Global Warming Hiatus Mátyás Csiky, Harold Eyster, & Sebastian Saldivar

Hiatus in warming in Earth warmed 0.5°C in 25 years after 1970s. Observed temperature was mostly constant since Black: Observed temperature anomaly Colored: Simulated temperature anomalies Observed & CMIP5 global mean surface temperature

Hiatus not predicted by models This observed warming cessation is inconsistent with climate model simulations. Red=observed gray=simulated Source: IPCC WG1 5th assessment, 2013 Histogram shows the normalized density of the per-decade increase in temperature of the observed (red) and simulated (gray)

Global CO2 levels CO2 levels continued to increase during Hiatus Exceeded 400 ppm in May 2013 at Mauna Loa of Hawaii Source: Robert A. Rohde, 2009

Other metrics of hiatus  Global Mean temperature constant  Heat waves (Russia summer 2010 and USA July 2012)  Arctic sea ice reached record lows in 2007 and 2012 Source: Yu Kosaka & Shang- Ping Xie, 2013 JJA SAT over the southern USA (°C) Annual-mean global near-surface temperature anomalies °C DJF Southern Oscillation Anomaly DJF Sea Level Pressure near the Aleutian Islands (hPa) September Arctic sea ice extent (10 6 km 2 )

Does this hiatus disprove Climate Change? Are environmentalists and climate scientists wrong about the truth of climate change? Or is there an explanation for the pause in warming that is consistent with climate change?

Scientific hypotheses  Subsurface ocean warming  Radiative warming dominated by pacific cooling  Stratospheric water vapor decrease  Reduction in solar forcing  Aerosol increase by volcanos  Model oversensitivity to GHGs

Models & observations over 15 yr periods Observations oscillate around models in the short run. Red=observed Gray=simulated Source: IPCC WG1 5th assessment, 2013 In , observed temp was above 93 of 114 models. In , observed temp was below nearly all of the models. GMST anomaly

Models consistent with GMST trends over last 60 years But over a longer historical time scale, the models are quite accurate. Decadal hiatus events may occur in future but global warming trend very likely to continue with GHG increase (Kosaka & Xie 2013) Red=observed gray=simulated Source: IPCC WG1 5th assessment, 2013

Sea level continued to rise Even with warming pause ( ), sea level continued to rise at close to the same rate of previous years (IPPC WG1AR5 2013) → Subsurface ocean warming

Seasonality of the hiatus SAT hiatus confined to winter. Global temperature continues to rise in summer (e.g. Arctic Ice) Tropical influence more pronounced in winter (but greater variation) Source: Yu Kosaka & Shang-Ping Xie, 2013 Tropics N. Extratropcis Tropics N. Extratropics Temperature anomaly in winter (blue) and summer (red) Probability density of temp. trends in winter (blue) & summer (red)

Kosaka and Xie’s methods Datasets: SAT and SST from HadCRUT dataset Sea Level Pressure from HadSLP2 set Precipitation from GPCP dataset Method: Restore SST in box to the observed pattern in this region by modifying the surface heat fluxes Temperature trend in boreal winter in °C per 11 yrs Source: Yu Kosaka & Shang-Ping Xie, 2013

El Niño / La Niña Oscillations Reverse conditions cause La Niña When Easterly winds weaken, warm conditions→ El Nino Southern Oscillation (ENSO). Occur every 2-7 yrs. Easterly winds in Pacific→45cm higher water in west→ Humboldt current along S.A.→ Warmer water in west Pac.

El Niño: Pacific SST and global weather events Monthly Sea Surface Temperature Anomalies

El Niño / La Niña fluctuations Large El Niño anomaly(warm) in 1998, followed by large La Niña (cold) events in last 15 years.

Hiatus driven by equatorial Pacific? El Niño-Southern Oscillation (ENSO) affecting climate Currently La Niña phase ->Pacific surface cooling Source: Held, 2013 Observed Global mean surface temperature relative to the 1961–90 mean:

POGA-H successfully predicts hiatus Purple: HIST Red: POGA-H Black: Observations Global temperature anomalies Source: Yu Kosaka & Shang-Ping Xie, 2013 HIST - radiative forcing POGA-H -takes variable radiative forcing and tropical Pacific SST as inputs POGA-H (r=.97) explains observed temperature trends better than HIST (r=.90)

ENSO affects global temperature Source: Yu Kosaka & Shang-Ping Xie, 2013 How much of the observed global warming is due to the tropical Pacific? Red= POGA-C Green= Pacific region SAT Seasonal global anomaly modeled by POGA-H 0 is average Temperature relation between restoring region & POGA-C POGA-C fixed radiative forcing to 1990 level La Niña event lowered global temperature by about 0.15°C

Pacific Decadal Oscillation  year events, (ENSO is 6-18months)  Occurs in N. Pacific and N. American waters, w/ secondary effects in tropics (ENSO is in tropics w/ secondary effects in N. Pacific and N. American waters).

 Positive phase of the PDO: enhanced the surface warming (reduced heat to deep ocean)  Negative phase of the PDO: more heat gets deposited at greater depths (ocean warming, surface cooling) Reanalyzed Ocean Heat Content as a fn. of time PDO index Negative PDO correlated with deep ocean heat uptake?

Winter sea level pressure and temperature trends Source: Yu Kosaka & Shang-Ping Xie, 2013 Temperature trend (°C per 11 yrs) Observed 0= average POGA-H Sea level pressure trend (hPa per 11 yrs.) Observed POGA-H Broad agreement over Indian, South Pacific, Atlantic Eurasia? Slowdown of Walker circulation predicted in global warming models In Fact, Walker cell intensified, which model captures

Summer precipitation and temperature trends Temperature trend (°C per 11 yrs) POGA-H 0= average Observed Agreement over Pacific Weak over Eurasia and Arctic Accurately captures rainfall decrease over S-US Source: Yu Kosaka & Shang-Ping Xie, 2013 POGA-H ObservedPrecipitation trend (mm per day per 11 yrs.)

How valuable is the model? What are the advantages/disadvantages of tying the model to SST of the equatorial Pacific? Is La Niña forced by Climate Change, or is it merely a result of internal variability (Held, 2013)? Can internal variability account for the warming hiatus?

Guemas et al.’s methods  Init experiment: all model state variables are initialized with the observations at a given year  NoInit: does not include any information about the previous observed variability, only information about the external radiative forcing.  Variables assessed for experiments: Measure Global SST anomalies (Kelvin) and Ocean Heat Uptake (Joules) in multiple geographic regions

Init model is more consistent with reanalyzed Ocean Heat Content than NoInit model Black: Reanalysis Blue: nonInit model Red: Init model 10^23 Joules

Init model is more consistent with reanalyzed SST than NoInit model 0=Global SST average

Can model predict Ocean Heat Content? ● NoInit and Init sensitivity experiments missed 2002 peak of reanalyzed data ● OHC is about 50% more in Init than NoInit during the warming peak Source: Guernas et al, Retro predictions Red: Init Model Black: Reanalysis Blue: NoInit Model

Importance of Tropic-waters in OHC Triangle: Mixed layer. Diamonds: Below mixed layer OHC 3-yr uptake Tropical Pacific OHC 3-yr uptake in Tropical Atlantic Black= Reanalysis, Red=Init, Blue= NoInit OHC 3-yr uptake in N. Atlantic The tropical Pacific, the tropical Atlantic and the North Atlantic absorption below the mixed layer explain 42%, 25% and 16% of the upper ocean heat uptake at the time of its maximum, respectively.

How accurate and useful is this model? “The reasons for the warming pause to be sustained late in the decade have not however been clearly identified from our experiments” “Here, we have shown...the external radiative forcing is negligible” Important analysis of the ocean temperature, but useful model? To predict….. retrospectively? Global OHC Energy Black: Observations Red: Init model Blue: NoInit model

Some models appear to be too sensitive to anthropogenic greenhouse gases. IPCC suggests that model response to anthropogenic GHG should be scaled by 0.9 Brown= scale by which the contributions from anthropogenic forcings are multiplied by to equal HadCrut4 observations Blue=...natural contribution... Source: IPCC WG1AY Are models too sensitive to GHG?

Stratospheric Water vapor decrease Caused decrease in reflection of longwave radiation back to earth. Source: IPCC WG1 5th assessment, 2013 Not reflected in models IPCC states that water vapor increased again in Y-axis: Water vapor anomaly (ppm) X-axis: Time ( )

Effective Radiative Forcing (ERF) Effective Radiative Forcing (ERF) Decrease Red=observed Gray=simulated Source: IPCC WG1 5th assessment, 2013 Observed Radiative Forcing ( ): 0.32 Wm-2 Observed Radiative Forcing ( ): 0.22 Wm-2

Decrease in Natural Forcing Natural forcing decreased from 0.01 W m-2 ( ) to W m-2 ( ). Volcano eruptions after 2000 produced cooling aerosols. However, this buildup in aerosols is refuted by satellite imagery. Credit: Martin Rietze

Decrease in Natural Forcing Solar forcing went from a relative maximum in 2000 to a relative minimum in 2009 Source: IPCC WG1 5th assessment, 2013

Simulated ERF is less than observed ERF Models ignored decreasing solar forcing and volcanism. However, models (CMIP5) still show lower ERF than observations (hadCRUT4). Red=observed Gray=simulated Source: IPCC WG1 5th assessment, 2013

Is the IPCC too confident? The IPCC states (WG1AR5 2013) that, despite the fact that they can’t satisfactorily explain the hiatus, that, “most 15-year GMST trends in the near-term future will be larger than during 1998–2012 (high confidence)