PCMDI’s* Role in Enabling Climate Science Through Coordinated Modeling Activities

Karl E. Taylor *Program for Diagnosis and Intercomparison (PCMDI) Lawrence Livermore National Laboratory

Briefing of BERAC

Washington D.C. 16 October 2012 PCMDI’s dual mission is unique and appropriate for a national lab

• Advance climate science through individual and team research contributions.

 Perform cutting-edge research to understand the climate system and reduce uncertainty in climate model projections.

• Provide leadership and infrastructure for coordinated modeling activities that promote and facilitate research by others.

 Plan and manage “model intercomparison projects” and provide access to multi-model output.

PCMDI’s work is funded by the Climate and Environmental Sciences Division of BER.

BERAC 16 October 2012 K. E. Taylor Outline: PCMDI’s role in coordinated modeling activities

• Overview of the Coupled Model Intercomparison Project (CMIP)

 What is CMIP?

 Historical perspective  International context • PCMDI’s role in CMIP

• CMIP’s scientific impact

 Publications  Multi-model perspective • Samples of CMIP research results (PCMDI & LLNL)

• CMIP’s future

BERAC 16 October 2012 K. E. Taylor What is the “Coupled Model Intercomparison Project” (CMIP)? Highlights of “model intercomparison” history

• ca. 1970s and 1980s: climate model evaluation was largely a qualitative endeavor done by modeling groups

• ca. 1991: Intercomparison Project (AMIP)

 Roughly 30 modeling groups from 10 different countries

 Engaged outside researchers in the evaluation and diagnosis of atmospheric models • ca. 1995: Coupled Model Intercomparison Project (CMIP)

• CMIP3 (2003 – ca. 2013):

 Expts: control, idealized climate change, historical, and SRES (future scenario) runs  Output largely available by 2005 • CMIP5 (2006 – beyond 2016; ongoing and revisited)

 An ambitious variety of “realistic” and diagnostic experiments

 Output largely available by 2012

BERAC 16 October 2012 K. E. Taylor Model intercomparison and modeling culture has evolved:

• More experiments

 Address a wider variety of questions

 Meet the needs of a broader community of users

• More comprehensive models (from atmos. to earth-system)

• More openness in making output available.

• Increased standardization facilitating data exchange

• More model output

• More complete documentation of models/experiments

• New strategies for making output accessible to users BERAC 16 October 2012 K. E. Taylor International context for CMIP: A grass-roots collaborative effort

National Funding Agencies DOE BER $$ Climate Modelers from: WGCM USA, UK, France, Working Group on PCMDI Canada, Germany, Coupled Modeling Australia, Japan, …

BERAC 16 October 2012 K. E. Taylor CMIP5 participating groups: 22 Sept. 2012 59 models available from 24 groups

Primary Group Country Model

CSIRO-BOM Australia ACCESS 1.0, 1.3 BCC China BCC-CSM1.1, 1.1(m) GCESS China BNU-ESM CCCMA Canada CanESM2, CanCM4, CanAM4 DOE-NSF-NCAR USA CCSM4, CESM1 (BGC), (CAM5), (CAM5.1,FV2), (FASTCHEM), (WACCM) RSMAS USA CCSM4(RSMAS) CMCC Italy CMCC- CESM, CM, & CMS CNRM/CERFACS France CNRM-CM5 CSIRO/QCCCE Australia CSIRO-Mk3.6.0 EC-EARTH Europe EC-EARTH LASG-IAP & LASG-CESS China FGOALS- g2, s2, & gl FIO China FIO-ESM NASA/GMAO USA GEOS-5 NOAA GFDL USA GFDL- HIRAM-C360, HIRAM-C180, CM2.1, CM3, ESM2G, ESM2M

NASA/GISS USA GISS- E2-H, E2-H-CC, E2-R, E2-R-CC, E2CS-H, E2CS-R

MOHC UK Had CM3, CM3Q, GEM2-ES, GEM2-A, GEM2-CC NMR/KMA Korea / UK HadGEM2-AO INM Russia INM-CM4 IPSL France IPSL- CM5A-LR, CM5A-MR, CM5B-LR MIROC Japan MIROC 5, 4m, 4h, ESM, ESM-CHEM MPI-M Germany MPI-ESM- HR, LR, P, ESM-P IAM DiagnosticsBERAC WorkshopMRI Japan MRI- AGCM3.2H, AGCM3.2S, CGCM3, ESM1 16 October3 May 2012 2012 K. E. Taylor NCC Norway NorESM1-M, NorESM-ME NCEP USA CFSv2-2011 NICAM Japan NICAM-09 INPE Brazil BESM OA2.3 CMIP: A grass-roots collaborative effort

National Funding Agencies DOE BER $$ Climate Modelers from: WGCM USA, UK, France, Working Group on PCMDI Canada, Germany, Coupled Modeling Australia, Japan, … CMIP: Under the umbrella of an internationally- coordinated research program

United Nations ICSU International Council for Science UNESCO WMO UN Educational, Scientific World Meteorological and Cultural Organization Organization

IOC Intergovernmental Oceanographic Commission

WCRP World Climate Research Programme

Climate Modelers from: WGCM USA, UK, France, Working Group on PCMDI Canada, Germany, Coupled Modeling Australia, Japan, …

Taylor et al., BAMS, 2012 IPCC assessments are separate from the international climate research programs

United Nations ICSU International Council for Science UNESCO WMO UNEP UN Educational, Scientific World Meteorological UN Environmental and Cultural Organization Organization Programme

IOC Intergovernmental Oceanographic Commission

IPCC WCRP Intergovernmental Panel World Climate Research Programme on Climate Change

Climate Modelers from: WGCM PCMDI CMIP Climate USA, UK, France, Working Group on Model Output Research Canada, Germany, Coupled Modeling Archive Australia, Japan, … community

Taylor et al., BAMS, 2012 One component of CMIP: All models make projections of future climate change based on the same set of scenarios

• Different “scenarios” lead to different climate responses

• Models forced similarly exhibit a range of responses

From IPCC AR4 Summary for Policy Makers

BERAC 16 October 2012 K. E. Taylor What contributes to the spread in projections?

• Differences in “scenarios” (i.e. different emissions or concentration prescriptions).

• Differences in “radiative forcing” (radiative impact) of changing atmospheric composition.

• Differences in “climate sensitivity” (i.e., differences in climate feedbacks)

• Differences in the (equally likely) paths of unforced variability exhibited by simulations forced in the same way

BERAC 16 October 2012 K. E. Taylor Forced changes and unforced variability in global mean tropospheric temperature (TLT) in CMIP3 runs

Single simulation

Ensembles of equally likely outcomes

BERAC Courtesy of B.Santer 16 October 2012 K. E. Taylor Projection ranges are initially dominated by model “uncertainty”*, but eventually are dominated by scenario

Unforced variabilty

scenario *nb. The “spread” of m o d e l r e s u l t s i s sometimes without much justification used as a measure of “uncertainty”.

model response

BERAC Hawkins & Sutton, BAMS, 2009 16 October 2012 K. E. Taylor Projection ranges are initially dominated by model “uncertainty”, but eventually are dominated by scenario

From IPCC AR4 Summary for Policy Makers

BERAC 16 October 2012 K. E. Taylor On global scales, the climate future quickly becomes dominated by model and scenario uncertainty.

Unforced variability is important only in the near-term.

Hawkins & Sutton, BAMS, 2009

BERAC 16 October 2012 K. E. Taylor CMIP5 experiments are designed to address the causes of spread in projections and much more.

hindcasts & evaluation forecasts & projection CORE CORE (initialized diagnosis ocean state) TIER 1 TIER 1 TIER 2 “Near-Term” expts. “Long-Term” (decadal prediction) traditional expts. (century & longer) AMIP CORE “time-slice”

TIER 1 Taylor, Stouffer, & TIER 2 Meehl, BAMS, 2012 Atmosphere-Only Simulations BERAC 16 October 2012 (for computationally demanding and NWP models) K. E. Taylor Stephenson (2007, hereafter CS07) used a simple scales (Fig. 4b), which are of much greater relevance climate model to estimate the three different con- for adaptation planning. Our results for global mean tributions to fractional uncertainty. Knutti et al. temperature are consistent with those of Knutti et al. (2008) used data from CMIP3 and from simpler (2008). They also show important similarities to the climate models in a similar analysis but only quan- findings of CS07, but there are also some crucial tified the model uncertainty component. Here, we differences. CMIP5 includes models initializedhave withused the CMIP3the observeddata to estimate the fractional Following CS07, Figs. 3 and 4a both show how state (in particular of the upperuncertainty ocean) associated with all three contributions the contributions to fractional uncertainty vary (Figs. 3, 4a), and extended the analysis to regional as a function of prediction lead time. In Fig. 3 the

• The hope is that through initialization the models will be able to predict the actual trajectory of “unforced” climate variations.

• The hypothesis is that some longer time-scale natural variability is predictable if the initial state of the system is known

The deviation from observations caused by unforced variability Hawkins & Sutton, 2009 can potentially be reduced through initialization.

BERAC 16 October 2012 K. E. Taylor

FIG. 4. The relative importance of each source of uncertainty in decadal mean surface temperature projec- tions is shown by the fractional uncertainty (the 90% confidence level divided by the mean prediction) for (a) the global mean, relative to the warming from the 1971–2000 mean, and (b) the British Isles mean, relative to the warming from the 1971–2000 mean. The importance of model uncertainty is clearly visible for all policy- relevant timescales. Internal variability grows in importance for the smaller region. Scenario uncertainty only becomes important at multidecadal lead times. The dashed lines in (a) indicate reductions in internal variability, and hence total uncertainty, that may be possible through proper initialization of the predictions through assimilation of ocean observations (Smith et al. 2007). The fraction of total variance in decadal mean surface air temperature predictions explained by the three components of total uncertainty is shown for (c) a global mean and (d) a British Isles mean. Green regions represent scenario uncertainty, blue regions represent model uncertainty, and orange regions represent the internal variability component. As the size of the region is reduced, the relative importance of internal variability increases.

AMERICAN METEOROLOGICAL SOCIETY AUGUST 2009 | 1097 The rich set of “long-term” experiments, drawn from several predecessor MIPs, focuses on model evaluation, projections, and understanding

Model Climate Evaluation Projections

ensembles:ensembles: AMIPAMIP && 2020 CC Red subset matches

the entire CMIP3 Control, RCP4.5, last AMIP, & 20 C RCP8.5 experimental suite

mid- millennium LGM E-driven E-driven Holocene & control & 20 C RCP8.5

(clouds) planet aqua

1%/yr CO2 (140 yrs) abrupt 4XCO (150 yrs) 2 Green subset is for fixed SST with 1x & coupled carbon-cycle 4xCO 2 climate models only 1%/ yr CO radiation sees2 (but 1xCO ) 1%/ 2 yr CO carbon cycle2 (butsees 1XCO Understanding ) BERAC 2 16 October 2012 K. E. Taylor CMIP5 output fields cover all parts of the system and include “high frequency” samples.

• Domains (number of monthly variables*):

 Atmosphere (60)  Aerosols (77)  Ocean (69)  Ocean biogechemistry (74) *Not all variables will be saved for all experiments  Land surface & carbon cycle (58) and time-periods  Sea ice (38)  Land ice (14)  CFMIP output (~100) • Temporal sampling (number of variables*)

 Climatology (22)  Annual (57)  Monthly (390)  Daily (53) http://cmip-pcmdi.llnl.gov/cmip5/output_req.html  6-hourly (6)  3-hourly (23)

BERAC 16 October 2012 K. E. Taylor What is PCMDI’s leadership role in CMIP? PCMDI contributes in a variety of ways to CMIP’s success

• CMIP5 planning: PCMDI forges community consensus and provides detailed specifications for

 Experiment design

 List of requested model output

 Format and structure of model output, as well as required metadata

• Software infrastructure development and support to enable community analysis of CMIP results

• Web site to provide information needed by modeling groups and users.

BERAC 16 October 2012 K. E. Taylor Data volumes have grown by many orders of magnitude

• Early 1990’s (e.g., AMIP1, PMIP, CMIP1): modest collection of monthly mean 2-D fields: ~1 Gbyte

• Late 1990’s (AMIP2): large collection of monthly mean and 6-hourly 2-D and 3-D fields: ~500 Gbytes

• 2004 (CMIP3): fairly comprehensive output from both ocean and atmospheric components; monthly, daily, & 3- hourly: ~36,000 Gbytes

• 2010 (CMIP5) 1000 - 3000 Tbytes (1 TB =1000 GB)

This required new approaches for delivering data to users!

BERAC 16 October 2012 K. E. Taylor CMIP3 data handling: ESG* central archive at PCMDI

climate modeling centers

Center 1 Center 3 Center 5 • Data shipped to PCMDI on hard disks Center 2 Center 4 • Delayed availability • Hindered corrections

PCMDI • Search service via (data server, catalog, web gateway web interface) • Download from single location (ftp, http) • Fragile dependence on a single server.

data users (climate model analysts worldwide)

BERAC 16 October 2012 *ESG = Earth System Grid K. E. Taylor CMIP5 new approach: Distributed data archive (ESGF*)

Data Data

Data Data Search catalog & service

Data portal Search catalog Search catalog Data & service & service

Data Users

portal portal Data Search catalog Search catalog & service & service Data Data

Data Data Data Data

BERAC 16 October 2012 *ESGF = Earth System Grid Federation K. E. Taylor All data can be browsed through a single portal because index nodes are federated.

“index nodes” are federated Data Data Data Data Search catalog & service

Data portal Search catalog Search catalog Data & service & service

Data Users

portal portal Data Search catalog Search catalog & service & service Data Data

Data Data Data Data

BERAC 16 October 2012 K. E. Taylor Once desired datasets have been found, user harvests data directly from the nodes.

Data Data

Data Data Search catalog & service

Data portal Search catalog Search catalog Data & service & service

Data Users

portal portal Data Search catalog Search catalog & service & service Data Data

Data Data Data Data

BERAC 16 October 2012 K. E. Taylor ESGF is unparalleled in capabilities and complexity

• Diagram does not show:

 Script-driven direct search and retrieval of data (bypassing portals)

 Server-side computing services

 Security & authentication layer

Data Data • Also: Data Data  Search catalog PCMDI and other & service major data centers Data have replicated portal Search catalog Search catalog Data high-demand & service & service Data datasets. Users

portal portal Data

CMIP5 output can be obtained Search catalog Search catalog at http://pcmdi9.llnl.gov Data & service & service Data

Data Data Data Data BERAC 16 October 2012 K. E. Taylor What has CMIP done to advance climate modeling? CMIP facilitates more comprehensive scrutiny of model behavior

• Expertise is limited at individual modeling groups

• Broad community of experts can analyze output from multiple models with ease.

• 1000’s of scientists have downloaded data from CMIP

• To date, more than 600 publications have been registered claiming to report on CMIP3 results, and more than 250 publications have been prepared based on CMIP5 results (which have been available for only a year or so).

BERAC 16 October 2012 K. E. Taylor Record of CMIP5 publications

See http://cmip.llnl.gov/cmip5/publications/allpublications

BERAC 16 October 2012 K. E. Taylor What has the multi-model perspective yielded?

• Visibly demonstrates that model results are uncertain

• Provides a range of (equally?) plausible projections for planners

• Has been used as a cornerstone for recent IPCC reports: In the 4th Assessment Report

 About 75% of 100 figures in AR4 Chapters 8-11 are based on CMIP3

 4 of the 7 figures AR4 “Summary for Policy Makers” are based on CMIP3

• Some argue the multi-model ensemble ensures more robust conclusions than can be obtained with a single model

BERAC 16 October 2012 K. E. Taylor CMIP establishes some benchmark experiments that allow us to gauge changes in model performance.

• AMIP runs (prescribed SST’s and seaice)

• CMIP control runs (variability characteristics)

• Historical runs (1850 – present)

• Idealized 1%/yr CO2 increases (determine climate sensitivity)

BERAC 16 October 2012 K. E. Taylor Changes in CMIP model errors (ca. 2000 to ca. 2005)

1.0 models, ca. 2000 models, ca. 2005 mean of group 0.8 change: ca. 2000 to ca. 2005

all

0.6 non-flux- adjusted flux- adjusted all 0.4

Normalized RMS Error flux- 0.2 adjusted non-flux- adjusted all flux- non-flux- adjusted adjusted 0.0 Mean Sea-Level Surface Air Precipitation Pressure Temperature Variable and Model Category

BERAC 16 October 2012 Gleckler, Taylor, & Doutriaux, JGR, 2008 (also IPCC AR4) K. E. Taylor Relationships between observables and projections have been gleaned from CMIP results, that indicate which models might be reliable

Response of snow cover to global warming in models is related to their snow response to spring warming

BERAC Hall and Xu, GRL, 2006 16 October 2012 K. E. Taylor Can important research questions be addressed by analysis of CMIP5 results? Example: What causes the spread in climate responses by models and is the uncertainty narrowing?

• One of the CMIP5 experiments was designed to answer this question

 The CMIP5 equilibrium pre-industrial control is subjected to an abrupt

quadrupling of CO2. 

Temperature approaches a new equilibrium over many decades.

BERAC 16 October 2012 K. E. Taylor The net flux of global radiation is approximately proportional to surface temperature Following Gregory et al. (2004), express the energy balance of the climate system as: N = F – Y ΔT

-2 Radiative forcing (F Nin =Wm F –) Y ΔT

Slope gives feedback (-Y in Wm-2 K-1) ) -2 N (W m

Eqm Climate Sensitivity (ΔT4x in K)

ΔT (K) In CMIP5, we will only be part way along this curve… BERAC 16 October 2012 K. E. Taylor We diagnosed climate sensitivity and feedback parameters for available CMIP5 models.

) 0 -1

K equil. climate sensitivity: 2.1 – 4.7 K

-2 -0.2 -0.4 MIROC5 -0.6 INM-CM4 -0.8 MRI-CGCM3 CNRM-CM5 CanESM2 -1 NorESM1-M

-1.2 IPSL-CM5A-LR

-1.4 HadGEM2-ES -1.6 -1.8 CSIRO-MK3-6-0

1 / Climate Feedback Parameter (Wm -2 1 2 3 4 5 Eqm Climate Sensitivity (K)

BERAC Andrews et al., GRL, 2012 16 October 2012 K. E. Taylor We diagnosed climate sensitivity and feedback parameters for available CMIP5 models.

) 0 Relatively narrow scatter -1

K indicates feedbacks, not forcing,

-2 -0.2 are primarily responsible for the -0.4 MIROC5 range of climate sensitivities -0.6 INM-CM4 -0.8 MRI-CGCM3 CNRM-CM5 CanESM2 -1 NorESM1-M

-1.2 IPSL-CM5A-LR

-1.4 HadGEM2-ES -1.6 -1.8 CSIRO-MK3-6-0

1 / Climate Feedback Parameter (Wm -2 1 2 3 4 5 Eqm Climate Sensitivity (K)

BERAC Andrews et al., GRL, 2012 16 October 2012 K. E. Taylor Differences in cloud feedback are responsible for a large fraction of the range of feedback strengths

1 )

-1 IPSL-CM5A-LR K -2

0.5 CSIRO-MK3-6-0 CanESM2 HadGEM2-ES MRI-CGCM3 0 INM-CM4 NorESM1-M

CNRM-CM5

-0.5 MIROC5 Cloud Feedback Parameter (Wm -1 -1.8 -1.6 -1.4 -1.2 -1 -0.8 -0.6 -0.4 -0.2 0 Net Feedback Parameter (Wm-2 K-1)

BERAC Andrews et al., GRL, 2012 16 October 2012 K. E. Taylor CMIP5 offers opportunities to evaluate and diagnose reasons for differences in cloud feedback among models

• “Satellite simulator” output collected for the first time.

• The “ISCCP simulator” code diagnoses from model cloud vertical distribution and optical properties the fraction of clouds occupying each of ISCCP’s cloud “categories”

Courtesy of Swati Gehlot BERAC 16 October 2012 K. E. Taylor

(a) 1xCO Cloud Mean Cloud Fraction Fraction: 58.12 % −2 −1 2 Multi-model(d) LW Cloud Feedback: mean 0.20 W m Kof CFMIP1 results 50 5 0.15

180

) 0.1 4 310 0.05 3 hPa 440 −2 −1 % 0 W m K 1xCO2 560 2 CTP (hPa) Each cloud fraction shown−0.05 is 58.1 % 680 1 “visible” from space (i.e., the 800 −0.1 non-overlapped cloud fraction) 1000 0 −0.15

(b) 2xCO Mean Cloud Fraction: 56.81 % −2 −1 2 (e) SW Cloud Feedback: 0.37 W m K 50 5 0.15

) CTP ( ) CTP 180 0.1 4 310 0.05 hPa 440 3 −2 −1 2xCO % 0 W m K 2 560 2 CTP (hPa) −0.05 56.8 % 680 1 CTP ( CTP −0.1

800

1000 0 −0.15

−1 (c) "Cloud Fraction: −0.46 % K (f) Net Cloud Feedback: 0.58 W m−2 K−1 50 0.15 0.15

180 0.1 0.1 310 Multiply by Cloud Radiative Kernels 0.05 0.05 440 at each location and month, −1 −2 −1 Change: 0 % K 0 W m K 560 then averaged annually, CTP (hPa) -0.5 % −0.05 −0.05 680 -1 globally, and across models… K 800 −0.1 −0.1

1000 −0.15 −0.15 BERAC 0 0.3 1.3 3.6 9.4 23 60 380 0 0.3 1.3 3.6 9.4 23 60 380 16 October 2012 ! Zelinka et! al., J. Climate, 2012a K. E. Taylor CTP (hPa) CTP Optical Depth

(a) 1xCO Mean Cloud Fraction: 58.12 % −2 −1 Cloud2 Fraction (d) CloudLW Cloud Feedback: Feedback 0.20 W m K 50 5 0.15

180 0.1 4 310 0.05 440 3 −2 −1 % 0 W m K LW 1xCO2 560 2 CTP (hPa) 0.20 −0.05 58.1 % 680 -2 -1 1 Wm K 800 −0.1

1000 0 −0.15

(b) 2xCO Mean Cloud Fraction: 56.81 % −2 −1 2 (e) SW Cloud Feedback: 0.37 W m K 50 5 0.15

180 0.1 4 310 0.05 440 3 SW −2 −1 2xCO % 0 W m K 2 560 2 0.37 CTP (hPa) −0.05 -2 -1 56.8 % 680 Wm K 1 800 −0.1

1000 0 −0.15

−1 (c) "Cloud Fraction: −0.46 % K (f) Net Cloud Feedback: 0.58 W m−2 K−1 50 0.15 0.15

180

) CTP (hPa) CTP (hPa) (hPa) CTP ) CTP 0.1 0.1 310 0.05 0.05 Net hPa 440 −1 −2 −1 Change 0 % K 0 W m K 560 0.58 CTP (hPa) -0.5 % −0.05 −0.05 -2 -1 680 Wm K -1 K 800 −0.1 −0.1

1000 −0.15 −0.15 BERAC 0 0.3 1.3 3.6 9.4 23 60 380 0 0.3 1.3 3.6 9.4 23 60 380 16 October 2012 ! ! K. E. Taylor CTP ( CTP Optical Depth Optical Depth Using kernel method, CMIP cloud radiative effect can be resolved into components.

)

-1 1.0 K -2

0.5

0 Cloud Feedback (W m Total Amount Altitude Optical Depth Residual

Component

Zelinka et al., J. Climate, 2012a BERAC 16 October 2012 K. E. Taylor Spread and mean of cloud components has not changed much between CMIP3 and CMIP5

)

-1 1.0 K -2

0.5

0 Cloud Feedback (W m Total Amount Altitude Optical Depth Residual

Component

Zelinka et al., J. Climate, submitted BERAC 16 October 2012 K. E. Taylor Cloud-induced net radiation anomalies plotted for CMIP5 models

(Positive = heating)

!RC = F +!C!TS

 intercept (FC) = “fast” adjustment  slope (αC) = feedback

-Robust positive cloud “fast” adjustments -Positive cloud feedbacks in 5 out of 6 models -Some early departures from linearity

Zelinka et al., J. Climate, submitted BERAC 16 October 2012 K. E. Taylor Accounting for the “fast adjustments” results in a stronger negative optical depth feedback (optical depth increases with warming): So total cloud feedback may be weaker than in earlier models.

)

-1 1.0 K -2

0.5

0 Cloud Feedback (W m Total Amount Altitude Optical Depth Residual

Component

Zelinka et al., J. Climate, submitted BERAC 16 October 2012 K. E. Taylor If inter-model spread in cloud feedbacks remains large, does this imply a lack of improvement in the simulation of clouds?

• We consider the simulation of the climatological distribution of clouds against satellite observations from two recent model ensembles

 CFMIP1 (~CMIP3) – ca. 2000-2005

 CFMIP2 (~CMIP5) – ca. 2012

BERAC 16 October 2012 K. E. Taylor How often does a cloud occur?

Climatological distribution of cloud amount (τ > 1.3)

CFMIP1 Models CFMIP2 Models

Multi-Model Mean

ISCCP MODIS

Satellite Observations

Klein et al., submitted BERAC 16 October 2012 K. E. Taylor Despite multi-model mean, some individual models have improved.

model ca. 2004 model ca. 2011

HADSM3 HADGEM2A

MET OFFICE MODELS

CAM3 CAM5

COMMUNITY ATMOSPHERE MODELS

SATELLITE OBSERVATIONS

ISCCP MODIS

Klein et al., submitted Models have improved simulation of optical depths

• Climate models often have a compensating error between cloud amount and cloud optical depth (Zhang et al. 2005)

 Models are tuned to the time-mean radiation balance

 They commonly achieve this by simulating too many optically thick clouds and too few optically thin ones to offset too little cloud cover

Improvement in the CAM !"#$%&'#()*"+#),&-+"+.)/0),(&1.2)3456)!)7)89)

Obs.

OBSER- UKMO CAM VATIONS MIROC CAN BERAC Kay et al., 2012 MODEL FAMILIES 16 October 2012 Klein et al.,K. submittedE. Taylor The good news is that models have quantitatively improved in the simulation of clouds!

• Consider the annual cycle of the global distributions of cloud amount and cloud properties (CTP and τ) • For each model, compute the normalized root-mean square errors. W idespread e r r o r reduction of 10-50% in simulation of cloud p r o p e r t i e s , w i t h smaller improvements ca. 2004 ca. 2004 in cloud amount.

ca. 2011 ca. 2011

BERAC 16 October 2012 Klein et al., submitted K. E. Taylor Where is CMIP headed? CMIP has become an integral part of climate modeling

• Modeling groups perform the core CMIP experiments as part of their model improvement efforts

• The IPCC continues to provide top-down incentives to provide projections based on common scenarios

• The scientific benefits of providing multi-model output for community analysis are now well established

• PCMDI, in cooperation with the WCRP, is working to establish climate model metrics that can be used to identify merits and shortcomings of models, relative to one another models.

• It can be anticipated that there will be a CMIP6, but that it will unlikely attempt to take on more than CMIP5.

BERAC 16 October 2012 K. E. Taylor Concluding remarks

• With BER’s support, PCMDI has made essential contributions to the success of coordinated modeling activities.

 Research contributions

 Project “management” responsibilities • CMIP has enabled a diverse community of researchers to evaluate models from a variety of perspectives and use model simulations in an enormous breadth of research.

• The ongoing uncertainty in projection accuracy stems from model treatment of clouds.

 A target of BER’s Atmospheric Radiation Measurement (ARM) program • A distributed data archive infrastructure has ben developed that could serve other projects and scientific communities

BERAC 16 October 2012 K. E. Taylor CMIP website: http://cmip-pcmdi.llnl.gov

BERAC 16 October 2012 K. E. Taylor