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Implementing accurate calculations of radiative forcing can improve climate projections Radiative forcing is a fundamental quantity for understanding both anthropogenic and natural changes in climate. It measures the extent to which human activities [such as the emission of carbon dioxide (CO 2 ), see the image] and natural events (such as volcanic eruptions) perturb the flow of energy into and out of the climate system. This perturbation initiates all other changes of the climate in response to external forcings. Inconsistencies in the calculation of radiative forcing by CO 2 introduce uncertainties in model projections of climate change, a problem that has persisted for more than two decades. The explicit calculation of radiative forcing and a careful vetting of radiative transfer parameterizations provide a straightforward means to substantially reduce these uncertainties and improve the projections.

A comprehensive understanding of human-induced changes to rainfall is essential for water resource management and infrastructure design. However, at regional scales, existing detection and attribution studies are rarely able to conclusively identify human influence on precipitation. Here we show that anthropogenic aerosol and greenhouse gas (GHG) emissions are the primary drivers of precipitation change over the United States. GHG emissions increase mean and extreme precipitation from rain gauge measurements across all seasons, while the decadal-scale effect of global aerosol emissions decreases precipitation. Local aerosol emissions further offset GHG increases in the winter and spring but enhance rainfall during the summer and fall. Our results show that the conflicting literature on historical precipitation trends can be explained by offsetting aerosol and greenhouse gas signals. At the scale of the United States, individual climate models reproduce observed changes but cannot confidently determine whether a given anthropogenic agent has increased or decreased rainfall. The authors use rain gauge measurements to derive data-driven estimates of how climate change impacts extreme rain in the US. They find that the expected rainfall increases driven by burning fossil fuels are offset with drying caused by anthropogenic aerosols.

The four idealized configurations of the U.S. CLIVAR Hurricane Working Group are integrated using the global Community Atmospheric Model version 5.1 at two different horizontal resolutions, approximately 100 and 25km. The publicly released 0.9° × 1.3° configuration is a poor predictor of the sign of the 0.23° × 0.31° model configuration’s change in the total number of tropical storms in a warmer climate. However, it does predict the sign of the higher-resolution configuration’s change in the number of intense tropical cyclones in a warmer climate. In the 0.23° × 0.31° model configuration, both increased CO₂ concentrations and elevated sea surface temperature (SST) independently lower the number of weak tropical storms and shorten their average duration. Conversely, increased SST causes more intense tropical cyclones and lengthens their average duration, resulting in a greater number of intense tropical cyclone days globally. Increased SST also increased maximum tropical storm instantaneous precipitation rates across all storm intensities. It was found that while a measure of maximum potential intensity based on climatological mean quantities adequately predicts the 0.23° × 0.31° model’s forced response in its most intense simulated tropical cyclones, a related measure of cyclogenesis potential fails to predict the model’s actual cyclogenesis response to warmer SSTs. These analyses lead to two broader conclusions: 1) Projections of future tropical storm activity obtained by a direct tracking of tropical storms simulated by coarse-resolution climate models must be interpreted with caution. 2) Projections of future tropical cyclogenesis obtained from metrics of model behavior that are based solely on changes in long-term climatological fields and tuned to historical records must also be interpreted with caution.

A six-member ensemble of twentieth-century simulations with changes to only time-evolving global distributions of black carbon aerosols in a global coupled climate model is analyzed to study the effects of black carbon (BC) aerosols on the Indian monsoon. The BC aerosols act to increase lower-tropospheric heating over South Asia and reduce the amount of solar radiation reaching the surface during the dry season, as noted in previous studies. The increased meridional tropospheric temperature gradient in the premonsoon months of March–April–May (MAM), particularly between the elevated heat source of the Tibetan Plateau and areas to the south, contributes to enhanced precipitation over India in those months. With the onset of the monsoon, the reduced surface temperatures in the Bay of Bengal, Arabian Sea, and over India that extend to the Himalayas act to reduce monsoon rainfall over India itself, with some small increases over the Tibetan Plateau. Precipitation over China generally decreases due to the BC aerosol effects. There is a weakened latitudinal SST gradient resulting from BC aerosols in the model simulations as seen in the observations, and this is present in the multiple-forcings experiments with the Community Climate System Model, version 3 (CCSM3), which includes natural and anthropogenic forcings (including BC aerosols). The BC aerosols and consequent weakened latitudinal SST gradient in those experiments are associated with increased precipitation during MAM in northern India and over the Tibetan Plateau, with some decreased precipitation over southwest India, the Bay of Bengal, Burma, Thailand, and Malaysia, as seen in observations. During the summer monsoon season, the model experiments show that BC aerosols have likely contributed to observed decreasing precipitation trends over parts of India, Bangladesh, Burma, and Thailand. Analysis of single ensemble members from the multiple-forcings experiment suggests that the observed increasing precipitation trends over southern China appear to be associated with natural variability connected to surface temperature changes in the northwest Pacific.

The Community Climate System Model version 3 (CCSM3) has recently been developed and released to the climate community. CCSM3 is a coupled climate model with components representing the atmosphere, ocean, sea ice, and land surface connected by a flux coupler. CCSM3 is designed to produce realistic simulations over a wide range of spatial resolutions, enabling inexpensive simulations lasting several millennia or detailed studies of continental-scale dynamics, variability, and climate change. This paper will show results from the configuration used for climate-change simulations with a T85 grid for the atmosphere and land and a grid with approximately 1° resolution for the ocean and sea ice. The new system incorporates several significant improvements in the physical parameterizations. The enhancements in the model physics are designed to reduce or eliminate several systematic biases in the mean climate produced by previous editions of CCSM. These include new treatments of cloud processes, aerosol radiative forcing, land–atmosphere fluxes, ocean mixed layer processes, and sea ice dynamics. There are significant improvements in the sea ice thickness, polar radiation budgets, tropical sea surface temperatures, and cloud radiative effects. CCSM3 can produce stable climate simulations of millennial duration without ad hoc adjustments to the fluxes exchanged among the component models. Nonetheless, there are still systematic biases in the ocean–atmosphere fluxes in coastal regions west of continents, the spectrum of ENSO variability, the spatial distribution of precipitation in the tropical oceans, and continental precipitation and surface air temperatures. Work is under way to extend CCSM to a more accurate and comprehensive model of the earth’s climate system.

This research assesses the influences of Atlantic Multidecadal Variability (AMV) on global tropical cyclones (TCs) using two large ensembles of idealized global climate model simulations with opposite signs of AMV forcings superimposed (i.e., AMV+ and AMV–). We first detect TCs and then compare TC activity by basin in the two AMV experiments. We find contrasting responses of Atlantic and Pacific TC frequency to the AMV anomalies. Compared to AMV–, AMV+ significantly increases TC frequency in the North Atlantic, including those making landfalls. The increase is explained by warmer sea surface temperature, higher relative humidity, increased relative vorticity, and weaker vertical wind shear under AMV+. By contrast, AMV+ decreases TC occurrence over the western North Pacific and South Pacific, which is tied to stronger vertical wind shear and lower relative humidity. The opposite responses of TC activity to AMV+ are attributed to strengthened Walker Circulation between the Atlantic and Pacific. Plain Language Summary The Atlantic Multidecadal Variability (AMV), a multidecadal variation in North Atlantic sea surface temperatures (SSTs), is a useful predictor for North Atlantic tropical cyclones (TCs). Yet how AMV influences TC activity on a global scale remains poorly understood. To fill this gap, we analyze two sets of idealized climate model simulations which differ from each other by either adding or subtracting AMV anomalies from the mean SST conditions over the North Atlantic. We first identify TCs in the simulations, and then compare and explain their differences. Relative to cold SST anomaly of AMV (AMV–), warm SST anomaly of AMV (AMV+) produces much more frequent TCs (including those making landfalls) over the North Atlantic. This is because AMV+ offers favorable conditions for TC development, including warmer SSTs, higher relative humidity, increased relative vorticity, and weaker vertical wind shear. By contrast, AMV+ causes less frequent TCs across the western North Pacific and South Pacific due to unfavorable conditions for TC occurrence (stronger vertical wind shear and less moist air). The contrasts in TC environment are due to increased zonal flow between the Atlantic and Pacific basins with AMV+. Key Points The warm phase of the Atlantic Multidecadal Variability (AMV) increases tropical cyclone (TC) frequency over the North Atlantic By contrast, the warm phase of AMV reduces TC occurrence in the western North Pacific and South Pacific The contrast in basinwide TCs is due to opposite changes in thermodynamics and dynamics with warm AMV

Significance We find that many of the Earth's climate variables, including surface temperature, outgoing longwave radiation, cooling rates, and frozen surface extent, are sensitive to far-IR surface emissivity, a largely unconstrained, temporally and spatially heterogeneous scaling factor for the blackbody radiation from the surface at wavelengths between 15 μm and 100 μm. We also describe a previously unidentified mechanism that amplifies high-latitude and high-altitude warming in finding significantly lower values of far-IR emissivity for ocean and desert surfaces than for sea ice and snow. This leads to a decrease in surface emission at far-IR wavelengths, reduced cooling to space, and warmer radiative surface temperatures. Far-IR emissivity can be measured from spectrally resolved observations, but such measurements have not yet been made. Presently, there are no global measurement constraints on the surface emissivity at wavelengths longer than 15 μm, even though this surface property in this far-IR region has a direct impact on the outgoing longwave radiation (OLR) and infrared cooling rates where the column precipitable water vapor (PWV) is less than 1 mm. Such dry conditions are common for high-altitude and high-latitude locations, with the potential for modeled climate to be impacted by uncertain surface characteristics. This paper explores the sensitivity of instantaneous OLR and cooling rates to changes in far-IR surface emissivity and how this unconstrained property impacts climate model projections. At high latitudes and altitudes, a 0.05 change in emissivity due to mineralogy and snow grain size can cause a 1.8–2.0 W m ⁻² difference in the instantaneous clear-sky OLR. A variety of radiative transfer techniques have been used to model the far-IR spectral emissivities of surface types defined by the International Geosphere-Biosphere Program. Incorporating these far-IR surface emissivities into the Representative Concentration Pathway (RCP) 8.5 scenario of the Community Earth System Model leads to discernible changes in the spatial patterns of surface temperature, OLR, and frozen surface extent. The model results differ at high latitudes by as much as 2°K, 10 W m ⁻², and 15%, respectively, after only 25 y of integration. Additionally, the calculated difference in far-IR emissivity between ocean and sea ice of between 0.1 and 0.2, suggests the potential for a far-IR positive feedback for polar climate change.

A new version of the Community Atmosphere Model (CAM) has been developed and released to the climate community. CAM Version 3 (CAM3) is an atmospheric general circulation model that includes the Community Land Model (CLM3), an optional slab ocean model, and a thermodynamic sea ice model. The dynamics and physics in CAM3 have been changed substantially compared to implementations in previous versions. CAM3 includes options for Eulerian spectral, semi-Lagrangian, and finite-volume formulations of the dynamical equations. It supports coupled simulations using either finite-volume or Eulerian dynamics through an explicit set of adjustable parameters governing the model time step, cloud parameterizations, and condensation processes. The model includes major modifications to the parameterizations of moist processes, radiation processes, and aerosols. These changes have improved several aspects of the simulated climate, including more realistic tropical tropopause temperatures, boreal winter land surface temperatures, surface insolation, and clear-sky surface radiation in polar regions. The variation of cloud radiative forcing during ENSO events exhibits much better agreement with satellite observations. Despite these improvements, several systematic biases reduce the fidelity of the simulations. These biases include underestimation of tropical variability, errors in tropical oceanic surface fluxes, underestimation of implied ocean heat transport in the Southern Hemisphere, excessive surface stress in the storm tracks, and offsets in the 500-mb height field and the Aleutian low.

We present an analysis of version 5.1 of the Community Atmospheric Model (CAM5.1) at a high horizontal resolution. Intercomparison of this global model at approximately 0.25°, 1°, and 2° is presented for extreme daily precipitation as well as for a suite of seasonal mean fields. In general, extreme precipitation amounts are larger in high resolution than in lower‐resolution configurations. In many but not all locations and/or seasons, extreme daily precipitation rates in the high‐resolution configuration are higher and more realistic. The high‐resolution configuration produces tropical cyclones up to category 5 on the Saffir‐Simpson scale and a comparison to observations reveals both realistic and unrealistic model behavior. In the absence of extensive model tuning at high resolution, simulation of many of the mean fields analyzed in this study is degraded compared to the tuned lower‐resolution public released version of the model. Key Points The effects of increasing resolution in an atmospheric model are presented Global models at 25 km can realistically simulate tropical cyclone statistics Increased resolution alone does not improve model defects

Despite the emerging influence of anthropogenic climate change on the global water cycle, at regional scales the combination of observational uncertainty, large internal variability, and modeling uncertainty undermine robust statements regarding the human influence on precipitation. Here, we use output from global climate models in a perfect-data sense to develop a framework for conducting regional detection and attribution (D&A) for precipitation, starting with the contiguous United States (CONUS) where observational uncertainty is lower than in other regions. Our unified approach can simultaneously detect systematic trends in mean and extreme precipitation, attribute trends to anthropogenic forcings, compute the effects of forcings as a function of time, and map the effects of individual forcings. Model output is used to conduct a set of tests that yield a parsimonious representation for characterizing seasonal precipitation over the CONUS for the historical record (1900 to present day), which ensures our D&A is insensitive to structural uncertainty. Our framework is developed using synthetic data in a Pearl-causal perspective wherein causality can be identified using intervention-based simulations. While the hypothesis-based framework and accompanying generalized D&A formula we develop should be widely applicable, we include a strong caution that the hypothesis-guided simplification of the formula for the historical climatic record of CONUS as described in this paper will likely fail to hold in other geographic regions and under future warming.