Climate Impacts Projections

Climate Science in the Public Interest

Guillaume MaugerClimate Impacts GroupUniversity of Washington

David RuppOregon Climate Change Research InstituteUniversity of Oregon

Background:

Source: Snover et al., Cons. Bio., 2013

Global Climate Scenarios

Regional Climate Scenarios

Local Climate-Related Environmental-

Scenarios

Effects/Impacts Simulations

Consequences for Management

General Approach to Climate Impacts Assessment

Global Climate Models (GCMs)

•  GCMs have been developed by universities and others throughout the world.

•  The Coupled Model Intercomparison Project, or CMIP, defines a set of specific model simulations that allow for apples-to-apples comparisons. – CMIP3 (2007): previous dataset

– CMIP5 (2012): newer dataset

GCM acronyms are weird

[4] Our goal here is not to cull or weight models to narrowor refine future projections, but rather to evaluate model per-formance in order to make informed recommendations tothose who may use these model outputs. Downscaled climatedata from these models will be used as inputs to impactsmodels, including models of forest and range dynamics, cropgrowth, and hydrology, and these “downstream” modelersmay want to know how well these GCMs simulate particularproperties of the regional climate. For those who have thecapacity to run only a few scenarios, this paper may guidethe selection of which GCMs to use as inputs.[5] Hawkins and Sutton [2009, 2011] have nicely illus-

trated the contributions of three sources of uncertainty to re-gional- and global-scale projections, and these are addressedwell in the formulation of CMIP5: uncertainties in global

forcing (chiefly greenhouse gases), physical response asrepresented by model formulation, and internal or unforcedvariability. CMIP5 handles the first by the use of severaldifferent “Representative Concentration Pathways” (RCPs).The second is the primary motivation for using a large num-ber of global models available through CMIP5 (Mote et al.[2011] recommend at least 10) in describing future climateor running impacts models. The third is the primary reason thatmany modeling centers have contributed multiple “ensemblemembers” to CMIP5—simulations whose boundary condi-tions and model formulation are the same, but which differtypically by having different initial conditions. In our modelevaluation, the first source of uncertainty is irrelevant (sinceglobal forcing for the recent past is certain and well quantified)but the second and third sources of uncertainty are important

Table 1. CMIP5 Models Used in This Study and Some of Their Attributes

Model Center

Number of EnsembleMembers:T/ P/ Tmin/

Tmax/

AtmosphericResolution(Lon. × Lat.)

Vertical Levelsin Atmosphere

BCC-CSM1-1 Beijing Climate Center, China Meteorological Administration 3/ 3/ 3/ 3 2.8× 2.8 26BCC-CSM1-1-M Beijing Climate Center, China Meteorological Administration 3/ 3/ 3/ 3 1.12× 1.12 26BNU-ESM College of Global Change and Earth System Science, Beijing

Normal University, China1/ 1/ 1/ 1 2.8× 2.8 26

CanESM2 Canadian Centre for Climate Modeling and Analysis 5/ 5/ 5/ 5 2.8× 2.8 35CCSM4 National Center of Atmospheric Research, USA 6/ 6/ 6/ 6 1.25× 0.94 26CESM1-BGC Community Earth System Model Contributors 1/ 1/ 1/ 1 1.25× 0.94 26CESM1-CAM5 Community Earth System Model Contributors 3/ 3/ 3/ 3 1.25× 0.94 26CESM1-FASTCHEM Community Earth System Model Contributors 3/ 3/ 3/ 3 1.25× 0.94 26CESM1-WACCM Community Earth System Model Contributors 1/ 1/ 1/ 1 2.5× 1.89 66CMCC-CESM Centro Euro-Mediterraneo per I Cambiamenti Climatici 1/ 1/ 1/ 1 3.75× 3.71 39CMCC-CM Centro Euro-Mediterraneo per I Cambiamenti Climatici 1/ 1/ 1/ 1 0.75× 0.75 31CMCC-CMS Centro Euro-Mediterraneo per I Cambiamenti Climatici 1/ 1/ 1/ 1 1.88× 1.87 95CNRM-CM5 National Centre of Meteorological Research, France 10/ 10/ 10/ 10 1.4× 1.4 31CNRM-CM5-2 National Centre of Meteorological Research, France 1/ 1/ 1/ 1 1.4× 1.4 31CSIRO-Mk3-6-0 Commonwealth Scientific and Industrial Research Organization/

Queensland Climate Change Centre of Excellence, Australia10/ 10/ 10/ 10 1.8× 1.8 18

EC-EARTH EC-EARTH consortium 5/ 7/ 4/ 4 1.13× 1.12 62FGOALS-g2 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences 5/ 5/ 5/ 5 2.8× 2.8 26FGOALS-s2 LASG, Institute of Atmospheric Physics, Chinese Academy of Sciences 3/ 3/ 3/ 3 2.8× 1.7 26FIO-ESM The First Institute of Oceanography, SOA, China 3/ 3/ 3/ 3 2.81× 2.79 26GFDL-CM3 NOAA Geophysical Fluid Dynamics Laboratory, USA 5/ 5/ 5/ 5 2.5× 2.0 48GFDL-ESM2G NOAA Geophysical Fluid Dynamics Laboratory, USA 3/ 3/ 1/ 1 2.5× 2.0 48GFDL-ESM2M NOAA Geophysical Fluid Dynamics Laboratory, USA 1/ 1/ 1/ 1 2.5× 2.0 48GISS-E2-H NASA Goddard Institute for Space Studies, USA 4/ 4/ 4/ 4 2.5× 2.0 40GISS-E2-H-CC NASA Goddard Institute for Space Studies, USA 1/ 1/ 1/ 1 2.5× 2.0 40GISS-E2-R NASA Goddard Institute for Space Studies, USA 2/ 2/ 2/ 2 2.5× 2.0 40GISS-E2-H-CC NASA Goddard Institute for Space Studies, USA 1/ 1/ 1/ 1 2.5× 2.0 40HadCM3 Met Office Hadley Center, UK 10/ 10/ 10/ 10 3.75× 2.5 19HadGEM2-AO Met Office Hadley Center, UK 1/ 1/ 1/ 1 1.88× 1.25 38HadGEM2-CC Met Office Hadley Center, UK 1/ 1/ 1/ 1 1.88× 1.25 60HadGEM2-ES Met Office Hadley Center, UK 5/ 5/ 5/ 5 1.88× 1.25 38INMCM4 Institute for Numerical Mathematics, Russia 1/ 1/ 1/ 1 2.0× 1.5 21IPSL-CM5A-LR Institut Pierre Simon Laplace, France 6/ 6/ 1/ 1 3.75× 1.8 39IPSL-CM5A-MR Institut Pierre Simon Laplace, France 3/ 3/ 1/ 1 2.5× 1.25 39IPSL-CM5B-LR Institut Pierre Simon Laplace, France 1/ 1/ 1/ 1 3.75× 1.8 39MIROC5 Atmosphere and Ocean Research Institute (The University of Tokyo),

National Institute for Environmental Studies, and Japan Agencyfor Marine-Earth Science and Technology

5/ 5/ 5/ 5 1.4× 1.4 40

MIROC-ESM Japan Agency for Marine-Earth Science and Technology, Atmosphereand Ocean Research Institute (The University of Tokyo), and National

Institute for Environmental Studies

3/ 3/ 3/ 3 2.8× 2.8 80

MIROC-ESM-CHEM

Japan Agency for Marine-Earth Science and Technology, Atmosphereand Ocean Research Institute (The University of Tokyo), and National

Institute for Environmental Studies

1/ 1/ 1/ 1 2.8× 2.8 80

MPI-ESM-LR Max Planck Institute for Meteorology, Germany 3/ 3/ 3/ 3 1.88× 1.87 47MPI-ESM-MR Max Planck Institute for Meteorology, Germany 3/ 3/ 3/ 3 1.88× 1.87 95MRI-CGCM3 Meteorological Research Institute, Japan 5/ 5/ 5/ 5 1.1× 1.1 48NorESM1-M Norwegian Climate Center, Norway 3/ 3/ 3/ 3 2.5× 1.9 26

10,885

RUPP ET AL.: CMIP5 20TH CENTURY CLIMATE OF THE PNW

~100-200 km (~60-120 mi) resolution

~6 km (~4 mi) resolution

Downscaling Relates the large to the small

Downscaling

Statistical: Apply changes from global model projection to historical observations Statistical approach

Dynamical: Use global model projections to drive a regional climate model

Physics-based approach

Impacts Modeling Translation from climate impacts to _____

e.g.: Hydrology, Vegetation

An important complement:

Source: Snover et al., Cons. Bio., 2013

Global Climate Scenarios

Regional Climate Scenarios

Local Climate-Related Environmental-

Scenarios

Effects/Impacts Simulations

Consequences for Management

Best when combined with a

bottom-up assessment of

information needs

Choosing & Using Scenarios

Source: Snover et al., Cons. Bio., 2013

Special Section

Choosing and Using Climate-Change Scenarios forEcological-Impact Assessments and ConservationDecisionsAMY K. SNOVER,∗ ‡‡ NATHAN J. MANTUA,∗† JEREMY S. LITTELL,∗‡ MICHAEL A. ALEXANDER,§MICHELLE M. MCCLURE,∗∗ AND JANET NYE††∗Climate Impacts Group, University of Washington, Box 355674, Seattle, WA 98195, U.S.A.†National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Southwest Fisheries Science Center, 110Shaffer Road, Santa Cruz, CA 95060, U.S.A.‡Department of Interior Alaska Climate Science Center, U.S. Geological Survey, 4210 University Drive, Anchorage, AK 99508, U.S.A.§NOAA, Earth System Research Laboratory, R/PSD1, 325 Broadway, Boulder, CO 80305-3328, U.S.A.∗∗National Oceanic and Atmospheric Administration, National Marine Fisheries Service, Northwest Fisheries Science Center, 2725Montlake Boulevard East, Seattle, WA 98112, U.S.A.††School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY 11794-5000, U.S.A.

Abstract: Increased concern over climate change is demonstrated by the many efforts to assess climateeffects and develop adaptation strategies. Scientists, resource managers, and decision makers are increasinglyexpected to use climate information, but they struggle with its uncertainty. With the current proliferation of cli-mate simulations and downscaling methods, scientifically credible strategies for selecting a subset for analysisand decision making are needed. Drawing on a rich literature in climate science and impact assessment and onexperience working with natural resource scientists and decision makers, we devised guidelines for choosingclimate-change scenarios for ecological impact assessment that recognize irreducible uncertainty in climateprojections and address common misconceptions about this uncertainty. This approach involves identifyingprimary local climate drivers by climate sensitivity of the biological system of interest; determining appropriatesources of information for future changes in those drivers; considering how well processes controlling localclimate are spatially resolved; and selecting scenarios based on considering observed emission trends, relativeimportance of natural climate variability, and risk tolerance and time horizon of the associated decision.The most appropriate scenarios for a particular analysis will not necessarily be the most appropriate foranother due to differences in local climate drivers, biophysical linkages to climate, decision characteristics,and how well a model simulates the climate parameters and processes of interest. Given these complexities,we recommend interaction among climate scientists, natural and physical scientists, and decision makersthroughout the process of choosing and using climate-change scenarios for ecological impact assessment.

Keywords: climate change, freshwater, marine, risk assessment, threatened species

Seleccion y Uso de Escenarios de Cambio Climatico para Estudios de Impacto Ecologico y Decisiones de Conser-vacion

Resumen: El incremento en la preocupacion por el cambio climatico se ve demostrado por la cantidad deesfuerzos para estudiar los efectos climaticos y desarrollar estrategias de adaptacion. Cada vez se espera masque los cientıficos, los administradores de recursos y los encargados de tomar decisiones usen la informacionclimatica pero ellos tienen problemas con esta incertidumbre. Con la actual proliferacion de simulacionesclimaticas y metodos con reduccion de escala, se requieren estrategias cientıficamente creıbles para la se-leccion de un subconjunto de analisis y toma de decisiones. Al tomar de una literatura rica en cienciasclimaticas y el estudio del impacto y con la experiencia de trabajar con cientıficos de recursos naturales y

‡‡email aksnover@uw.eduPaper submitted October 31, 2012; revised manuscript accepted May 23, 2013.

1147Conservation Biology, Volume 27, No. 6, 1147–1157C⃝ 2013 Society for Conservation BiologyDOI: 10.1111/cobi.12163

The RMJOC-II project: �Climate Projections

•  Global model selection

•  Statistical Downscaling

•  Dynamical Downscaling

(Ordered by sum total of relative errors. All metrics treated equally.)

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The RMJOC-II project: �Climate Projections

•  Global model selection

•  Downscaling Approaches

•  Downscaling Comparison

Downscaling

Statistical: Apply changes from global model projection to historical observations Statistical approach

Dynamical: Use global model projections to drive a regional climate model

Physics-based approach

Downscaling

Statistical:

Two methods: MACA BCSD

Dynamical:

One approach:

RegCM4

39

778 Figure 2. 6-hour accumulated precipitation simulated by ECHAM5/WRF for 27 Nov 2030; the 779 left panel shows results for the outer, 36-km domain; right panel the inner, 12-km domain. 780 (1966-2005, 2011-2050;

RCP 8.5 only)

(monthly)

The RMJOC-II project: �Climate Projections

•  Global model selection

•  Downscaling Approaches

•  Downscaling Comparison

Change in JJA temperature between 1970-1999 and 2020-2049, RCP8.5

Change in DJF precipitation between 1970-1999 and 2020-2049, RCP8.5

The RMJOC-II project: �Climate Projections

•  Global model selection

•  Downscaling Approaches

•  Downscaling Comparison

Thank yougmauger@uw.edu 206.685.0317 @guillaumemauger

Greenhouse Gas Scenarios

2000 2050 21000

5

10

15

20

25

30

Year

Glo

bal C

O2 E

miss

ions

(GtC

/yea

r)

RCP 8.5A1FIA2RCP 6.0A1BRCP 4.5B1RCP 2.6OBS

Results from Statistical Downscaling

Increased flood risk

!

Dec

reas

ed

flood

risk

"

Incr

ease

d flo

od ri

sk

Warm Basins Cold Basins

Change in Flood Magnitude for 297 NW rivers 2040s, A1b scenario, ECHAM5 model

Source: Salathé et al 2014

Warm Basins Cold Basins

Source: Salathé et al 2014

Results from Dynamical Downscaling

Change in Flood Magnitude for 297 NW rivers 2050s, A1b scenario, ECHAM5 model

Less error More error

GCM

Nor

mal

ized

Err

or S

core

GCM ranking for the PNW

Climate Projections for the Columbia River Basin*

CMIP3(SRES AIB, B1)

CMIP5:RMJOC-II

*Above The Dalles10%-90% percentilesGray: CMIP3Light blue: CMIP5Blue: overlap

CMIP5(RCP 4.5, 8.5)

CMIP3: RMJOC-I

Change in MAM temperature between 1970-1999 and 2020-2049, RCP8.5

Change in JJA precipitation between 1970-1999 and 2020-2049, RCP8.5