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Baja California, Mxico The Science of Climate Change: a focus on - - PowerPoint PPT Presentation

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Tereza Cavazos Dept. de Oceanografa Fsica Baja California, Mxico The Science of Climate Change: a focus on Central America and the Caribbean Islands Antigua, Guatemala, 14-16 de marzo de 2017 CONTENT 1. Observed Variability and Trends

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Tereza Cavazos

  • Dept. de Oceanografía Física

Baja California, México

The Science of Climate Change: a focus on Central America and the Caribbean Islands Antigua, Guatemala, 14-16 de marzo de 2017

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CONTENT

  • 2. General Circulation Models
  • 3. Regional Climate Downscaling
  • 1. Observed Variability and Trends
  • 4. Climate Change Scenarios
  • 5. Regional Strategic Actions
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CONTENT

  • 1. Observed Variability

and Trends

Precipitation

More Extreme Precip?

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2016: Trend +1.08 oC (Jan-May) 2015: 15oC, Trend +0.87 oC

1 1 2

  • 1

Temperature Anomaly (oC) Temperature Anomaly (oF)

1900 1950 2000

https://www.ncdc.noaa.gov/cag/time-series/global/globe/land_ocean/ytd/5/1880-2016

Earth and Ocean Global Temperature Anomaly Tmean = 14oC between 1951-1980

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Cavazos 2017

+AMO?

  • PDO?

CLLJ weaker?

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+AMO +PDO

Decadal Patterns of f th the Atl tlantic and Pacifi fic

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Zwiers et al. 2013

  • AMO + AMO -

+

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JJA: Ts (oC) 1979-2005

Several studies:

  • Fuentes-Franco et al. 2015, 2016
  • Cavazos and De Grau 2014
  • Martínez-Sanchez & Cavazos 2014
  • Torres-Alavez et al. 2014

Problem with the SSTs in GCMs Inverse termal contrast

  • El Niño-like
  • Stronger CLLJ
  • Reduced Precip
  • +

ITCZ

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EPAC Warm Pool SST>28.5C

Size of EPAC and NATL Warm Pools

Obs 1970-2010: _______

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Zwiers et al. 2013

R1XD Trends: Intense 1d Precipitation

CMIP5 GCMs: Antrop Forcing GCMs: Nat + Ant Understimate Observations

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0-700 m

0 – 700 m (Anomaly) 0 – 2000 m

Observed Global Ocean Heat Content (Joules)

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2005

NASA

Sep 2015

https://www.washingtonpost.com/news/energy- environment/wp/2015/09/15/arctic-sea-ice-just- hit-its-annual-low-and-it-was-the-fourth-lowest-

  • n-record/

NASA

Arctic Ice Melting

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Changes in the Arctic Sea Ice

http://www.wired.com/2015/01/science-graphic-week-perrenial-arctic-sea-ice-continues-shrink/

Faster warming in the Arctic because sulfate aerosols have been reduced after actions to improve air quality in Europe? (Aerosls tend to cool the atmosphere). (Acosta Navarro et al. 2016, NGEO)

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Zwiers et al. 2013

Trends in extreme Sea Level (P99) (1970-2010) +

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Coral bleaching Droughts and heat waves More intense tropical cyclones, Floods, mosquitoes

Ocean acidification

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CONTENT

  • 2. General Circulation Models (GCMs)
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500 km, low complexity

Pioneers in GCMs: Suki Manabe and Kirk Bryan (1965), GFDL

1990 1995 2001 2007 2013 2020 IPCC Reports

50 yrs Evolution of f Cli limate Modeling and IP IPCC

ES Coupled Climate Models (~100 Km) and Regional Climate Models (50-25 km)

AR6 AR6

NCAR (CCM) Hadley (HadCM3)

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CMIP5 GCMs used in in AR5 of f th the IP IPCC

27 AO-GCMs 12 Regional Climate Centers

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7 Primitive equations: 1 Equation of State  p =  R T 1 Hydrostatic Eq.  -1/ p/z = g = PGF 1 Thermodynamic Eq.  dQ = dU + dW=cpdT-dP 3 Momentum Eqs. (u, v, w): Newton´s 2nd Law  dV/dt = V/t + V. V 1 Continuity Eq. (DIV)  ( . V)H = - w/z Solution in each gripoint

3-Dimensional Coupled Modeling

Clouds not resolved by AO- GCMs  Physical parameterizations

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p RT p p D C E p q q t q D c Q c Q p T p T T t T Fricción f p t

q H p con p rad

                                                          V V V V k V V V V     

These terms involve scales not solved by GCMs Momentum Eqs. Thermodynamic Eq.

  • Conserv. of water vapor

Continuity Eq. (Div) Hydrostatic Eq. Non-hydrostatic

Local change Horiz. Advection Vert. Advection Coriolis PGF Other forcings

( = gZ)

Dif ifference between AOGCMs and RCMs

Governing Equations

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Physical Processes in in a Model

Atmosphere

Solar & Terrestrial Radiation Advection Snow Momentum Heat Water Sea Ice

Continent

Mixed layer ocean

Advection

PARAMETERIZATIONS

Microphysics Cumulus Radiation PBL Surface (soil, veget, ice, albedo. etc)

GHGs

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CONTENT

  • 3. Regional Climate Downscaling
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Types of Downscaling

  • Statistical (SDSM, Neural Nets,

Bias Correction)

  • Hybrid

(Dynamic & Stat)

  • Dynamic

(CORDEX: RegCM, WRF , RCA, REMO, RCanM, PRECIS) Utililty

  • Study

physical processes at meso-local scale

  • Validation and sensitivity studies
  • Climate

change scenarios relevant for integrated VIA assessment (Vulnerability, Impacts and Adaptation)

  • Decision

support tools for local climate change impacts

Climate Downscaling

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Regional Cli limate Downscaling: Decision Support Tools

SDSM Statistical Downscaling Model http://co-public.lboro.ac.uk/cocwd/SDSM/sdsmmain.html CORDEX: COordinated Regional climate Downscaling Experiment http://www.meteo.unican.es/es/projects/CORDEX CORDEX Output http://esg-dn1.nsc.liu.se/search/cordex/ Platform for evaluation- RCMES Regional Climate Model Evaluation System, Kyo Lee, JPL https://rcmes.jpl.nasa.gov/content/software-support

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GCM

Regional Cli limate Downscaling

GCM CMs > > 15 150 Km vs s RCMs < < 50 50 km

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(Giorgi y Gutowski 2016)

Added Value of Increasing the Spatial Resolution: CORDEX Alpes: Sep-Nov Precipitación

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(Giorgi y Gutowski 2016)

Sources of uncertainty in regional climate change projections

(Giorgi and Gutowski 2016)

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(Cavazos et al. 2017)

CRU Obs Ensamble

> 1000 msnm

Intercomparison of 15 GCMs 1979-2005: Mid-Summer Drought (Canícula) Region

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Cavazos and de Grau, 2014

> 1000 msnm

CRU GPCP ERA-Int

Precipitation SE Mex & Guatemala

RegCM4-HadGEM_CAM

Intercomparison of 4 GCMs and REgCM 1979-2005. Relevant Process: the MSD

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CONTENT

  • 4. Climate Change Scenarios using GCMs

(Figs. 9 y 10, van Vuuren et al. 2011) (W/m2) (W/m2) T(oC) 8.5 5.0o 4.5 2.5o 2.6 1.5o (ppm)

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(Cavazos et al. 2017)

Seasonal Change of Precipitation (%) 2075-2099 minus 1961-2000 under RCP8.5

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CRU

Historic Weighted Ensemble of 15 MCG

Cavazos et al. 2013

1961-2000: JJA P90 Threshold of Tmax (oC)

 Good approx

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(Cavazos et al. 2013)

Change in the JJA P90 Threshold of Tmax, 2075-2099 minus 1961-2000 under RCP8.5

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(Cavazos et al. 2013)

JJA P90 Threshold of Precip, 1961-2000

Historic Weighted GCM Ensemble

CRU

 Understimation

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(Cavazos et al. 2013)

Change in the JJA P90 Threshold of Precip, 2075-2099 minus 1961-2000 under RCP8.5

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CONTENIDO

2.2 Acciones estratégicas: Estudios de procesos y Modelación regional del clima

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CONTENT

  • 5. Regional Strategic Actions

(W/m2)

CLLJ

OEs

MSD

FFs

AMO PDO SSTs ITCZ

Monsoon

El Niño/La Niña

(m) TCs

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  • Hacer múltipes pruebas para seleccionar el mejor

GCM que va a forzar a un modelo regional

  • Hacer multiples pruebas para seleccionar la mejor

configuración del modelo regional

  • Desarrollar de capacidades a escala regional a

través de talleres de modelación, visitas académicas

  • Fortalecer la colaboración científica regional para

estudiar procesos y desarrollar escenarios climáticos a escala regional y local

  • Promover proyectos regionales de modelación y de

evaluación integrada VIA para diferentes sectores

Modelación Regional

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Aumentar la investigación del clima, el agua y la energía

 Formación de recursos humanos y colaboraciones regionales  Investigación básica y aplicada para comprender y predecir los fenómenos  Desarrollos tecnológicos que resuelvan problemas de infraestructura (adaptación) y de mitigación (verdes)  Estudios interdisciplinarios para entender los impactos  Desarrollar mecanismos de adaptación para diferentes sectores  Desarrollar mejores escenarios y a escalas más finas

Estrategias de investigación