Dependence of global temperatures on atmospheric CO2 and solar irradiance

Changes in global average temperatures and of the seasonal cycle are strongly coupled to the concentration of atmospheric CO2. I estimate transfer functions from changes in atmospheric CO2 and from changes in solar irradiance to hemispheric temperatures that have been corrected for the effects of precession. They show that changes from CO2 over the last century are about three times larger than those from changes in solar irradiance. The increase in global average temperature during the last century is at least 20 times the SD of the residual temperature series left when the effects of CO2 and changes in solar irradiance are subtracted.

Possible forcing of global temperature by the oceanic tides

An approximately decadal periodicity in surface air temperature is discernable in global observations from A.D. 1855 to 1900 and since A.D. 1945, but with a periodicity of only about 6 years during the intervening period. Changes in solar irradiance related to the sunspot cycle have been proposed to account for the former, but cannot account for the latter. To explain both by a single mechanism, we propose that extreme oceanic tides may produce changes in sea surface temperature at repeat periods, which alternate between approximately one-third and one-half of the lunar nodal cycle of 18.6 years. These alternations, recurring at nearly 90-year intervals, reflect varying slight degrees of misalignment and departures from the closest approach of the Earth with the Moon and Sun at times of extreme tide raising forces. Strong forcing, consistent with observed temperature periodicities, occurred at 9-year intervals close to perihelion (solar perigee) for several decades centered on A.D. 1881 and 1974, but at 6-year intervals for several decades centered on A.D. 1923. As a physical explanation for tidal forcing of temperature we propose that the dissipation of extreme tides increases vertical mixing of sea water, thereby causing episodic cooling near the sea surface. If this mechanism correctly explains near-decadal temperature periodicities, it may also apply to variability in temperature and climate on other times-scales, even millennial and longer.

Climate forcings in the Industrial era

The forcings that drive long-term climate change are not known with an accuracy sufficient to define future climate change. Anthropogenic greenhouse gases (GHGs), which are well measured, cause a strong positive (warming) forcing. But other, poorly measured, anthropogenic forcings, especially changes of atmospheric aerosols, clouds, and land-use patterns, cause a negative forcing that tends to offset greenhouse warming. One consequence of this partial balance is that the natural forcing due to solar irradiance changes may play a larger role in long-term climate change than inferred from comparison with GHGs alone. Current trends in GHG climate forcings are smaller than in popular “business as usual” or 1% per year CO2 growth scenarios. The summary implication is a paradigm change for long-term climate projections: uncertainties in climate forcings have supplanted global climate sensitivity as the predominant issue.

Case study of the effects of atmospheric aerosols and regional haze on agriculture: An opportunity to enhance crop yields in China through emission controls?

The effect of atmospheric aerosols and regional haze from air pollution on the yields of rice and winter wheat grown in China is assessed. The assessment is based on estimates of aerosol optical depths over China, the effect of these optical depths on the solar irradiance reaching the earth’s surface, and the response of rice and winter wheat grown in Nanjing to the change in solar irradiance. Two sets of aerosol optical depths are presented: one based on a coupled, regional climate/air quality model simulation and the other inferred from solar radiation measurements made over a 12-year period at meteorological stations in China. The model-estimated optical depths are significantly smaller than those derived from observations, perhaps because of errors in one or both sets of optical depths or because the data from the meteorological stations has been affected by local pollution. Radiative transfer calculations using the smaller, model-estimated aerosol optical depths indicate that the so-called “direct effect” of regional haze results in an ≈5–30% reduction in the solar irradiance reaching some of China’s most productive agricultural regions. Crop-response model simulations suggest an ≈1:1 relationship between a percentage increase (decrease) in total surface solar irradiance and a percentage increase (decrease) in the yields of rice and wheat. Collectively, these calculations suggest that regional haze in China is currently depressing optimal yields of ≈70% of the crops grown in China by at least 5–30%. Reducing the severity of regional haze in China through air pollution control could potentially result in a significant increase in crop yields and help the nation meet its growing food demands in the coming decades.

The observed global warming record: What does it tell us?

Global, near-surface temperature data sets and their derivations are discussed, and differences between the Jones and Intergovernmental Panel on Climate Change data sets are explained. Global-mean temperature changes are then interpreted in terms of anthropogenic forcing influences and natural variability. The inclusion of aerosol forcing improves the fit between modeled and observed changes but does not improve the agreement between the implied climate sensitivity value and the standard model-based range of 1.5–4.5°C equilibrium warming for a CO2 doubling. The implied sensitivity goes from below the model-based range of estimates to substantially above this range. The addition of a solar forcing effect further improves the fit and brings the best-fit sensitivity into the middle of the model-based range. Consistency is further improved when internally generated changes are considered. This consistency, however, hides many uncertainties that surround observed data/model comparisons. These uncertainties make it impossible currently to use observed global-scale temperature changes to narrow the uncertainty range in the climate sensitivity below that estimated directly from climate models.