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

Transcripción

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

2 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

3 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.

4 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.

5 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) 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

6 Red de Radiosondas (OMM, GTS) Perfiles verticales (20 km) de T, HR, viento, presión, cada 12 / 24 hr

7 DATA SOURCES AND PRODUCTS Surface and Upper Air Observations Gridded Analysis Gridding method Satellite Products Assimilation System Model

8 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 L H L SLP map, October

9 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)

10 Table 1. Main features of datasets commonly used in climate studies Dataset Key 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 lat-lon Land only 1900 present Monthly Gridded UEA-CRU05 Mitchell and Jones (2005) Sfc. Obs Precip and SAT lat-lon Land only 1901 present Monthly Griddded U. Delware Legates and Willmott (1999a,b) Sfc. Obs Precip and SAT lat-lon Land only Monthly Gridded SAM-CDC data Liebmann and Allured (2005) Sfc. Obs Precip 1 1 lat-lon South America Daily and Monthly Gridded CMAP Xie and Arkin (1987) Sfc. Obs.; Sat. data Precip lat-lon Global 1979 present Pentad and Monthly Gridded GCPC Adler et al. (2003) Sfc. Obs.; Sat. data Precip 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 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 lat-lon, 17 vertical levels Global 1948 present 6 hr, daily, monthly

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

13 Sistema Terrestre Subsistema Océano superficial Océano Profundo Campos de Hielo Biosfera continental Litosfera Tiempo de ajuste a cambio climático de escala global Meses - Años Años - Décadas Años - Décadas Años - Décadas > Miles de años

14 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) dt = dias-años dt = años - décadas dt = siglos y + Atmosfera Atmosfera Atmosfera Oceano Superficial Oceano Superficial Oceano Superficial Oceano Profundo Oceano Profundo Oceano Profundo Sistema Modelado Prescrito

15 Sistema Terrestre Dividir para modelar Criosfera Espacio Exterior Atmósfera Hidrosfera Biosfera Litosfera

16 Global Models (GCM) Type SST Sea Ice Land Ice Biosphere Land use Complexity 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 A: Atmospheric Only; C: Coupled; O: Ocean; ESM: Earth-system models External parameters: GHG, O3, aerosols concentration; solar forcing

17 My first toy model A system of coupled, non-linear algebraic equations 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 X, Y, Z: Time-dependent variables Pressure, winds, temperature, moisture,. A, B, C, D: External parameter Orbital parameters, CO 2 Concentration, SST (AGCM), Land cover x y z Randoms error Set to zero Deterministic model

18 My first toy 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

19 Mod Obs Weather forecast Model predicts daily values Climate Prediction Model does NOT predict daily values but still gives reasonable climate state (mean, variance, spectra, etc )

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

21 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. A=2 A=1 Two runs of the model, everything equal but parameter A Note the Climate Change related to change in A

22 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.

23 Atmospheric circulation is governed by fluid dynamics equation + ideal gas thermodynamics dv dt fkˆ V 1 p F R g Momentum eqn. ( t V ) T S P Q RAD Q Conv Q Sfc Energy eqn. V p 0 Mass eqn. ( gz ) p RT p Idea gas law dq v dt dq dt C E r C E S r Water substance eqns.

24 Where is precipitation??? Warm cloud Cloud droplets Water Vapor Ice crystal Cold clouds Rain droplets Snow Graupel/Hail dqv C Ec Er dt dqc C Ec Ac K dt dqr Ac Kc Er Fr dt c PP s F r

25 Previous system is highly non-linear, with no simple analytic solution... We solve the system using numerical methods applied upon a three-dimensional grid

26 diab Q x T u t T diab i t i t i t i t i t Q x T T u t T T 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

27 Global Models (GCM) z lat lon lat ~ lon ~ 1-3 z ~ 1 km t ~ minutes-hours Top of atmosphere: km

28 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

29 EJEMPLO 1: Clima del Siglo XXI Escenarios Desarrollo Economico-Social GCMs

30 EJEMPLO 1 IPCC 2007: WG1-AR4 (2007)

31 EJEMPLO 1 IPCC 2007: WG1-AR4 (2007)

32 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

33 Ruddiman: Earth s Climate, Chapter 4 Early Cretaceous world

34 Modelos además presentan problemas en climas pasados Termostato tropical? Múltiples equilibrios? GCMs (cambios CO2 + geografía) Ruddiman: Earth s Climate, Chapter 4

35 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

36 Head et al. Nature 2009

37 Head et al. Nature 2009

38 Reconstrucción incluyendo Titanoboa otorga más credibilidad a GCMs Reconstrucción c/culebra GCMs (cambios CO2 + geografía) Ruddiman: Earth s Climate, Chapter 4

39 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 tierrasol, TSM, geografía. Componentes variables: Circulación atmosférica.

40 High Andes & dry Atacama Iquique 6 mm/década Calama 12 mm/decada 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)

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

42 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

43 Model Validation

44 PLASIM Topography Experiments 0.3*Topo minus Control (DJF) 900 hpa winds and Precip % Precip (P/Pc)

45 usst: SST() only wsep: warmer Southeast Pacific PLASIM Humboldt Experiments

46 PLASIM Humboldt Experiments usst minus Control (DJF) 900 hpa winds and Precip % Precip (P/Pc)

47 Ejemplo 4: Clima durante el LGM

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52 Regional Models (LAM, MM) L x z L y y L z x x ~ y ~ 1-50 km z ~ m t ~ seconds L x ~ L y ~ km L z ~ 15 km

53 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

54 Proyecto CONAMA DGF/UCH 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

55 Surface Temperature Difference A2-BL 30 S 75 W C 50 S

56 Precipitation Difference A2-BL mm/mes 30 S 50 S 75 W

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