Premium
Climate model simulations of the equilibrium climatic response to increased carbon dioxide
Author(s) -
Schlesinger Michael E.,
Mitchell John F. B.
Publication year - 1987
Publication title -
reviews of geophysics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 8.087
H-Index - 156
eISSN - 1944-9208
pISSN - 8755-1209
DOI - 10.1029/rg025i004p00760
Subject(s) - cloud feedback , atmospheric sciences , environmental science , climate model , radiative forcing , climatology , energy balance , cloud forcing , albedo (alchemy) , radiative transfer , convection , climate change , water vapor , climate sensitivity , physics , meteorology , thermodynamics , geology , oceanography , art , quantum mechanics , performance art , art history
The first assessments of the potential climatic effects of increased CO 2 were performed using simplified climate models, namely, energy balance models (EBMs) and radiative‐convective models (RCMs). A wide range of surface temperature warming has been obtained by surface EBMs as a result of the inherent difficulty of these models in specifying the behavior of the climate system away from the energy balance level. RCMs have given estimates of Δ T s for a CO 2 doubling that range from 0.48° to 4.2°C. This response can be characterized by Δ T s = Δ R T G 0 /(1 ‐ f ), where Δ R T is the radiative forcing at the tropopause due to the CO 2 doubling (∼4 W m −2 ), G 0 is the gain of the climate system without feedbacks (∼0.3°C/(W m −2 )), and f is the feedback. The feedback processes in RCMs include water vapor feedback ( f is 0.3 to 0.4), moist adiabatic lapse rate feedback ( f is −0.25 to −0.4), cloud altitude feedback ( f is 0.15 to 0.30), cloud cover feedback ( f is unknown), cloud optical depth feedback ( f is 0 to −1.32), and surface albedo feedback ( f is 0.14 to 0.19). However, these feedbacks can be predicted credibly only by physically based models that include the essential dynamics and thermodynamics of the feedback processes. Such physically based models are the general circulation models (GCMs). The earliest GCM simulations of CO 2 ‐induced climate change were performed without the annual insolation cycle. These “annual mean” simulations gave for a CO 2 doubling a global mean surface air temperature warming of 1.3° to 3.9°C, an increase in the global mean precipitation rate of 2.7 to 7.8%, and an indication of a soil moisture drying in the middle latitudes. The first GCM simulation of the seasonal variation of CO 2 ‐induced climate change was performed for a CO 2 quadrupling and obtained annual global mean surface temperature and precipitation changes of 4.1°C and 6.7%, respectively. Substantial seasonal differences in the CO 2 ‐induced climate changes were found, especially in polar latitudes where the warming was maximum in winter and in the middle latitudes of the northern hemisphere where a soil moisture desiccation was found in summer. Recently, three CO 2 ‐doubling experiments have been performed with GCMs that include the annual insolation cycle. These seasonal simulations give an annual global mean warming of 3.5° to 4.2°C and precipitation increases of 7.1 to 11%. These changes are approximately twice as large as those implied for a CO 2 doubling by the earliest seasonal simulation, apparently as a result of a positive cloud feedback. The geographical distributions of the CO 2 ‐induced warming obtained by the recent simulations agree qualitatively but not quantitatively. Furthermore, the precipitation and soil moisture changes do not agree quantitatively and even show qualitative differences. In particular, the summertime soil moisture drying in middle‐latitudes is simulated by only one of the GCMs. In order to improve the state of the art in simulating the equilibrium climatic change induced by increased CO 2 concentrations, it is recommended first that the contemporary GCM simulations be analyzed to determine the feedback processes responsible for their differences and second that the parameterization of these processes in the GCMs be validated against highly detailed models and observations.