It is my general impression that most readers of my posts do not yet understand how a dynamical reinterpretation of current Earth System Model projections could highlight the potential consequences of current ESM assumptions/limitations that could transform what are commonly perceived as fat-tail risks into stark reality later this century, with continuing global warming. Hansen's ice-climate feedback (triggered by fresh water hosing events) can interact synergistically with mechanisms such as: (a) the bipolar seesaw; (b) Arctic Amplification; (c) positive cloud feedback in the tropics; (d) methane releases from Arctic continental shelves/slopes including the ESAS; and (e) methane releases from Arctic permafrost thermokarst lakes; that are not included in the CMIP5 models nor in Hansen's model.
Furthermore, in the first attached image shows how high ECS is based on a dynamical interpretation of the paleo record as compared to what is assumed by CMIP5. The second attached image from PK17 [Proistosescu & Huybers (2017)] shows that when ECS values based on observed data are corrected for ocean heat content the likely values fall into the upper end of the AR5 range. Additionally, the third image shows how climate attractors can ratchet up Earth System states in a stepwise fashion.
As an example of this dynamical response, I note that in the first linked research: "Severe testing is applied to observed global and regional surface and satellite temperatures and modelled surface temperatures to determine whether these interactions are independent, as in the traditional signal-to-noise model, or whether they interact, resulting in steplike warming." The reference concludes that indeed steplike warming occurs due to "… a store-and-release mechanism from the ocean to the atmosphere…" like the classical Lorenzian attractor case of ENSO decadal cycles. Such steplike behavior raises the issue of what I call "Ratcheting Quasi-static Equilibrium States" that can accelerate non-linear Earth Systems response beyond the linear Earth Systems response assumed by AR5/CMIP5 researchers. As the authors point-out such AR5/CMIP5 researcher likely missed this behavior because: "This may be due in part to science asking the wrong questions."; and they advise that such AR5/CMIP5 researchers should change how they view the output from their models. For example, the reference shows global warming increasing much faster for a steplike response if ECS is 4.5 than for a the traditional AR5/CMIP5 interpretation; which means that ESLD researchers are exposing society to far more risk of the consequences of high ECS values than AR5/CMIP5 are leading us to believe:
Jones, R. N. and Ricketts, J. H.: Reconciling the signal and noise of atmospheric warming on decadal timescales, Earth Syst. Dynam. Discuss., doi:10.5194/esd-2016-35, in review, 2016.
http://www.earth-syst-dynam-discuss.net/esd-2016-35/&
http://www.earth-syst-dynam-discuss.net/esd-2016-35/esd-2016-35.pdfThe second linked reference indicates that the frequency of extreme El Nino events will increase rapidly with relatively minor increases in GMSTA; while the frequency of extreme La Nina events will increase relatively little between 1.5 and 2C GMSTA. This indicates that climate sensitivity is higher than assumed in AR5:
Guojian Wang et al (2017), "Continued increase of extreme El Niño frequency long after 1.5 °C warming stabilization", Nature Climate Change 7, 568–572, doi:10.1038/nclimate3351
http://www.nature.com/nclimate/journal/v7/n8/full/nclimate3351.html?foxtrotcallback=trueThe third linked reference indicates that the time required to recharge the Western Pacific warm water pool has decreased from 1.5–3.5 years, in the 1979–99 period, to 0.8–1.3 years, in the 2000–16 period. This is a clear sign that climate sensitivity is likely accelerating from the recent past, due to increased El Nino events:
Zeng-Zhen Hu et al (2017), "On the Shortening of the Lead Time of Ocean Warm Water Volume to ENSO SST Since 2000", Scientific Reports 7, Article number: 4294, doi:10.1038/s41598-017-04566-z
http://www.nature.com/articles/s41598-017-04566-zNext, I note that it is well known that the primary source of CO₂ fluctuations over the ENSO cycle is due to changes in land vegetation in the tropics (from 30N to 30S), rather than due to emissions from the ocean. In this regards, the fourth reference shows that there has been a two-fold increase of carbon cycle sensitivity to tropical temperature variations over the past several decades.
Wang, X., Piao, S., Ciais, P., Friedlingstein, P., Myneni, R.B., Cox, P., Heimann, M., Miller, J., Peng, S.P., Wang, T., Yang, H. and Chen, A., (2014), "A two-fold increase of carbon cycle sensitivity to tropical temperature variations", Nature, 2014; DOI: 10.1038/nature12915.
http://www.nature.com/nature/journal/v506/n7487/full/nature12915.html#extended-datahttp://sites.bu.edu/cliveg/files/2014/01/wang-nature-2014.pdfCaption for the fourth attached image: "Figure 1 | Change in detrended anomalies in CGR and tropical MAT, in dCGR/dMAT and in ªintCGR over the past five decades. a, Change in detrended CGR anomalies at Mauna Loa Observatory (black) and in detrended tropical MAT anomalies (red) derived from the CRU data set16. Tropical MAT is calculated as the spatial average over vegetated tropical lands (23uN to 23u S). The highest correlations between detrended CGR and detrended tropicalMAT are obtained when no time lags are applied (R50.53, P,0.01). b, Change in dCGR/dMAT during the past five decades. c, Change in cintCGR during the past five decades. In b and c, different colours showdCGR/dMATor cint CGR estimated with moving time windows of different lengths (20 yr and 25 yr). Years on the horizontal axis indicate the central year of the moving time window used to derive dCGR/dMAT or cintCGR (for example, 1970 represents period 1960–1979 in the 20-yr time window). The shaded areas show the confidence interval of dCGR/dMATand cintCGR, as appropriate, derived using 20-yr or 25-yr moving windows in 500 bootstrap estimates."
The fifth reference indicates global warming is increasing the frequency of extreme El Ninos. As strong El Ninos increase both the temperature and induce droughts in the tropics it is clear that CO₂ emissions increase from the tropical land vegetation during strong El Ninos:
Wenju Cai, Agus Santoso, Guojian Wang, Sang-Wook Yeh, Soon-Il An, Kim M. Cobb, Mat Collins, Eric Guilyardi, Fei-Fei Jin, Jong-Seong Kug, Matthieu Lengaigne, Michael J. McPhaden, Ken Takahashi, Axel Timmermann, Gabriel Vecchi, Masahiro Watanabe & Lixin Wu (2015), "ENSO and greenhouse warming", Nature Climate Change, Volume: 5, Pages: 849–859, doi:10.1038/nclimate2743
http://www.nature.com/nclimate/journal/v5/n9/full/nclimate2743.htmlThe sixth linked reference uses CMIP5 projections to estimate that increases in atmospheric CO₂ concentrations accelerate during El Nino events due to reductions in terrestrial productivity:
Jin-Soo Kim, Jong-Seong Kug, Jin-Ho Yoon and Su-Jong Jeong (2016), "Increased atmospheric CO2 growth rate during El Niño driven by reduced terrestrial productivity in the CMIP5 ESMs", Journal of Climate, doi:
http://dx.doi.org/10.1175/JCLI-D-14-00672.1 http://journals.ametsoc.org/doi/abs/10.1175/JCLI-D-14-00672.1The seventh linked reference indicates that AR5 meaningfully underestimates future global warming from land use and land cover change (LULCC). This is an example of a mechanisms that may result in more rapid warming in the coming decades than projected by CMIP5:
Natalie M Mahowald, Daniel Ward, Scott Doney, Peter Hess and James T Randerson (2017), "Are the impacts of land use on warming underestimated in climate policy?", Environmental Research Letters,
https://doi.org/10.1088/1748-9326/aa836dhttp://iopscience.iop.org/article/10.1088/1748-9326/aa836d&
http://iopscience.iop.org/article/10.1088/1748-9326/aa836d/pdfIn the eighth linked reference, the authors found that sea surface temperatures from ENSO alone could not adequately explain the size and severity of the 2015-2016 drought in the Amazon. The paper reports that the 2015-2016 drought clearly exceeded that of the 100-year events in 2005 and 2010. The tropical Pacific SST was unable to explain the severity of the 2015-2016 drought for a several reasons including: (a) land-use changes; and (b) warming from greenhouse gases. Simply put, man-made warming is accelerating the movement of water through the Amazon ecosystem, which can cause drought even if precipitation does not decrease. Warming also causes changes in the large-scale patterns of air motion (atmospheric circulation) that reduces rainfall in this region.
Amir Erfanian, Guiling Wang, and Lori Fomenko (2017), "Unprecedented drought over tropical South America in 2016: significantly under-predicted by tropical SST", Sci Rep.; 7: 5811, doi: 10.1038/s41598-017-05373-2
https://www.ncbi.nlm.nih.gov/pmc/articles/PMC5517600/Lastly, in the ninth linked reference, Shrivastava et al (2017) states: "Several SOA processes highlighted in this review are complex and interdependent, and have non-linear effects on the properties, formation and evolution of SOA. Current global models neglect this complexity and non-linearity, and thus are less likely to accurately predict the climate forcing of SOA, and project future climate sensitivity to greenhouse gases." Thus, climate change induced increases in extreme El Ninos, combined with land-use changes in the tropics can result in deforestation that decreases local cloud formation from VOC emissions.
Shrivastava M, Kappa CD, Fan J, et al. (2017), "Recent Advances in Understanding Secondary Organic Aerosol: Implications for global climate forcing", Reviews of Geophysics, DOI: 10.1002/2016RG000540