Jason N. S. Cole

Impact of a New Radiation Package, McRad, in the ECMWF Integrated Forecasting System

Abstract A new radiation package, “McRad,” has become operational with cycle 32R2 of the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). McRad includes an improved description of the land surface albedo from Moderate Resolution Imaging Spectroradiometer (MODIS) observations, the Monte Carlo independent column approximation treatment of the radiative transfer in clouds, and the Rapid Radiative Transfer Model shortwave scheme. The impact of McRad on year-long simulations at TL159L91 and higher-resolution 10-day forecasts is then documented. McRad is shown to benefit the representation of most parameters over both shorter and longer time scales, relative to the previous operational version of the radiative transfer schemes. At all resolutions, McRad improves the representation of the cloud–radiation interactions, particularly in the tropical regions, with improved temperature and wind objective scores through a reduction of some systematic errors in the ...

The EarthCARE Satellite: The Next Step Forward in Global Measurements of Clouds, Aerosols, Precipitation, and Radiation

AbstractThe collective representation within global models of aerosol, cloud, precipitation, and their radiative properties remains unsatisfactory. They constitute the largest source of uncertainty in predictions of climatic change and hamper the ability of numerical weather prediction models to forecast high-impact weather events. The joint European Space Agency (ESA)–Japan Aerospace Exploration Agency (JAXA) Earth Clouds, Aerosol and Radiation Explorer (EarthCARE) satellite mission, scheduled for launch in 2018, will help to resolve these weaknesses by providing global profiles of cloud, aerosol, precipitation, and associated radiative properties inferred from a combination of measurements made by its collocated active and passive sensors. EarthCARE will improve our understanding of cloud and aerosol processes by extending the invaluable dataset acquired by the A-Train satellites CloudSat, Cloud–Aerosol Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), and Aqua. Specifically, EarthCARE’s c...

Origins of the solar radiation biases over the Southern Ocean in CFMIP2 models

AbstractCurrent climate models generally reflect too little solar radiation over the Southern Ocean, which may be the leading cause of the prevalent sea surface temperature biases in climate models. The authors study the role of clouds on the radiation biases in atmosphere-only simulations of the Cloud Feedback Model Intercomparison Project phase 2 (CFMIP2), as clouds have a leading role in controlling the solar radiation absorbed at those latitudes. The authors composite daily data around cyclone centers in the latitude band between 40° and 70°S during the summer. They use cloud property estimates from satellite to classify clouds into different regimes, which allow them to relate the cloud regimes and their associated radiative biases to the meteorological conditions in which they occur. The cloud regimes are defined using cloud properties retrieved using passive sensors and may suffer from the errors associated with this type of retrievals. The authors use information from the Cloud–Aerosol Lidar and I...

The Continual Intercomparison of Radiation Codes: Results from Phase I

[1] We present results from Phase I of the Continual Intercomparison of Radiation Codes (CIRC), intended as an evolving and regularly updated reference source for evaluation of radiative transfer (RT) codes used in global climate models and other atmospheric applications. CIRC differs from previous intercomparisons in that it relies on an observationally validated catalog of cases. The seven CIRC Phase I baseline cases, five cloud free and two with overcast liquid clouds, are built around observations by the Atmospheric Radiation Measurements program that satisfy the goals of Phase I, namely, to examine RT model performance in realistic, yet not overly complex, atmospheric conditions. Besides the seven baseline cases, additional idealized “subcases” are also employed to facilitate interpretation of model errors. In addition to quantifying individual model performance with respect to reference line-by-line calculations, we also highlight RT code behavior for conditions of doubled CO2, issues arising from spectral specification of surface albedo, and the impact of cloud scattering in the thermal infrared. Our analysis suggests that improvements in the calculation of diffuse shortwave flux, shortwave absorption, and shortwave CO2 forcing as well as in the treatment of spectral surface albedo should be considered for many RT codes. On the other hand, longwave calculations are generally in agreement with the reference results. By expanding the range of conditions under which participating codes are tested, future CIRC phases will hopefully allow even more rigorous examination of RT codes.

A multi-model assessment of regional climate disparities caused by solar geoengineering

Global-scale solar geoengineering is the deliberate modification of the climate system to offset some amount of anthropogenic climate change by reducing the amount of incident solar radiation at the surface. These changes to the planetary energy budget result in differential regional climate effects. For the first time, we quantitatively evaluate the potential for regional disparities in a multi-model context using results from a model experiment that offsets the forcing from a quadrupling of CO2 via reduction in solar irradiance. We evaluate temperature and precipitation changes in 22 geographic regions spanning most of Earthʼs continental area. Moderate amounts of solar reduction (up to 85% of the amount that returns global mean temperatures to preindustrial levels) result in regional temperature values that are closer to preindustrial levels than an un-geoengineered, high CO2 world for all regions and all models. However, in all but one model, there is at least one region for which no amount of solar reduction can restore precipitation toward its preindustrial value. For most metrics considering simultaneous changes in both variables,

A multimodel examination of climate extremes in an idealized geoengineering experiment

Temperature and precipitation extremes are examined in the Geoengineering Model Intercomparison Project experiment G1, wherein an instantaneous quadrupling of CO2 from its preindustrial control value is offset by a commensurate reduction in solar irradiance. Compared to the preindustrial climate, changes in climate extremes under G1 are generally much smaller than under 4 × CO2 alone. However, it is also the case that extremes of temperature and precipitation in G1 differ significantly from those under preindustrial conditions. Probability density functions of standardized anomalies of monthly surface temperature T and precipitation P in G1 exhibit an extension of the high-T tail over land, of the low-T tail over ocean, and a shift of P to drier conditions. Using daily model output, we analyzed the frequency of extreme events, such as the coldest night (TNn), warmest day (TXx), and maximum 5 day precipitation amount, and also duration indicators such as cold and warm spells and consecutive dry days. The strong heating at northern high latitudes simulated under 4 × CO2 is much alleviated in G1, but significant warming remains, particularly for TNn compared to TXx. Internal feedbacks lead to regional increases in absorbed solar radiation at the surface, increasing temperatures over Northern Hemisphere land in summer. Conversely, significant cooling occurs over the tropical oceans, increasing cold spell duration there. Globally, G1 is more effective in reducing changes in temperature extremes compared to precipitation extremes and for reducing changes in precipitation extremes versus means but somewhat less effective at reducing changes in temperature extremes compared to means.

The impact of parametrized convection on cloud feedback.

We investigate the sensitivity of cloud feedbacks to the use of convective parametrizations by repeating the CMIP5/CFMIP-2 AMIP/AMIP + 4K uniform sea surface temperature perturbation experiments with 10 climate models which have had their convective parametrizations turned off. Previous studies have suggested that differences between parametrized convection schemes are a leading source of inter-model spread in cloud feedbacks. We find however that ‘ConvOff’ models with convection switched off have a similar overall range of cloud feedbacks compared with the standard configurations. Furthermore, applying a simple bias correction method to allow for differences in present-day global cloud radiative effects substantially reduces the differences between the cloud feedbacks with and without parametrized convection in the individual models. We conclude that, while parametrized convection influences the strength of the cloud feedbacks substantially in some models, other processes must also contribute substantially to the overall inter-model spread. The positive shortwave cloud feedbacks seen in the models in subtropical regimes associated with shallow clouds are still present in the ConvOff experiments. Inter-model spread in shortwave cloud feedback increases slightly in regimes associated with trade cumulus in the ConvOff experiments but is quite similar in the most stable subtropical regimes associated with stratocumulus clouds. Inter-model spread in longwave cloud feedbacks in strongly precipitating regions of the tropics is substantially reduced in the ConvOff experiments however, indicating a considerable local contribution from differences in the details of convective parametrizations. In both standard and ConvOff experiments, models with less mid-level cloud and less moist static energy near the top of the boundary layer tend to have more positive tropical cloud feedbacks. The role of non-convective processes in contributing to inter-model spread in cloud feedback is discussed.

The Monte Carlo independent column approximation's conditional random noise : Impact on simulated climate

Abstract The Monte Carlo Independent Column Approximation (McICA) method for computing domain-average radiative fluxes is unbiased with respect to the full ICA, but its flux estimates contain conditional random noise. Results for five experiments are used to assess the impact of McICA-related noise on simulations of global climate made by the NCAR Community Atmosphere Model (CAM). The experiment with the least noise (an order of magnitude below that of basic McICA) is taken as the reference. Two additional experiments help demonstrate how the impact of noise depends on the time interval between calls to the radiation code. Each experiment is an ensemble of seven 15-month simulations. Experiments with very high noise levels feature significant reductions to cloudiness in the lowermost model layer over tropical oceans as well as changes in highly related quantities. This bias appears immediately, stabilizes after a couple of model days, and appears to stem from nonlinear interactions between clouds and radi...

Solar radiation management impacts on agriculture in China: A case study in the Geoengineering Model Intercomparison Project (GeoMIP)

Geoengineering via solar radiation management could affect agricultural productivity due to changes in temperature, precipitation, and solar radiation. To study rice and maize production changes in China, we used results from 10 climate models participating in the Geoengineering Model Intercomparison Project (GeoMIP) G2 scenario to force the Decision Support System for Agrotechnology Transfer (DSSAT) crop model. G2 prescribes an insolation reduction to balance a 1% a−1 increase in CO2 concentration (1pctCO2) for 50 years. We first evaluated the DSSAT model using 30 years (1978–2007) of daily observed weather records and agriculture practices for 25 major agriculture provinces in China and compared the results to observations of yield. We then created three sets of climate forcing for 42 locations in China for DSSAT from each climate model experiment: (1) 1pctCO2, (2) G2, and (3) G2 with constant CO2 concentration (409 ppm) and compared the resulting agricultural responses. In the DSSAT simulations: (1) Without changing management practices, the combined effect of simulated climate changes due to geoengineering and CO2 fertilization during the last 15 years of solar reduction would change rice production in China by −3.0 ± 4.0 megaton (Mt) (2.4 ± 4.0%) as compared with 1pctCO2 and increase Chinese maize production by 18.1 ± 6.0 Mt (13.9 ± 5.9%). (2) The termination of geoengineering shows negligible impacts on rice production but a 19.6 Mt (11.9%) reduction of maize production as compared to the last 15 years of geoengineering. (3) The CO2 fertilization effect compensates for the deleterious impacts of changes in temperature, precipitation, and solar radiation due to geoengineering on rice production, increasing rice production by 8.6 Mt. The elevated CO2 concentration enhances maize production in G2, contributing 7.7 Mt (42.4%) to the total increase. Using the DSSAT crop model, virtually all of the climate models agree on the sign of the responses, even though the spread across models is large. This suggests that solar radiation management would have little impact on rice production in China but could increase maize production.

Radiative flux and forcing parameterization error in aerosol‐free clear skies

Abstract This article reports on the accuracy in aerosol‐ and cloud‐free conditions of the radiation parameterizations used in climate models. Accuracy is assessed relative to observationally validated reference models for fluxes under present‐day conditions and forcing (flux changes) from quadrupled concentrations of carbon dioxide. Agreement among reference models is typically within 1 W/m2, while parameterized calculations are roughly half as accurate in the longwave and even less accurate, and more variable, in the shortwave. Absorption of shortwave radiation is underestimated by most parameterizations in the present day and has relatively large errors in forcing. Error in present‐day conditions is essentially unrelated to error in forcing calculations. Recent revisions to parameterizations have reduced error in most cases. A dependence on atmospheric conditions, including integrated water vapor, means that global estimates of parameterization error relevant for the radiative forcing of climate change will require much more ambitious calculations.