Climate models René D. Garreaud Departement of Geophysics Universidad de Chile

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 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 Upper Air Observations Satellite Products Assimilation System Model Gridded Analysis Gridding method 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. H H L L SLP map, October

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 300 hPa (contours) Air temperature averaged between 700 and 400 hPa (colors)

Table 1. Main features of datasets commonly used in climate studies DatasetKey references Input 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 Gridded GHCN Peterson and Vose (1997) Sfc. Obs Precip and SAT 5°  5° lat-lon Land only 1900 – present Monthly Gridded UEA-CRU New et al Sfc. Obs Precip and SAT 3.75°  2.5° lat-lon Land only 1900 – present Monthly Gridded UEA-CRU05 Mitchell and Jones (2005) Sfc. Obs Precip and SAT 0.5°  0.5° lat-lon Land only 1901 – present Monthly Griddded U. Delware Legates and Willmott (1999a,b) Sfc. Obs Precip and SAT 0.5°  0.5° lat-lon Land only 1950 – 1999 Monthly Gridded SAM-CDC data Liebmann and Allured (2005) Sfc. Obs Precip 1°  1° lat-lon South America 1940 – 2006 Daily and Monthly Gridded CMAP Xie and Arkin (1987) Sfc. Obs.; Sat. data Precip 2.5°  2.5° lat-lon Global 1979 – present Pentad and Monthly Gridded GCPC 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 Kistler et al 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

Sistema Terrestre Todos juntos y al mismo tiempo Hidrosfera Criosfera Litosfera Biosfera Espacio Exterior Atmósfera

Sistema Terrestre SubsistemaTiempo de ajuste a cambio climático de escala global Océano superficialMeses - Años Océano ProfundoAños - Décadas Campos de HieloAños - Décadas Biosfera continentalAños - Décadas Litosfera> Miles de años

Atmosfera Oceano Superficial Oceano Profundo Atmosfera Oceano Superficial Oceano Profundo Atmosfera Oceano Superficial Oceano Profundo Sistema Modelado 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 Terrestre Dividir para modelar Hidrosfera Criosfera Litosfera Biosfera Espacio Exterior Atmósfera

Global Models (GCM) TypeSSTSea Ice Land Ice BiosphereLand use AGCMPPPPP CGCMCCP/C P OGCMCCPPP ESMCCCCC External parameters: GHG, O3, aerosols concentration; solar forcing Complexit y 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 parameter Orbital parameters, CO 2 Concentration, SST (AGCM), Land cover X, Y, Z: Time-dependent variables Pressure, winds, temperature, moisture,…. My first toy model A 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: X 0, Y 0, Z 0 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 forecast Model predicts daily values Climate Prediction Model does NOT predict daily values but still gives reasonable climate state (mean, variance, spectra, etc…) ObsMod

X 0 = X 0 = A slight difference in the initial conditions Large differences later on Non-linear equations The Lorenz’s (butterfly) chaos effect

A=2 A=1 Two runs of the model, everything equal but parameter A Note 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 Parameters that 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.

Momentum eqn. Energy eqn. Mass eqn. Idea gas law Atmospheric circulation is governed by fluid dynamics equation + ideal gas thermodynamics Water substance eqns.

Water Vapor Ice crystalCloud droplets SnowRain droplets Graupel/Hail ¿¿¿Where is precipitation??? Warm cloud 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

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 z  lat ~  lon ~ 1  - 3   z ~ 1 km  t ~ minutes-hours Top of atmosphere: 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 Desarrollo Economico-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 pasados Termostato tropical? Múltiples equilibrios?

Head et al. Nature 2009 La aparente existencia de un termostato tropical y su no representación en GCMs es un problema… Sin embargo una buena culebra ayuda

Head et al. Nature 2009

Ruddiman: Earth’s Climate, Chapter 4 GCMs (cambios CO2 + geografía) Reconstrucción incluyendo Titanoboa otorga 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) Calama 12 mm/decada Iquique 6 mm/década

High Andes & dry Atacama Posicionamiento de Sud América en rango actual de latitudes ( 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

PLASIM “Humboldt” Experiments uSST: SST(  ) only wSEP: warmer Southeast 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 ~ m  t ~ seconds L x ~ L y ~ km L z ~ 15 km  z z  y y  x x LyLy LxLx LzLz

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 Baseline SRES A2 y B min  4 months per simulation in fast PC Proyecto CONAMA – DGF/UCH

-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 mm/mes