A new algorithm is proposed for estimating time-evolving global forcing in climate models. The method is a further development of the work of Forster et al. (2013), taking into account the non-constancy of the global feedbacks. We assume the non-constancy of this global feedback can be explained as a time-scale dependence, associated with linear temperature responses to the forcing on different time scales. With this method we obtain stronger forcing estimates than previously assumed for the representative concentration pathway experiments in the Coupled Model Intercomparison Project Phase 5 (CMIP5). The reason for the higher future forcing is that the global feedback parameter is more negative at shorter time scales than at longer time scales, consistent with the equilibrium climate sensitivity increasing with equilibration time. Our definition of forcing provides a clean separation of forcing and response, and we find that linear temperature response functions estimated from experiments with abrupt quadrupling of CO$_2$ can be used to predict responses also for future scenarios. In particular, we demonstrate that applying this response to our new forcing estimate predicts the modelled response up to year 2100 quite well for most models.

Radiation schemes are physically important but computationally expensive components of weather and climate models. This has spurred efforts to replace them with a cheap emulator based on neural networks (NN), obtaining large speed-ups, but at the expense of accuracy, energy conservation and generalization. An alternative approach which is slower but more robust than full emulation is to use NNs to predict optical properties, without abandoning the radiative transfer equations. Recently, NNs were developed to replace the RRTMGP gas optics scheme, and shown to be accurate while improving speed.However, the evaluations were based solely on offline radiation computations. In this paper, we describe the implementation and prognostic evaluation of RRTMGP-NN in the Integrated Forecasting System (IFS) of the European Centre for Medium-Range Weather Forecasts (ECMWF). The new gas optics scheme was incorporated into ecRad, the modular ECMWF radiation scheme. Using a hybrid loss function designed to reduce radiative forcing errors, and an early stopping method based on monitoring fluxes and heating rates with respect to a line-by-line benchmark, new NN models were trained on RRTMGP k-distributions with reduced spectral resolutions. Offline evaluation shows a very high level of accuracy for clear-sky fluxes and heating rates; for instance the RMSE in shortwave surface downwelling flux is 0.78 W m−2 for RRTMGP and 0.80 W m−2 for RRTMGP-NN in a present-day scenario, while upwelling flux errors are actually smaller for the NN. Because our approach does not affect the treatment of clouds, no additional errors will be introduced for cloudy profiles. RRTMGP-NN closely reproduces radiative forcings for 5 important greenhouse gases across a wide range of concentrations such as 8x CO2. To assess the impact of different gas optics schemes in the IFS, four 1-year coupled ocean-atmosphere simulations were performed for each configuration. The results show that RRTMGP-NN and RRTMGP produce very similar model climates, with the differences being smaller than those between existing schemes, and statistically insignificant for zonal means of single-level quantities such as surface temperature. The use of RRTMGP-NN speeds up ecRad by a factor of 1.5 compared to RRTMGP (the gas optics being almost 3 times faster), and is also faster than the older and less accurate RRTMG which is used in the current operational cycle of the IFS

The January 2022 Hunga Tonga–Hunga Haʻapai eruption was one of the most explosive volcanic events of the modern era, producing a vertical plume which peaked > 50km above the Earth. The initial explosion and subsequent plume triggered atmospheric waves which propagated around the world multiple times. A global-scale wave response of this magnitude from a single source has not previously been observed. Here we show the details of this response, using a comprehensive set of satellite and ground-based observations to quantify it from surface to ionosphere. A broad spectrum of waves was triggered by the initial explosion, including Lamb waves5,6 propagating at phase speeds of 318.2+/-6 ms-1 at surface level and between 308+/-5 to 319+/-4 ms-1 in the stratosphere, and gravity waves propagating at 238+/-3 to 269+/-3 ms-1 in the stratosphere. Gravity waves at sub-ionospheric heights have not previously been observed propagating at this speed or over the whole Earth from a single source. Latent heat release from the plume remained the most significant individual gravity wave source worldwide for >12 hours, producing circular wavefronts visible across the Pacific basin in satellite observations. A single source dominating such a large region is also unique in the observational record. The Hunga Tonga eruption represents a key natural experiment in how the atmosphere responds to a sudden point-source-driven state change, which will be of use for improving weather and climate models.

Survey evidence has indicated that a significant percentage of the population does not fully embrace the scientific consensus regarding climate change. This paper assesses whether the hourly temperature data support this denial. The analysis examines the relationship between hourly CO2 concentration levels and temperature using hourly data from the NOAA-operated Barrow observatory in Alaska. At this observatory, the average annual temperature over the 2015-2020 period was about 3.37 oC higher than in 1985–1990. A time-series model to explain hourly temperature is formulated using the following explanatory variables: the hourly level of total downward solar irradiance, the CO2 value lagged by one hour, proxies for the diurnal variation in temperature, proxies for the seasonal temperature variation, and proxies for possible non-anthropomorphic drivers of temperature. The purpose of the time-series approach is to capture the data’s heteroskedastic and autoregressive nature, which would otherwise “mask” CO2’s “signal” in the data. The model is estimated using hourly data from 1985 through 2015. The results are consistent with the hypothesis that increases in CO2 concentration levels have nontrivial consequences for hourly temperature. The estimated annual contributions of factors exclusive of CO2 and downward total solar irradiance are very small. The model was evaluated using out-of-sample hourly data from 1 Jan 2016 through 31 Aug 2017. The model’s out-of-sample hourly temperature predictions are highly accurate, but this accuracy is significantly degraded if the estimated CO2 effects are ignored. In short, the results are consistent with the scientific consensus on climate change.

Moisture recycling via evapotranspiration (ET) is often invoked as a mechanism for the high deuterium excess signals observed in continental precipitation (dP). However, a global-scale analysis of precipitation monitoring station isotope data shows that metrics of ET contributions to precipitation (van der Ent et al., 2014) explain little dp variability on seasonal timescales. This occurs despite the fact that ET contributions increase by ~50% in continental locations such as the Eurasian interior from wet to dry seasons. To explain this apparent paradox, we hypothesize that the effects of ET on dP are dampened during dry seasons due to contributions from isotopically-evolved residual water storage that act to lower the d-excess of ET fluxes (dET), in combination with changes in transpiration fraction (T/ET). To test this hypothesis, we develop a parsimonious two-season (wet, dry) model for dET incorporating residual water storage and ET partitioning effects. We find that in environments with limited water storage, such as shallow-rooted grasslands, dry season dET is lower than wet season dET despite lower relative humidity. As global average ratios of annual water storage to precipitation are relatively low (Guntner et al., 2007), these dynamics may be widespread over continents. In environments where water storage is not limiting, such as groundwater-dependent ecosystems, dry season dET is still likely lower; however, this effect arises instead due to higher seasonal T/ET when energy-driven plant water use is enhanced and surface evaporation is relatively limited by water availability. Together, these analyses also indicate multiple mechanisms by which dET may be lower than dp during the same season, challenging the view that moisture recycling feedback increases the dp in continental interiors. This work demonstrates the potential complexity of seasonal dp dynamics and cautions against simple interpretations of dP as a process tracer for moisture recycling. References: Guntner et al., 2007. Water Resour. Res., 43, W05416. van der Ent et al., 2014. Earth Syst. Dynam., 5, 471–489.

Mangrove forests are among the most productive ecosystems in the world. These tropical and subtropical coastal forests provide a wide array of ecosystem services, including the ability to sequester and store large amounts of ‘blue carbon’. Given rising concerns over anthropogenic carbon dioxide (CO2) emissions, mangrove forests have been increasingly recognized for their potential in climate change mitigation programs. However, their productivity differs considerably across environments, making it difficult to estimate carbon sequestration potentials at regional scales. Additionally, most research has focused in humid and tropical latitudes, with limited studies in arid and semi-arid regions. A semi-arid mangrove forest in Magdalena Bay, Baja California Sur, Mexico was studied to quantify the average net ecosystem exchange (NEE), determine the annual carbon (C) budget and the environmental controls driving those fluxes. Measurements were taken during 2012-2013 using the eddy covariance technique, with a daily mean NEE of -2.25 +/- 0.4 g C m-2 d-1 and annual carbon uptake of 894 g C m-2 y-1. Daily variations in NEE were primarily regulated by light, but air temperature and vapor pressure deficit were strong seasonal drivers. Our research demonstrates that despite the harsh and arid climate, the mangroves of Magdalena Bay were nearly as productive as mangroves found in tropical and subtropical climates. These results broaden understanding of the ecosystem services of one of the largest mangrove ecosystems in the Baja California peninsula, and highlight the potential role of arid mangrove ecosystems for C accounting, management and mitigation plans for the region.

The TRACMIP ensemble includes slab-ocean aquaplanet control simulations and experiments with a highly idealized narrow tropical continent (0-45ºW; 30ºS - 30ºN). We compare the two setups to contrast the characteristics of oceanic and continental rain bands and investigate monsoon development in GCMs with CMIP5-class dynamics and physics. Over land, the rainy season occurs close to the time of maximum insolation. Other than in its timing, the continental rain band remains in an ITCZ-like regime akin deep-tropical monsoons, with a smooth latitudinal transition, a poleward reach only slightly farther than the oceanic ITCZ’s (about 10º), and a constant width throughout the year. This confinement of the monsoon to the deep tropics is the result of a tight coupling between regional rainfall and circulation anomalies: ventilation of the lower troposphere by the anomalous meridional circulation is the main limiting mechanism, while ventilation by the mean westerly jet aloft is secondary. Comparison of two sub-sets of TRACMIP simulations indicates that a low heat capacity determines, to a first degree, both the timing and the strength of the regional solsticial circulation; this lends support to the choice of idealizing land as a thin slab ocean in much theoretical literature on monsoon dynamics. Yet, the timing and strength of the monsoon are modulated by the treatment of evaporation over land, especially when moisture and radiation can interact. This points to the need for a fuller exploration of land characteristics in the hierarchical modeling of the tropical rain bands.

Concerns about water security often inform climate risk-related decisions made by environmentally focused investors (Porritt, 2001; Stern, 2006). Yet, potential liabilities for damage caused by extreme flood and drought events linked to global warming present risks that are not always reflected in share prices (Krosinsky et al., 2012). Considering the highly destructive nature of such events, we query whether companies, or specific sectors, could and should be held at least partially liable for their emission-releasing business activities. Recent articles (Rayer & Millar, 2018; Rayer et al., 2020) estimate that under a hypothetical climate liability regime, North Atlantic hurricane seasons might increasingly generate 1-2% losses on market capitalizations (or share prices) for the top seven carbon-emitting, publicly listed companies. In this paper, we extend the concept of the climate liability regime to estimate the impact of global flood- and drought-related damages on the share prices of nine fossil-fuel firms (including the seven mentioned by Rayer et al. (2020)). Following Rayer et al. (2020), we use incremental climate impacts and historical corporate emissions to estimate that climate change-related global flood and drought damages for the period of 2012 to 2016 amount to approximately 2-3% of the top nine carbon-emitting companies’ market capitalizations. We also include a discussion of moral responsibility and the proportion of obligations between producers and users. Quantifying impacts from extreme weather events increases salience and serves as an example of how science can identify and address the important business questions, pertinent to both investors and companies, that arise from a changing climate. References Krosinsky, C., Robins, N., & Viederman, S. (2012). Evolutions in sustainable investing. John Wiley & Sons. Porritt, J. (2001). The world in context. HRH The Prince of Wales’ Business and the Environment Programme, Cambridge. Rayer, Q. G., & Millar, R. J. (2018). Investing in Extreme Weather Conditions. Citywire Wealth Manager®, (429) 36. Rayer, Q., Pfleiderer, P., & Haustein, K. (2020). Global Warming and Extreme Weather Investment Risks. Palgrave Macmillan. https://doi.org/10.1007/978-3-030-38858-4_3 Stern, N. (2006). Stern Review executive summary. London.

By injecting SO2 into the stratosphere at four latitudes (30°, 15° N/S), it might be possible not only to reduce global mean surface temperature but also to minimize changes in the equator-to-pole and inter-hemispheric gradients of temperature, further reducing some of the impacts arising from climate change relative to equatorial injection. This can happen only if the aerosols are transported to higher latitudes by the stratospheric circulation, ensuring that a greater part of the solar radiation is reflected back to space at higher latitudes, compensating for the reduced sunlight. However, the stratospheric heating produced by these aerosols modifies the circulation and strengthens the stratospheric polar vortex which acts as a barrier to the transport of air toward the poles. We show how the heating results in a feedback where increasing injection rates lead to stronger high-latitudinal transport barriers. This implies a potential limitation in the high-latitude aerosol burden and subsequent cooling.

Pyrocumulonimbus (pyroCb) are fire-triggered or fire-augmented thunderstorms and can by transporting a large amount of smoke particles into the lower stratosphere. With satellite remote sensing measurements, the plumes from pyroCb events over British Columbia in 2017 were observed in the lower stratosphere for about 8-10 months after the smoke injections. Several previous studies used global climate models to investigate the physical parameters for the 2017 pyroCb events, but the conclusions show strong model dependency. In this study, we use Energy Exascale Earth System Model (E3SM) atmosphere model version 1 (EAMv1) and complete an ensemble of runs exploring three injection parameters: smoke amount, the ratio of black carbon to smoke, and injection height. Additionally, we consider the heterogeneous reaction of ozone and primary organic matter. According to the satellite daily observed aerosol optical depth, we find that the best ensemble member is the simulation with 0.4 Tg of smoke, 3% of which is black carbon, a 13.5 km smoke injection height, and a 10-5 probability factor of the heterogeneous reaction of ozone and primary organic matter. We use the Random Forest machine learning technique to quantify the relative importance of each parameter in accurately simulating the 2017 pyroCb events and find that the injection height is the most critical feature. Due to the long lifetime and wide transport of stratospheric aerosols, the estimated e-folding time of smoke aerosols in the stratosphere is about 188 days, and the global averaged shortwave surface cooling is -0.292 W m-2 for about 10 months.

Improved knowledge of the contributing sources of uncertainty in projections of Arctic sea ice over the 21st century is essential for evaluating impacts of a changing Arctic environment. Here, we consider the role of internal variability, model structure and emissions scenario in projections of Arctic sea-ice area (SIA) by using six single model initial-condition large ensembles and a suite of models participating in Phase 5 of the Coupled Model Intercomparison Project. For projections of September Arctic SIA change, internal variability accounts for as much as 40-60% of the total uncertainty in the next decade, while emissions scenario dominates uncertainty toward the end of the century. Model structure accounts for approximately 60-70% of the total uncertainty by mid-century and declines to 30% at the end of the 21st century during the summer months. For projections of wintertime Arctic SIA change, internal variability contributes as much as 50-60% of the total uncertainty in the next decade and impacts total uncertainty at longer lead times when compared to the summertime. Model structure contributes most of the remaining uncertainty with emissions scenario contributing little to the total uncertainty during the winter months. At regional scales, the contribution of internal variability can vary widely and strongly depends on the month and region. For wintertime SIA change in the GIN and Barents Seas, internal variability contributes approximately 60-70% to the total uncertainty over the coming decades and remains important much longer than in other regions. We further find that the relative contribution of internal variability to total uncertainty is state-dependent and increases as sea ice volume declines. These results demonstrate the need to improve the representation of internal variability of Arctic SIA in models, which is a significant source of uncertainty in future projections.

We present a very high-resolution (400 m) operational air quality forecasting system developed to alert citizens of Delhi and the National Capital Region (NCR) about acute air pollution episodes. Such a high-resolution system has been developed for the first time and is evaluated during October 2019-February 2020. The system assimilates near real time aerosol observations from in situ and space-borne observations in the WRF-Chem model to produce a 72-h forecast every day in a dynamical downscaling framework. The assimilation of aerosol optical depth and surface PM 2.5 observations improves the initial condition for surface PM 2.5 by about 45 µg/m 3 (about 50%). The accuracy of the forecast degrades slightly with time as mean bias increases from +2.5 µg/m 3 on the first day to-17 µg/m 3 on the third day of forecast. Our forecasts are found to be very capable both for PM 2.5 concentration and unhealthy/ very unhealthy air quality indices categories. 2

The atmospheric aerosol dynamics model (AADM) has been widely used in both comprehensive air quality model systems and chemical transport modeling from road to global scales. The AADM consists of the Smoluchowski coagulation equation (SCE) which describes the atmospheric aerosol size growth due to coagulation. The numerical solution to the SCE undergoing Brownian coagulation in the free molecular regime is a direct challenge because of a stumbling block for the kernel to be expressed by an equivalent linear expression and a predefined lognormal size distribution, which is inconsistent with aerosols having bimodal or multimodal size distribution. Thus, a new mathematical method for solving the SCE without the strong assumption of log-normal size distribution is proposed and developed. This method is verified with a referenced sectional method (SM) with excellent agreement. The accuracy of the method approaches closely to the TEMOM, but overcomes the limitation of the classical log MOM. The computational time of this scheme is largely reduced when comparing to the SM. The new method is successfully implemented to reveal the formation and growth of secondary particles emitted from the vehicle exhaust tailpipe. It is surprisingly found that the formation of new particles only appears in the interface region of the turbulent exhaust jet which is very close to the tailpipe exit, while there is no new particle formation in the strong mixture along the downstream. The new method is finally verified to be an efficient and reliable numerical scheme for studying atmospheric aerosol dynamics.

In atmospheric modeling, superparameterization has gained popularity as a technique to improve cloud and convection representations in large scale models by coupling them locally to cloud-resolving models. We show how the different representations of cloud water in the local and the global models in superparameterization lead to a suppression of cloud advection and ultimately to a systematic underrepresentation of the cloud amount in the large scale model. We demonstrate this phenomenon in a regional superparameterization experiment with the global model OpenIFS coupled to the local model DALES (the Dutch Atmospheric Large Eddy Simulation), as well as in an idealized setup, where the large-scale model is replaced by a simple advection scheme. To mitigate the problem of suppressed cloud advection, we propose a scheme where the spatial variability of the local model’s total water content is enhanced in order to achieve the correct cloud condensate amount.

The Indo-Gangetic Plain (IGP) is one of the dominant sources of air pollution worldwide. During winter, the variations in planetary boundary layer (PBL) height, driven by a strong radiative thermal inversion, affect the regional air pollution dispersion. To date, measurements of aerosol-water vapour interactions, especially cloud condensation nuclei (CCN) activity, are limited in the Indian sub-continent, causing large uncertainties in the radiative forcing estimates of aerosol-cloud interactions. We present the results of a one-month field campaign (February-March 2018) in the megacity, Delhi, a significant polluter in the IGP. We measured the composition of fine particulate matter (PM1) and size-resolved CCN properties over a wide range of water vapour supersaturations. The analysis includes PBL modelling, backward trajectories, and fire spots to elucidate the influence of PBL and air mass origins on the aerosols. The aerosol properties depended strongly on the PBL height, and a simple power-law fit could parameterize the observed correlations of PM1 mass, aerosol particle number, and CCN number with PBL height, indicating PBL induced changes in aerosol accumulation. The low inorganic mass fractions, low aerosol hygroscopicity and high externally mixed weakly CCN-active particles under low PBL height (<100 m) indicated the influence of the PBL on aerosol aging processes. In contrast, aerosol properties did not depend strongly on air mass origins or wind direction, implying that the observed aerosol and CCN are from local emissions. An error function could parameterize the relationship between CCN number and supersaturation throughout the campaign.

In calculating solar radiation, climate models make many simplifications, in part to reduce computational cost and enable climate modeling, and in part from lack of understanding of critical atmospheric information. Whether known errors or unknown errors, the community’s concern is how these could impact the modeled climate. The simplifications are well known and most have published studies evaluating them, but with individual studies it is difficult to compare. Here we collect a wide range of such simplifications in either radiative transfer modeling or atmospheric conditions and assess potential errors within a consistent framework on climate-relevant scales. We build benchmarking capability around a solar heating code (Solar-J) that doubles as a photolysis code for chemistry and can be readily adapted to consider other errors and uncertainties. The broad classes here include: use of broad wavelength bands to integrate over spectral features; scattering approximations that alter phase function and optical depths for clouds and gases; uncertainty in ice-cloud optics; treatment of fractional cloud cover including overlap; and variability of ocean surface albedo. We geographically map the errors in W m-2 using a full climate re-creation for January 2015 from a weather forecasting model. For many approximations assessed here, mean errors are ~2 W m-2 with greater latitudinal biases and are likely to affect a model’s ability to match the current climate state. Combining this work with previous studies, we make priority recommendations for fixing these simplifications based on both the magnitude of error and the ease or computational cost of the fix.

Narrow Bipolar Events (NBEs) are powerful radio emissions from thunderstorms which have been recently associated with blue optical flashes on cloud tops and attributed to extensive streamer electrical discharges named fast breakdown. Combining data obtained from a thunderstorm over South China by the space-based Atmosphere Space Interactions Monitor (ASIM), the Vaisala GLD360 global lightning network and very low frequency (VLF)/low frequency (LF) radio detectors, here we report and analyze for the first time the optical emissions of Blue LUminous Events (BLUEs) associated with negative NBEs and located at the top edge of a thundercloud. These emissions are weakly affected by scattering from cloud droplets, allowing us to estimate the source extension and optical energy involved in the process. The optical energy in the 337-nm band emitted by fast breakdown is about 10^4 J, which involves around 10^9 streamer initiation events.

Clouds are primary modulators of Earth's energy balance. It is thus important to understand the links connecting variabilities in cloudiness to variabilities in other state variables of the climate system, and also describe how these links would change in a changing climate. A conceptual model of global cloudiness can help elucidate these points. In this work we derive simple representations of cloudiness, that can be useful in creating a theory of global cloudiness. These representations illustrate how both spatial and temporal variability of cloudiness can be expressed in terms of basic state variables. Specifically, cloud albedo is captured by a nonlinear combination of pressure velocity and a measure of the low-level stability, and cloud longwave effect is captured by surface temperature, pressure velocity, and standard deviation of pressure velocity. We conclude with a short discussion on the usefulness of this work in the context of global warming response studies.

Rain belts in East Asia frequently pose threats to human societies and natural systems. Advances in a skillful forecast on heavy precipitation require a deeper understanding of the preconditioned environments and the hydrologic cycle. Here, we disentangle 15 dominant moisture channels along four corridors reaching the Somali Jet, South Asia, Bay of Bengal and Pacific basin for the warm-season rain belts. Among them, the Somali and South Asian channels were underappreciated in the literature. The results also highlight the importance of terrestrial moisture sources and the close relationship between the moisture pathways and rain belts' characteristics. Back-tracing the weather within a 2-week lead time reveals the pre-existing weather systems and circumglobal wave trains that govern the moisture channels. Findings from this work develop a better understanding of East Asian rain belts' water cycle and may offer insights into model evaluation and heavy rainfall prediction at a longer lead time.

Work in recent decades has demonstrated a robust relationship between tropical precipitation and the column relative humidity (CRH). This study identifies a similar relationship between CRH and the atmospheric cloud radiative effect (ACRE) calculated from satellite observations. Like precipitation, the ACRE begins to increase rapidly when CRH exceeds a critical value near 75\%. We show that the ACRE can be estimated from CRH, similar to the way that CRH has been used to estimate precipitation. Our method reproduces the annual mean spatial structure of ACRE in the tropics, and skillfully estimates the mean ACRE on monthly and daily time scales in six regions of the tropics. We propose that the exponential dependence of precipitation on CRH is a result of cloud-longwave feedbacks, which facilitate a shift from convective to stratiform conditions.

Mixed-phase clouds play an important role in determining Arctic warming, but are parametrized in models and difficult to constrain with observations. We use two satellite-derived cloud phase metrics to investigate the vertical structure of Arctic clouds in two global climate models that use the Community Atmosphere Model version 6 (CAM6) atmospheric component. We report a model error limiting ice nucleation, produce a set of Arctic-constrained model runs by adjusting model microphysical variables to match the cloud phase metrics, and evaluate cloud feedbacks for all simulations. Models in this small ensemble uniformly overestimate total cloud fraction in the summer, but have variable representation of cloud fraction and phase in the winter and spring. By relating modelled cloud phase metrics and changes in low-level liquid cloud amount under warming to longwave cloud feedback, we show that mixed-phase processes mediate the Arctic climate by modifying how wintertime and springtime clouds respond to warming.

Clouds cover on average nearly 70% of Earth’s surface and regulate the global albedo. The magnitude of the shortwave reflection by clouds depends on their location, optical properties, and three-dimensional (3D) structure. Due to computational limitations, Earth system models are unable to perform 3D radiative transfer calculations. Instead they make assumptions, including the independent column approximation (ICA), that neglect effects of 3D cloud morphology on albedo. We show how the resulting radiative flux bias (ICA-3D) depends on cloud morphology and solar zenith angle. Using large-eddy simulations to produce 3D cloud fields, a Monte Carlo code for 3D radiative transfer, and observations of cloud climatology, we estimate the effect of this flux bias on global climate. The flux bias is largest at small zenith angles and for deeper clouds, while the negative albedo bias is most prominent for large zenith angles. In the tropics, the radiative flux bias from neglecting 3D radiative transfer is estimated to be 4.0 +/- 2.4 Wm-2 in the mean and locally as large as 9 Wm-2.

A simple yet flexible and robust algorithm is described for fully partitioning an arbitrary dataset into compact, non-overlapping groups or classes, sorted by size, based entirely on a pairwise similarity matrix and a user-specified similarity threshold. Unlike many clustering algorithms, there is no assumption that natural clusters exist in the dataset, though clusters, when present, may be preferentially assigned to one or more classes. The method also does not require data objects to be compared within any coordinate system but rather permits the user to define pairwise similarity using almost any conceivable criterion. The method therefore lends itself to certain geoscientific applications for which conventional clustering methods are unsuited, including two non-trivial and distinctly different datasets presented as examples. In addition to identifying large classes containing numerous similar dataset members, it is also well-suited for isolating rare or anomalous members of a dataset. The method is inductive, in that prototypes identified in representative subset of a larger dataset can be used to classify the remainder.

This article introduces factors contributing significantly to climate change that have been largely neglected in both the scientific and popular press. These factors have immediate implications for public policy directed at slowing, halting and even reversing climate change and its effects. This article argues that in addition to the known contributions made by greenhouse gasses, climate change is also driven by shifts in the patterns of global atmospheric circulation which are influenced by persistent, large-scale vortices caused by the wake turbulence left by commercial air traffic. Because this traffic is highly concentrated along the most frequently traveled routes, the vortices aircraft create have transformed into semi-permanent atmospheric circulation which have widespread effects on how the atmosphere traps and releases heat. It is also possible that these changes alter the loss of water from the atmosphere. This would endanger all life on earth, not just the human population.