We use a global climate model to compare the effectiveness of many climate forcing agents for producing climate change. We find a substantial range in the “efficacy” of different forcings, where the efficacy is the global temperature response per unit forcing relative to the response to CO₂ forcing. Anthropogenic CH₄ has efficacy ∼110%, which increases to ∼145% when its indirect effects on stratospheric H₂O and tropospheric O₃ are included, yielding an effective climate forcing of ∼0.8 W/m² for the period 1750–2000 and making CH₄ the largest anthropogenic climate forcing other than CO₂. Black carbon (BC) aerosols from biomass burning have a calculated efficacy ∼58%, while fossil fuel BC has an efficacy ∼78%. Accounting for forcing efficacies and for indirect effects via snow albedo and cloud changes, we find that fossil fuel soot, defined as BC + OC (organic carbon), has a net positive forcing while biomass burning BC + OC has a negative forcing. We show that replacement of the traditional instantaneous and adjusted forcings, Fi and Fa, with an easily computed alternative, Fs, yields a better predictor of climate change, i.e., its efficacies are closer to unity. Fs is inferred from flux and temperature changes in a fixed‐ocean model run. There is remarkable congruence in the spatial distribution of climate change, normalized to the same forcing Fs, for most climate forcing agents, suggesting that the global forcing has more relevance to regional climate change than may have been anticipated. Increasing greenhouse gases intensify the Hadley circulation in our model, increasing rainfall in the Intertropical Convergence Zone (ITCZ), Eastern United States, and East Asia, while intensifying dry conditions in the subtropics including the Southwest United States, the Mediterranean region, the Middle East, and an expanding Sahel. These features survive in model simulations that use all estimated forcings for the period 1880–2000. Responses to localized forcings, such as land use change and heavy regional concentrations of BC aerosols, include more specific regional characteristics. We suggest that anthropogenic tropospheric O₃ and the BC snow albedo effect contribute substantially to rapid warming and sea ice loss in the Arctic. As a complement to a priori forcings, such as Fi, Fa, and Fs, we tabulate the a posteriori effective forcing, Fe, which is the product of the forcing and its efficacy. Fe requires calculation of the climate response and introduces greater model dependence, but once it is calculated for a given amount of a forcing agent it provides a good prediction of the response to other forcing amounts.

A full description of the ModelE version of the Goddard Institute for Space Studies (GISS) atmospheric general circulation model (GCM) and results are presented for present-day climate simulations (ca. 1979). This version is a complete rewrite of previous models incorporating numerous improvements in basic physics, the stratospheric circulation, and forcing fields. Notable changes include the following: the model top is now above the stratopause, the number of vertical layers has increased, a new cloud microphysical scheme is used, vegetation biophysics now incorporates a sensitivity to humidity, atmospheric turbulence is calculated over the whole column, and new land snow and lake schemes are introduced. The performance of the model using three configurations with different horizontal and vertical resolutions is compared to quality-controlled in situ data, remotely sensed and reanalysis products. Overall, significant improvements over previous models are seen, particularly in upper-atmosphere temperatures and winds, cloud heights, precipitation, and sea level pressure. Data–model comparisons continue, however, to highlight persistent problems in the marine stratocumulus regions.

We present a description of the ModelE2 version of the Goddard Institute for Space Studies (GISS) General Circulation Model (GCM) and the configurations used in the simulations performed for the Coupled Model Intercomparison Project Phase 5 (CMIP5). We use six variations related to the treatment of the atmospheric composition, the calculation of aerosol indirect effects, and ocean model component. Specifically, we test the difference between atmospheric models that have noninteractive composition, where radiatively important aerosols and ozone are prescribed from precomputed decadal averages, and interactive versions where atmospheric chemistry and aerosols are calculated given decadally varying emissions. The impact of the first aerosol indirect effect on clouds is either specified using a simple tuning, or parameterized using a cloud microphysics scheme. We also use two dynamic ocean components: the Russell and HYbrid Coordinate Ocean Model (HYCOM) which differ significantly in their basic formulations and grid. Results are presented for the climatological means over the satellite era (1980–2004) taken from transient simulations starting from the preindustrial (1850) driven by estimates of appropriate forcings over the 20th Century. Differences in base climate and variability related to the choice of ocean model are large, indicating an important structural uncertainty. The impact of interactive atmospheric composition on the climatology is relatively small except in regions such as the lower stratosphere, where ozone plays an important role, and the tropics, where aerosol changes affect the hydrological cycle and cloud cover. While key improvements over previous versions of the model are evident, these are not uniform across all metrics.

This paper describes the GISS‐E2.1 contribution to the Coupled Model Intercomparison Project, Phase 6 (CMIP6). This model version differs from the predecessor model (GISS‐E2) chiefly due to parameterization improvements to the atmospheric and ocean model components, while keeping atmospheric resolution the same. Model skill when compared to modern era climatologies is significantly higher than in previous versions. Additionally, updates in forcings have a material impact on the results. In particular, there have been specific improvements in representations of modes of variability (such as the Madden‐Julian Oscillation and other modes in the Pacific) and significant improvements in the simulation of the climate of the Southern Oceans, including sea ice. The effective climate sensitivity to 2 × CO₂ is slightly higher than previously at 2.7–3.1°C (depending on version) and is a result of lower CO₂ radiative forcing and stronger positive feedbacks.

Why do plants reflect in the green and have a "red edge" in the red, and should extrasolar photosynthesis be the same? We provide (1) a brief review of how photosynthesis works, (2) an overview of the diversity of photosynthetic organisms, their light harvesting systems, and environmental ranges, (3) a synthesis of photosynthetic surface spectral signatures, and (4) evolutionary rationales for photosynthetic surface reflectance spectra with regard to utilization of photon energy and the planetary light environment. We found the "near-infrared (NIR) end" of the red edge to trend from blue-shifted to reddest for (in order) snow algae, temperate algae, lichens, mosses, aquatic plants, and finally terrestrial vascular plants. The red edge is weak or sloping in lichens. Purple bacteria exhibit possibly a sloping edge in the NIR. More studies are needed on pigment-protein complexes, membrane composition, and measurements of bacteria before firm conclusions can be drawn about the role of the NIR reflectance. Pigment absorbance features are strongly correlated with features of atmospheric spectral transmittance: P680 in Photosystem II with the peak surface incident photon flux density at approximately 685 nm, just before an oxygen band at 687.5 nm; the NIR end of the red edge with water absorbance bands and the oxygen A-band at 761 nm; and bacteriochlorophyll reaction center wavelengths with local maxima in atmospheric and water transmittance spectra. Given the surface incident photon flux density spectrum and resonance transfer in light harvesting, we propose some rules with regard to where photosynthetic pigments will peak in absorbance: (1) the wavelength of peak incident photon flux; (2) the longest available wavelength for core antenna or reaction center pigments; and (3) the shortest wavelengths within an atmospheric window for accessory pigments. That plants absorb less green light may not be an inefficient legacy of evolutionary history, but may actually satisfy the above criteria.