Dangerous human-made interference with climate: A GISS modelE study
J. Hansen, M. Sato, R. Ruedy, P. Kharecha, A. Lacis, R. Miller, L. Nazarenko, K. Lo, G. A. Schmidt, G. Russell, I. Aleinov, S. Bauer, E. Baum, B. Cairns, V. Canuto, M. Chandler, Y. Cheng, A. Cohen, A. Del Genio, G. Faluvegi, E. Fleming, A. Friend, T. Hall, C. Jackman, J. Jonas, M. Kelley, N. Y. Kiang, D. Koch, G. Labow, J. Lerner, S. Menon, T. Novakov, V. Oinas, Ja. Perlwitz, Ju. Perlwitz, D. Rind, A. Romanou, R. Schmunk, D. Shindell, P. Stone, S. Sun, D. Streets, N. Tausnev, D. Thresher, N. Unger, M. Yao, S. Zhang
Abstract
We investigate the issue of "dangerous human-made interference with climate" using simulations with GISS modelE driven by measured or estimated forcings for 1880-2003 and extended to 2100 for IPCC greenhouse gas scenarios as well as the 'alternative' scenario of Hansen and Sato. Identification of 'dangerous' effects is partly subjective, but we find evidence that added global warming of more than 1 degree C above the level in 2000 has effects that may be highly disruptive. The alternative scenario, with peak added forcing ~1.5 W/m2 in 2100, keeps further global warming under 1 degree C if climate sensitivity is \~3 degrees C or less for doubled CO2. We discuss three specific sub-global topics: Arctic climate change, tropical storm intensification, and ice sheet stability. Growth of non-CO2 forcings has slowed in recent years, but CO2 emissions are now surging well above the alternative scenario. Prompt actions to slow CO2 emissions and decrease non-CO2 forcings are needed to achieve the low forcing of the alternative scenario.
Create a lesson
Related papers
Bridging short- and medium-range weather forecasting with machine learning
Timothy A. Smith, Mariah Pope, Sergey Frolov et al.
Detectability of Forced ENSO Changes under Global Warming: Insights from the Recharge Oscillator
Sooman Han, Jérôme Vialard, Alexey V. Fedorov et al.
When Does Forecast-Error Energy Grow Logistically in Geophysical Turbulence?
Malaquias Peña
A place for stabilization alongside tipping cascades: the AMOC-cryosphere system
Sacha Sinet
Missing the Butterfly and Predicting the Past: Features or Bugs of Accurate AI Weather Models?
Pedram Hassanzadeh, Weidong Li, Y. Qiang Sun et al.
AICON: An operational global machine learning weather forecasting model
Tobias Goecke, Marek Jacob, Florian Prill et al.