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Changes in concentrations of greenhouse gases lead to changes in radiative fluxes throughout the atmosphere. The value of this change, the instantaneous radiative forcing, varies across climate models, due partly to differences in the distribution of clouds, humidity, and temperature across models and partly due to errors introduced by approximate treatments of radiative transfer. This paper describes an experiment within the Radiative Forcing Model Intercomparision Project that uses benchmark calculations made with line‐by‐line models to identify parameterization error in the representation of absorption and emission by greenhouse gases. Clear‐sky instantaneous forcing by greenhouse gases is computed using a set of 100 profiles, selected from a reanalysis of present‐day conditions, that represent the global annual mean forcing from preindustrial times to the present day with sampling errors of less than 0.01 W m−2. Six contributing line‐by‐line models agree in their estimate of this forcing to within 0.025 W m−2 while even recently developed parameterizations have typical errors 4 or more times larger, suggesting both that the samples reveal true differences among line‐by‐line models and that parameterization error will be readily identifiable. Agreement among line‐by‐line models is better in the longwave than in the shortwave where differing treatments of the water vapor continuum affect estimates of forcing by carbon dioxide and methane. The impacts of clouds on instantaneous radiative forcing are estimated from climate model simulations, and the adjustment due to stratospheric temperature changes estimated by assuming fixed dynamical heating. Adjustments are large only for ozone and for carbon dioxide, for which stratospheric cooling introduces modest nonlinearity. Key Points: Mean clear‐sky instantaneous radiative forcing by greenhouse gases is computed with six benchmark models using 100 atmospheric profilesSampling error is several times smaller than the level of disagreement among models, which is itself smaller than parameterization errorThe impacts of clouds and stratospheric adjustment are roughly estimated; adjustments are large only for carbon dioxide and ozone [ABSTRACT FROM AUTHOR]

We have developed a dynamic land model (LM3V) able to simulate ecosystem dynamics and exchanges of water, energy, and CO2 between land and atmosphere. LM3V is specifically designed to address the consequences of land use and land management changes including cropland and pasture dynamics, shifting cultivation, logging, fire, and resulting patterns of secondary regrowth. Here we analyze the behavior of LM3V, forced with the output from the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric model AM2, observed precipitation data, and four historic scenarios of land use change for 1700-2000. Our analysis suggests a net terrestrial carbon source due to land use activities from 1.1 to 1.3 GtC/a during the 1990s, where the range is due to the difference in the historic cropland distribution. This magnitude is substantially smaller than previous estimates from other models, largely due to our estimates of a secondary vegetation sink of 0.35 to 0.6 GtC/a in the 1990s and decelerating agricultural land clearing since the 1960s. For the 1990s, our estimates for the pastures' carbon flux vary from a source of 0.37 to a sink of 0.15 GtC/a, and for the croplands our model shows a carbon source of 0.6 to 0.9 GtC/a. Our process-based mode! suggests a smaller net deforestation source than earlier bookkeeping models because it accounts for decelerated net conversion of primary forest to agriculture and for stronger secondary vegetation regrowth in tropical regions. The overall uncertainty is likely to be higher than the range reported here because of uncertainty in the biomass recovery under changing ambient conditions, including atmospheric CO2 concentration, nutrients availability, and climate. [ABSTRACT FROM AUTHOR]

An ocean model has been forced with NCEP reanalysis fluxes over 1948-2003 to evaluate the pathways and timescales associated with the uptake of anthropogenic CO2 over the North Pacific. The model reveals that there are two principal regions of uptake, the first in the region bounded by 35-45°N and 140-180°E, and the second along a band between 10-20°N and between 120°W and 180°E. For both of these regions, the dominant timescale of variability in uptake is seasonal, with maximum uptake occurring during winter and uptake being close to zero or slightly negative during summer when integrated over the basin. A decadal trend toward increased uptake of anthropogenic CO2 consists largely of modulations of the uptake maximum in winter. For detection of anthropogenic changes, this implies that in situ measurements will need to resolve the seasonal cycle in order to capture decadal trends in ΔpCO2. As uptake of anthropogenic CO2 occurs preferentially during winter, observationally based estimates which do not resolve the full seasonal cycle may result in underestimates of the rate of uptake of anthropogenic CO2. There is also a sizable circulation-driven decadal trend in the seasonal cycle of sea surface ΔpCO2 for the North Pacific, with maximum changes found near the boundary separating the subtropical and subpolar gyres in western and central regions of the basin. These changes are due to a trend in the large-scale circulation of the gyres, which itself is driven by a trend in the wind stress over the basin scale. This trend in the three-dimensional circulation is more important than the local trend in mixed layer depth (MLD) in contributing to the decadal trend in ΔpCO2. [ABSTRACT FROM AUTHOR]

MEthane Remote LIdar missioN (MERLIN) is a German‐French space mission, scheduled for launch in 2024 and built around an innovative light detecting and ranging instrument that will retrieve methane atmospheric weighted columns. MERLIN products will be assimilated into chemistry transport models to infer methane emissions and sinks. Here the expected performance of MERLIN to reduce uncertainties on methane emissions is estimated. A first complete error budget of the mission is proposed based on an analysis of the plausible causes of random and systematic errors. Systematic errors are spatially and temporally distributed on geophysical variables and then aggregated into an ensemble of 32 scenarios. Observing System Simulation Experiments are conducted, originally carrying both random and systematic errors. Although relatively small (±2.9 ppb), systematic errors are found to have a larger influence on MERLIN performances than random errors. The expected global mean uncertainty reduction on methane emissions compared to the prior knowledge is found to be 32%, limited by the impact of systematic errors. The uncertainty reduction over land reaches 60% when the largest desert regions are removed. At the latitudinal scale, the largest uncertainty reductions are achieved for temperate regions (84%) and then tropics (56%) and high latitudes (53%). Similar Observing System Simulation Experiments based on error scenarios for Greenhouse Gases Observing SATellite reveal that MERLIN should perform better than Greenhouse Gases Observing SATellite for most continental regions. The integration of error scenarios for MERLIN in another inversion system suggests similar results, albeit more optimistic in terms of uncertainty reduction. Plain Language Summary: Atmospheric methane is the second most important anthropogenic greenhouse gas. Its evolution in the atmosphere reflects the balance between its emissions and its sinks, both being still very uncertain. Observations and models are necessary to improve this situation and reduce the uncertainties associated to the global methane cycle, which is critical considering climate change. In this context, the MEthane Remote LIdar missioN (MERLIN) German‐French space satellite mission, scheduled for launch in 2023, will retrieve methane atmospheric columns. MERLIN products will be integrated into atmospheric models to improve estimates of methane emissions and sinks. In this paper, we establish the first complete error budget of the future MERLIN instrument and use it to estimate the reduction of uncertainties on methane emissions that can be expected once the satellite is launched. The two main findings are that the uncertainties should be reduced on average by 60% over land, where most methane emissions are located, and that MERLIN should perform better than the main methane sounder currently on orbit for most continental regions. Key Points: MERLIN is a German‐French space mission, scheduled for launch in 2023 that will retrieve methane atmospheric weighted columnsThe expected performances of MERLIN products to improve the estimation of methane emissions is assessedMERLIN should improve estimates of continental‐scale methane emissions by 60%, performing better than GOSAT for most regions [ABSTRACT FROM AUTHOR]