM, GTS)
Perfiles verticales (20 km) de T, HR, viento, presión, cada 12 / 24 hr
Surface and UpperAir Observations
Satellite Products
AssimilationSystemModel
Gridded Analysis
Griddingmethod
DATA SOURCES AND PRODUCTS
Isobars: lines of constant pressure at a given level(e.g. sea level pressure chart) and time (snapshot).
High (anticyclones) and Low (cyclones) pressure centers.
HH
L
L
SLP map, October 17 2010
We now can reinterpret troughs and ridges seen in the geopotential height in the upper troposphere (e.g., 300 hPa) as tongues of cold air abnormally at low latitudes and warm air abnormally at high latitudes
Geopotential @ 300 hPa (contours)Air temperature averaged between 700 and 400 hPa (colors)
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
http://www.cdc.noaa.gov
http://www.ncdc.noaa.gov
http://iridl.ldeo.columbia.edu/
Sistema TerrestreTodos juntos y al mismo tiempo
Hidrosfera
Criosfera
Litosfera
Biosfera
EspacioExterior
Atmósfera
Sistema Terrestre
Subsistema Tiempo de ajuste a cambio climático de escala global
Océano superficial Meses - Años
Océano Profundo Años - Décadas
Campos de Hielo Años - Décadas
Biosfera continental Años - Décadas
Litosfera > Miles de años
Atmosfera
Oceano Superficial
Oceano Profundo
Atmosfera
Oceano Superficial
Oceano Profundo
Atmosfera
Oceano Superficial
Oceano Profundo
SistemaModelado
dT = dias-años dT = siglos y +dT = años - décadas
Mayor largo de simulacion requiere incorporar mas subsistemas terrestres a la simulacion (variables vs parámetros) hasta llegar a modelos del sistema terrestre completo (atmos-ocean-crio-bio-lito)
Prescrito
Sistema TerrestreDividir para modelar
Hidrosfera
Criosfera
Litosfera
Biosfera
EspacioExterior
Atmósfera
Global Models (GCM)
Type SST Sea Ice
Land Ice
Biosphere Land use
AGCM P P P P P
CGCM C C P/C P/C P
OGCM C C P P P
ESM C C C C C
External parameters: GHG, O3, aerosols concentration; solar forcing
Com
plex
ity
1980-
1990-
2005-
A: Atmospheric Only; C: Coupled; O: Ocean; ESM: Earth-system models
X(t) = A·X(t-1) ·Y(t) + B·Z(t-1) + x
Y(t)= C·X(t-1)·Y (t-1) + B·Z (t)+ y
Z(t)= D·Z(t-1)·Y (t) + E·X(t-1) + z
x = y = z = 0
A, B, C, D: External parameterOrbital parameters, CO2 Concentration, SST (AGCM), Land cover
X, Y, Z: Time-dependent variablesPressure, winds, temperature, moisture,….
My first toy modelA system of coupled, non-linear algebraic equations
x y z Randoms error
Set to zero Deterministic model
X(t) = A·X(t-1) ·Y(t) + B·Z(t-1) + x
Y(t)= C·X(t-1)·Y (t-1) + B·Z (t)+ y
Z(t)= D·Z(t-1)·Y (t) + E·X(t-1) + z
x = y = z = 0
To run the model, we need:
• Initial conditions: X0, Y0, Z0
• The values of the External Parameters … they can vary on time
• A numerical algorithm to solve the equations
• A computer big enough
My first toy model
Weather forecastModel predicts daily values
Climate PredictionModel does NOT predict daily valuesbut still gives reasonable climate state
(mean, variance, spectra, etc…)
ObsMod
X0 = -0.502
X0 = -0.501
A slight difference inthe initial conditions
Large differenceslater on
Non-linearequations
The Lorenz’s (butterfly) chaos effect
A=2
A=1
Two runs of the model, everything equal but parameter ANote the “Climate Change” related to change in A
Nevertheless, simulations after two-weeks are still “correct” in a climatic perspective and highly dependent upon external parameters models can
be used to see how the climate changes as external parameters vary.
Examples of External Parametersthat can be modified:
1. The relatively long memory of tropical SST can be used to obtain an idea of the SST field in the next few months (e.g., El Niño conditions). Using this predicted SST field to force an AGCM, allows us climate outlooks one season ahead.
2. Changes in solar forcing (due to changes in sun-earth geometry) are very well known for the past and future (For instance, NH seasonality was more intense in the Holocene than today). Modification of this parameter allow us paleo-climate reconstructions (still need to prescribe other parameters in a consistent way: Ice cover, SST, etc.…hard!)
3. Changes in greenhouse gases concentration in the next decades gases give us some future climate scenarios.
gFpVkfdtVd
R
1ˆ
SfcConvRADPQQQSTV
t
)(
0p
V
pRT
pgz
)(
Momentum eqn.
Energy eqn.
Mass eqn.
Idea gas law
Atmospheric circulation is governed by fluid dynamics equation + ideal gas thermodynamics
ECdt
dqv
rr SEC
dt
dq
Water substance eqns.
Water Vapor
Ice crystalCloud droplets
SnowRain droplets
Graupel/Hail
rcv EEC
dt
dq
cccc KAEC
dt
dq
rsrrccr FPPFEKA
dt
dq
¿¿¿Where is precipitation???W
arm
clo
ud
Cold clouds
Previous system is highly non-linear,with no simple analytic solution
?!.... We solve the system using numerical methods
applied upon a three-dimensional grid
diabQ
x
Tu
t
T
diab
it
iti
t
it
it Q
x
TTu
t
TT
11
111
Once selected the domain and grid, the numerical integration uses finite differences in time and space
Numerial method(stable & efficient)
Sub-grid processes must be parameterized, that is specified in term of large-scale variables
lat
lon
z
lat ~ lon ~ 1 - 3 z ~ 1 km t ~ minutes-hoursTop of atmosphere: 15-50 km
Global Models (GCM)
Ejemplo 1: Cambio climático antropogénico (siglo XXI)
Parámetros variables: Concentración de gases con efecto invernadero.
Parámetros fijos: Geografía, geometría tierra sol.
Componentes variables: Circulación atmosférica y oceánica, biosfera simplificada
GCMs
Escenarios DesarrolloEconomico-Social
EJEMPLO 1: Clima del Siglo XXI
© IPCC 2007: WG1-AR4 (2007)
EJEMPLO 1
© IPCC 2007: WG1-AR4 (2007)
EJEMPLO 1
Ejemplo 2: Clima del Cretáceo
Parámetros “alterados”: Concentración de gases con efecto invernadero, geografía, geometría tierra sol.
Componentes variables: Circulación atmosférica y oceánica, biosfera simplificada
Ruddiman: Earth’s Climate, Chapter 4
Early Cretaceous world
Ruddiman: Earth’s Climate, Chapter 4
GCMs (cambios CO2 + geografía)
Modelos además presentan problemas en climas pasadosTermostato tropical? Múltiples equilibrios?
Head et al. Nature 2009
La aparente existencia de un termostato tropical y suno representación en GCMs es un problema…
Sin embargo una buena culebra ayuda
Head et al. Nature 2009
Head et al. Nature 2009
Ruddiman: Earth’s Climate, Chapter 4
GCMs (cambios CO2 + geografía)
Reconstrucción incluyendo Titanoboaotorga más credibilidad a GCMs
Reconstrucción c/culebra
Ejemplo 3: Aridificación del desierto de Atacama
En este caso haremos un experimento de sensibilidad y no una simulación paleo-climática. No es necesario alcanzar equilibrio.
Parámetros “alterados”: geografía (Andes) y temperatura superficial del oceano.
Parametros fijos: Concentración GEI, geometría tierra-sol, TSM, geografía.
Componentes variables: Circulación atmosférica.
High Andes & dry Atacama
Condiciones actuales hiper-áridas a lo largo del desierto costero. Sin embargo, abundante evidencia geológica de un pasado remoto (Ma) menos extremo en cuanto a déficit de precipitación (e.g., formación de yacimientos de Cu)
Calama12 mm/decada
Iquique6 mm/década
High Andes & dry Atacama
Posicionamiento de Sud América en rango actual de latitudes (80-100 Ma)
Conceptually, both Andean uplift (enhanced bloking of moist air) and SEP cooling (less evaporation from ocean) may increase dryness of the Atacama desert…it would be nice to use a “simple” climate model to study these two conditions.
We use PLASIM, an Earth System Model of Intermediate Complexity from Hamburg University:
• Atmospheric component: PUMA• Simple slab model for SST and Sea Ice• SIMBA for biosphere
We performed 50 year long simulations altering one Boundary condition at a time
Model Validation
0.3*Topo minus Control (DJF)
900 hPa winds and Precip % Precip (P/Pc)
PLASIM Topography Experiments
PLA
SIM
“H
umbo
ldt”
Exp
erim
ents
uSST: SST() only
wSEP: warmerSoutheast Pacific
PLASIM “Humboldt” Experiments
uSST minus Control (DJF)
900 hPa winds and Precip % Precip (P/Pc)
Ejemplo 4: Clima durante el LGM
Regional Models (LAM, MM)
x ~ y ~ 1-50 km z ~ 50-200 m t ~ secondsLx ~ L y ~ 100-5000 km Lz ~ 15 km
z
y
x
Ly
Lx
Lz
Regional Models (LAM, MM)
Regional models gives us a lot more detail (including topographic effects) but they need to be “feeded” at their lateral boundaries by results from a GCM.
Main problem: Garbage in – Garbage out
Model:
• PRECIS – UK
Single domain
• Horiz. grid spacing. 25 km
• 19 vertical levels
• Lateral BC: HadAM every 6h
• Sfc. BC: HadISST1 + Linear trend
Simulations
• 1961-1990 Baseline
• 2071-2100 SRES A2 y B2
• 30 years @ 3 min 4 months per
simulation in fast PC
Proyecto CONAMA – DGF/UCHhttp://www.dgf.uchile.cl/PRECIS
-1° 0° 1° 2° 3° 4° 5° C
Surface Temperature Difference A2-BL
30°S
50°S
75°W
Precipitation Difference A2-BL
30°S
50°S 75°W
-50 0 +50 mm/mes