Interpre'ng Model Results
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1 Interpre'ng Model Results Clara Deser Na'onal Center for Atmospheric Research Boulder, CO CESM Tutorial, 12 August 2016
2 Interpre'ng Model Results 1) What kind of model? 2) What kind of simula'on? 3) What can (and can t) you learn from it?
3 Types of Models and Experiments Atmosphere Free-running coupled climate models Ocean Land Sea Ice Sea Ice Biology Land Examples: NCAR Community Earth System Model (CESM) CMIP5 models used in IPCC Assessment Reports
4 Types of Models and Experiments Atmosphere Free-running coupled climate models Ocean Land Sea Ice Sea Ice Biology Land Produce their own internally-generated sequences of climate variability (i.e., ENSO, decadal) which need NOT match the observed sequence. However, the sta's'cs of the variability should match observa'ons.
5 Eastern Tropical Pacific Sea Surface Temperature Anomalies El Nino C La Nina El Nino C La Nina DiNezio et al., 2016: Climate Dynamics
6 Eastern Tropical Pacific Sea Surface Temperature Anomalies El Nino C La Nina El Nino C La Nina Are the characteris'cs of ENSO well simulated? amplitude, period, spa<al pa=ern, mechanisms Need long enough simula'ons to evaluate.
7 Types of Models and Experiments Atmosphere Free-running coupled climate models Ocean Land Sea Ice Sea Ice Biology Land Control simula/ons (1000+ years) No changes in radia<ve forcing (e.g., fixed GHGs). >> Robust sta's'cs of the model s internal variability in an unchanging climate (e.g., ENSO, PDO, AMO, MJO, ).
8 Power Spectrum of Nino3.4 SST Index HadISST observa'ons (first 100 years) (second 100 years) CESM1 control simula'on (eighteen 100-year segments) CCSM4 control simula'on (thirteen 100-year segments) DiNezio et al., 2016: Climate Dynamics
9 Explore more examples from the Climate Variability Diagnos'cs Package www2.cesm.ucar.edu/working-groups/cvcwg/cvdp
10 Explore more examples from the Climate Variability Diagnos'cs Package www2.cesm.ucar.edu/working-groups/cvcwg/cvdp
11
12 SST Standard Devia'ons (DJF means) 100
13 SST Standard Devia'ons (DJF means) Spread in Observa'ons 100
14 Which Observational Data Set to Use? The only data portal that combines data discovery, metadata, figures and world-class citable exper'se hhps://climatedataguide.ucar.edu Schneider, Deser, Fasullo and Trenberth: EOS, 2013
15 SST Standard Devia'ons (DJF means) Spread in Observa'ons 100 Observa'onal record is only about 100 years long, so might want to look at 100 year segments of the control run for a more informa've comparison.
16 SST Standard Devia'ons (DJF means) Large spread in 100 year segments of the control run.
17 SST Standard Devia'ons (DJF means) Large spread in 100 year segments of the control run. Brings up challenges with model evalua'on: How well do we know the observed variability? How do we validate our models given the limited span of the observa'onal record?
18 Types of Models and Experiments Atmosphere Free-running coupled climate models Ocean Land Sea Ice Sea Ice Biology Land Historical simula/ons (generally ) Prescribed evolu<on of radia<ve forcing (GHG, aerosols, volcanoes, solar irradiance, stratospheric ozone, ). >> Internal variability PLUS response to radia've forcing.
19 Explore more examples from the Climate Variability Diagnos'cs Package 38 CMIP5 models (1 run each) vs. 40 CESM1 simula'ons What s the difference?
20 SST Standard Devia'ons (DJF means) CMIP5 models: Large inter-model spread in variability Due to different model physics or limited sampling?
21 Explore more examples from the Climate Variability Diagnos'cs Package CESM1 control vs. historical simula'ons What s the difference?
22 SST Standard Devia'ons (DJF means) Four 100-year segments of CESM1 Control Four 100-year CESM1 Historical simula'ons
23 SST Standard Devia'ons (DJF means) Four 100-year segments of CESM1 Control Four 100-year CESM1 Historical simula'ons Does ENSO increase due to radia've forcing? Need many runs (large sample size) to determine.
24 Types of Models and Experiments Atmosphere Free-running coupled climate models Ocean Land Sea Ice Sea Ice Biology Land Projec/ons (present-2100) RCP2.5, RCP4.5, RCP8.5 radia<ve forcing scenarios Internal variability PLUS response to radia've forcing.
25 40 RCP8.5 & 15 RCP4.5 simula'ons with CESM1 Robust assessment of benefits of climate mi<ga<on Climate and Human Systems Project //chsp.ucar.edu/brace-benefits-reduced-anthropogenic-climate-change
26 Probability of Exceeding the Historical ( ) Record Summer Temperature in RCP8.5 RCP4.5 80% 41% Lehner, Deser and Sanderson, 2015: Clima<c Change
27 Probability of Exceeding the Historical ( ) Record Summer Temperature in RCP8.5 RCP4.5 80% 41% 95x1 95x40 20x15 20x40 California OBS CESM RCP4.5 RCP8.5 historical future
28 Types of Models and Experiments Free-running coupled climate models Constrained model simula'ons
29 Types of Models and Experiments Free-running coupled climate models Constrained model simula'ons Hypothesis tes'ng Physical understanding Direct comparison with nature (ahribu'on)
30 Types of Models and Experiments Free-running coupled climate models Constrained model simula'ons Coupled Models or Component Models (atmosphere, ocean) Hypothesis tes'ng Physical understanding Direct comparison with nature (ahribu'on)
31 Some Examples of Constrained Model Simula'ons Response to Tropical Pacific SST varia'ons Atmospheric Model Coupled Model
32 Some Examples of Constrained Model Simula'ons Response to Tropical Pacific SST varia'ons Atmospheric Model Ø Specify observed SST evolu'on in the Tropics, climatological seasonal cycle elsewhere. AMIP protocol TOGA: Tropical Ocean, Global Atmosphere
33 Some Examples of Constrained Model Simula'ons Response to Tropical Pacific SST varia'ons Coupled Model Ø Nudge model s SST anomalies to observa'ons in the eastern Tropical Pacific, allow full ocean-atmosphere coupling elsewhere. Pacemaker protocol
34 Some Examples of Constrained Model Simula'ons Response to Tropical Pacific SST varia'ons Coupled Model Ø Nudge model s SST anomalies to observa'ons in the eastern Tropical Pacific, allow full ocean-atmosphere coupling elsewhere. Pacemaker protocol Global warming hiatus, ENSO response
35 Global Mean Surface Temperature Anomalies Free-running simula'ons Observa'ons Tropical Pacific Pacemaker simula'ons Kosaka and Xie, 2016: Nature Geosciences
36 Global Mean Surface Temperature Anomalies hiatus Free-running simula'ons Observa'ons Tropical Pacific Pacemaker simula'ons Kosaka and Xie, 2016: Nature Geosciences
37 ENSO Response El Nino minus La Nina Composite Nino3.4 SST > 1 σ Nino3.4 SST < -1 σ Deser and Simpson, in prepara<on
38 Winter SLP (hpa) Observa'ons CESM1 Pacemaker 10-member avg Gray shading: ENSO response not significant at the 5% level
39 Winter SLP (hpa) Observa'ons CESM1 Pacemaker 10-member avg Differences between ensemble members due en'rely to sampling variability (ENSO events are the same). Gray shading: ENSO response not significant at the 5% level
40 Winter SLP (hpa) Observa'ons CESM1 Pacemaker 10-member avg What might observa'ons have looked like had a different sequence of internal variability occurred?
41 Winter SLP (hpa) Observa'ons CESM1 Pacemaker 10-member avg What might observa'ons have looked like had a different sequence of internal variability occurred? Is the model s response realis'c? Do the observed and modeled ENSO responses come from the same distribu<on?
42 Types of Models and Experiments Hierarchy of Control Runs Fully Coupled Atmosphere- Mixed Layer Ocean Atmosphere Only Ø Atmospheric vs. Coupled Processes
43 //webext.cgd.ucar.edu/mul'-case/cvdp_ex/cesm1-lens-controls/
44 Precipita'on Standard Devia'on (DJF) Observa'ons Fully Coupled (1800 years) Coupled ocean mixed layer (900 years) Atmosphere-only (2600 years)
45 Types of Models and Experiments Free-running coupled climate models Constrained climate models Model hierarchy All have u'lity. The most appropriate one depends on the ques'ons you are asking.
46 Extra
47 The CESM Large Ensemble Project A Community Resource for Studying Climate Change in the Presence of Internal Climate Variability
48 The CESM Large Ensemble Project www2.cesm.ucar.edu/models/experiments/lens BAMS doi: /bams-d
49 CESM-LE Experimental Design Generate ensemble w/ round-off air temperature differences 42 members to date Figure from doi: /bams-d
50 First Days of the CESM-LE: Determinis'c Weather to Chao'c Climate Figure from Vineel Ye=ella (University of Colorado)
51 The CESM-LE is a Big Data Project. Original proposal was 1850 control runs and 30 ensemble members: -10,100 simulated years -21 million core-hours on NSF supercomputer Yellowstone -3 weeks per ensemble member -225 (600) Terabytes of post-processed (raw) output
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Contents of this file
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