Intraseasonal, seasonal, and interannual variations of the Arctic temperature in paleoclimates, present, and future experiments in CMIP5 model outputs Intraseasonal, seasonal, and interannual variations of the Arctic temperature in paleoclimates, present, and future experiments in CMIP5 model outputs Shigeo Yoden, Eriko Nishimoto, Yoko Naito, and Kai Shinhara Dept. Geophysics, Kyoto Univ., Japan Shigeo Yoden, Eriko Nishimoto, Yoko Naito, and Kai Shinhara Dept. Geophysics, Kyoto Univ., Japan Workshop of the SPARC Temperature Trends Activity April 9-10, 2015, Victoria, BC, Canada

stratosphere troposphere  Non-Gaussian nature of internal interannual variability Nishizawa and Yoden (2005, JGR )  “Distribution functions of a spurious trend in a finite length data set with natural variability” in an MCM  normalized pdfs of monthly [T] at the North Pole

 Example of cooling trend run ‑ 96 ensembles of 50-year integration ‑ with external linear trend -0.25K/year around 1hPa Natural variability small in summer (July) large in winter (Feb.)  Detectability of cooling trend with a finite-length data 96 ensembles of 50-year integration with external linear trend  K/year around 1hPa

necessary data length [years] to detect a linear trend of - 0.5K/decade with 90% conf. month pressure [hPa] 0.1 JanDec 100 necessary magnitude of the trend [K/decade] to detect with a 20-year dataset  Seasonally dependent detectability How many years do we need to get a statistically significant trend ? How small trend can we detect in finite length data with a statistical significance ? T(North Pole) 10

1. Introduction  Intraseasonal and interannual variations of polar temperature: largely internal variation in the nonlinear system, such as SSW, vortex intensification, … vs. external forcings such as volcanic eruption, solar cycle, anthropogenic effects, etc.  Seasonal cycle: periodic response to the external solar forcing (revolution of the earth) with large difference between the two poles in dynamically active season (1979~1997) (Yoden et al., 2001)

 Polar-night Jet Oscillation (PJO): an important component of intraseasonal variations Kuroda and Kodera (2001) low-frequency internal variations in polar winter correspondence to the occurrence of SSW and VI slow descent of temperature anomaly at the North Pole to the upper troposphere

 Motivation: What’s about such intraseasonal, seasonal, and interannual variations in the Arctic temperature in paleoclimates, present or future climate ?  Purpose of this study: Analyses (fundamental statistics, EOF, cluster) of CMIP5 model outputs to make model intercomparison Paleoclimates:  Last Glacial Maximum (LGM) 21,000 BP(before present)  Mid-Holocene (MN) 6,000 BP Present:  Pre Industrial Control (PI) 1,850 AD Future:  Abrupt 4xCO2 (4xCO2)

2. CMIP5 model output data  Seven models  Four experiments ModelLid HeightLevels Above 200 hPa High/Low Horizontal grid numbers MIROC-ESM hPa8063High 128 ✕ 64 MPI-ESM-P0.01 hPa4725High 196 ✕ 98 MRI-CGCM30.01 hPa4825High 320 ✕ 160 IPSL-CM5A-LR0.04 hPa3922Middle 96 ✕ 95 GISS-E2-R0.1 hPa4019Middle 144 ✕ 90 CCSM hPa2713Low 288 ✕ 192 CNRM-CM510 hPa31 9Low 256 ✕ 128 experimentNameyearCO2 [ppm] paleoclimateLast Glacial Maximum (LGM)21,000 BP 185 paleoclimateMid-Holocene (MH) 6,000 BP 280 presentPre Industrial Control (PI) 1850 AD 285 futureAbrupt 4xCO2 (4xCO2)hypothetical1140

3. Fundamental statistics  Vertical profiles of annual and global mean temperature LGM CO2 = 185 ppm MH 280 ppm 285 ppm PI 1140 ppm 4xCO2 LGM – PI MH – PI 4xCO2 – PI MIROC-ESM MPI-ESM-P MRI-CGCM3 IPSL-CM5A-LR GISS-E2-R CCSM4 CNRM-CM5

 Latitude-height sections of seasonal and zonal mean temperature (seven model ensemble-mean) LGM MH PI 4xCO2 JJA LGM MH PI 4xCO2 DJF

 Latitude-height sections of seasonal and zonal mean temperature difference from PI Control JJA DJF LGM – PI MH – PI 4xCO2 – PI

 Latitude-height sections of standard deviations among seven models on seasonal & zonal mean temperature LGM MH PI 4xCO2 JJA LGM MH PI 4xCO2 DJF

 Month-to-month variations of North Pole temperature for seven models in four experiments LGM MH PI 4xCO2 MIROC-ESM MPI-ESM-P MRI-CGCM3 IPSL-CM5A-LR GISS-E2-R CCSM4 CNRM-CM5 10 hPa 100 hPa 700 hPa

 Month-to-month variations of the standard deviation (interannual variation) of North Pole temperature for seven models in four experiments LGM MH PI 4xCO2

4. EOF analysis of North Pole temperature  T(925 hPa < p < 10 hPa, t)  7 models x 4 experiments x 100 years = 2,800 years x 12 months = 33,600 months EOF1 EOF2 (70.2%) (16.6%)

 Month-to-month variation of distribution in (PC1, PC2) large amplitude 10 %  “extreme” other 90% warm cold events

 Histograms of extreme (top 10% large-amplitude) events cold events warm events cold events phase angle

 Time-height composites of extremely warm events for each month Nov (81, 95.3%) Dec (234, 79.1%) Jan (393, 59.9%) Feb (553, 53.2%) Mar (515, 54.7%) Apr (151, 44.8%)

 Time-height composites of extremely cold events for each month Nov (4, 4.7%) Dec (62, 20.9%) Jan (263, 40.1%) Feb (486, 46.8%) Mar (427, 45.3%) Apr (186, 55.2%)

 Month-to-month variations of extremely warm events and cold events (33,600 months x 10% = 3,360 month)

 Month-to-month variations of extremely warm events and cold events for LGM, MH, PI, and 4xCO2

 Month-to-month variations of extremely warm events and cold events for seven models High top: MIROC, MPI, MRI Middle: IPSL, GISS Low top: CCSM4, CNRM

5. Cluster analysis of North Pole temperature  In EOF analysis, each month is treated independently  Time evolution of (PC1,PC2) for 5 months (D,J,F,M,A) in 7 models x 100 years = 700 years for 4 experiments Hierarchical Tree Plot Distribution of distance number of clusters

 Distribution of distance for LGM, MH, PI, and 4xCO2 number of clusters LGM MH 4xCO2PI

 Time-height sections for each cluster in 4 experiments warm2(170) warm2(322) warm2(201) warm2(112) cold1(331) cold1(179) cold1(109) cold1(90) warm3(317) cold2(284) LGM MH PI 4xCO2 warm1(149) warm1(262) warm1(161) warm1(85)

 Number of members in each cluster LGM MH PI 4xCO2

 Number of members in each cluster divided into each model 1: MIROC 2: MPI 3: MRI 4: IPSL 5: GISS 6: CCSM4 7: CNRM

 Time evolution of (PC1,PC2) for 5 months (D,J,F,M,A) in 4 exp. x 100 years = 400 years for 7 models  Distribution of distance for seven models  number of clusters

 Time-height sections for each cluster in 7 models

 Number of members in each cluster MIROC MPI MRI IPSL GISS CCSM4 CNRM

 Number of members in each cluster divided into each experiment, LGM, MH, PI, and 4xCO2

6. Summary  Intraseasonal, seasonal, and interannual variations in the Arctic temperature are described for four experiments of paleoclimates, present and future climate in CMIP5 model outputs.  Fundamental statistical analysis shows large sensitivity of climatology and variations in dynamically active season, particularly in low top models.  EOF analysis extracts PJO-like variations as extremely warm events and cold events. Extreme events are more frequent in high-top models than in low-top models.  Cluster analysis can determine intraseasonal evolution of PJO-like oscillation (warm and cold), but we need further experience for better use of it.

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