Building a long-term cloud record from spaceborne lidars: merging CALIOP with ATLID

The Clouds and Earth's Radiant Energy System (CERES) mission currently employs four instruments onboard two spacecraft to measure the earth's reflected shortwave energy and the earth emitted thermal energy that represents two components of the earth's energy budget. These measurements are made through three sensors that measure different spectral channels- a shortwave channel that measures the 0.3 to 5 microns wavelength band, a total channel that measures all the incident energy (0.3 to ~200 microns) and a window channel that measures the 8 to 12 micron wavelength band. The radiances measured in each channel (filtered radiances) are used to estimate the incident (unfiltered) shortwave and longwave radiances using knowledge of the response functions of each of the measurement channels as well as theoretical knowledge of the energy spectrum of the earth scene being measured. For longer wavelengths particularly in the far infrared, both the earth scene spectra as well as the instrument spectral response functions are not very well characterized because of the difficulties in obtaining models for the earth scene spectra as well as the limitations in the capabilities to measure the spectral responses over a very large spectral range. This results in errors in obtaining estimates of the unfiltered radiances. This paper will focus on studying the sensitivity of the CERES instrument to these inaccuracies and its impact on the errors in estimation. In addition, those spectral regions in the longwave infrared where the CERES instruments are most sensitive will be identified.

The correct identification of variables affecting parasite diversity and assemblage composition at different spatial scales is crucial for understanding how pathogen distribution responds to anthropogenic disturbance and climate change. Here, we used a database of avian haemosporidian parasites to test how the taxonomic andphylogenetic diversity and phylogenetic structure of the genera Plasmodium, Haemoproteus and Leucocytozoon from three zoogeographic regions are related to surrogate variables of Earth's energy input, habitat heterogeneity (climatic diversity, landscape heterogeneity, host richness and human disturbance) and ecological interactions (resource use), which was measured by a novel assemblage-level metric related to parasite niche overlap (degree of generalism). We found that different components of energy input explained variation in richness for each genus. We found that human disturbance influences the phylogenetic structure of Haemoproteus while the degree of generalism explained richness and phylogenetic structure of Plasmodium and Leucocytozoon genera. Furthermore, landscape attributes related to human disturbance (human footprint) can filter Haemoproteus assemblages by their phylogenetic relatedness. Finally, assembly processes related to resource use within parasite assemblages modify species richness and phylogenetic structure of Plasmodium and Leucocytozoon assemblages. Overall, our study highlighted the genus-specific patterns with the different components of Earth's energy budget, human disturbances and degree of generalism.

Geoneutrinos and the energy budget of the Earth

Abstract. Solar radiation is the ultimate source of energy flowing through the atmosphere; it fuels all atmospheric motions. The visible-wavelength range of solar radiation represents a significant contribution to the earth's energy budget, and visible light is a vital indicator for the composition and thermodynamic processes of the atmosphere from the smallest weather scales to the largest climate scales. The accurate and fast description of light propagation in the atmosphere and its lower-boundary environment is therefore of critical importance for the simulation and prediction of weather and climate. Simulated Weather Imagery (SWIm) is a new, fast, and physically based visible-wavelength three-dimensional radiative transfer model. Given the location and intensity of the sources of light (natural or artificial) and the composition (e.g., clear or turbid air with aerosols, liquid or ice clouds, precipitating rain, snow, and ice hydrometeors) of the atmosphere, it describes the propagation of light and produces visually and physically realistic hemispheric or 360∘ spherical panoramic color images of the atmosphere and the underlying terrain from any specified vantage point either on or above the earth's surface. Applications of SWIm include the visualization of atmospheric and land surface conditions simulated or forecast by numerical weather or climate analysis and prediction systems for either scientific or lay audiences. Simulated SWIm imagery can also be generated for and compared with observed camera images to (i) assess the fidelity and (ii) improve the performance of numerical atmospheric and land surface models. Through the use of the latter in a data assimilation scheme, it can also (iii) improve the estimate of the state of atmospheric and land surface initial conditions for situational awareness and numerical weather prediction forecast initialization purposes.

The global ocean heat content evolution is a key component of the Earth's energy budget and can be consistently determined by ocean reanalyses that assimilate hydrographic profiles. This work investigates the impact of the atmospheric reanalysis forcing through a multiforcing ensemble ocean reanalysis, where the ensemble members are forced by five state‐of‐the‐art atmospheric reanalyses during the meteorological satellite era (1979–2013). Data assimilation leads the ensemble to converge toward robust estimates of ocean warming rates and significantly reduces the spread (1.48 ± 0.18 W/m2, per unit area of the World Ocean); hence, the impact of the atmospheric forcing appears only marginal for the global heat content estimates in both upper and deeper oceans. A sensitivity assessment performed through realistic perturbation of the main sources of uncertainty in ocean reanalyses highlights that bias correction and preprocessing of in situ observations represent the most crucial component of the reanalysis, whose perturbation accounts for up to 60% of the ocean heat content anomaly variability in the pre‐Argo period. Although these results may depend on the single reanalysis system used, they reveal useful information for the ocean observation community and for the optimal generation of perturbations in ocean ensemble systems.

The zonal-mean transport of tracers on a large scale, such as ozone and water vapor, is predominantly governed by the Brewer-Dobson circulation. However, this transport undergoes modifications influenced by small-scale gravity waves (GW) and turbulence resulting from GW breaking. As these dynamics are not completely resolved in weather and climate models, they necessitate parameterization. Given the significant impact of tracers on the Earth's energy budget and surface climate, understanding their transport variations is crucial for accurate atmospheric modeling. Presently, existing GW parameterization schemes neither account for the direct effects of GW tracer transport nor the enhanced tracer mixing due to GW breaking, but only for the indirect effect by driving the mean meridional circulation. Therefore, it becomes imperative to ascertain how and to what extent these small-scale phenomena modify the large-scale transport of tracers. To address this, we employ wave-resolving simulations, specifically investigating the impact of a three-dimensional wavepacket on tracer distribution using a pseudo-incompressible flow solver. Additionally, we extend a GW parameterization scheme, a Lagrangian ray tracer, to incorporate GW-induced tracer transport. Our research demonstrates the non-negligible direct impact of GW on tracer transport. Furthermore, we possibly discuss the influence of turbulent diffusive mixing on tracers. Our aim is to provide a comprehensive understanding of the intricate processes shaping large-scale tracer transport in the atmosphere.

Atmospheric water vapour plays a key role for the Earth's energy budget and temperature distribution via radiative effects and latent heat transport. Moreover, the distribution and transport of water vapour is closely linked to atmospheric dynamics on all spatiotemporal scales. In this context, monitoring of the water vapour distribution is essential for numerical weather prediction as well as for climate modelling.The Geostationary Environment Monitoring Spectrometer (GEMS) instrument on board the GEO-KOMPSAT-2B satellite offers new opportunities for observing and investigating the regional water vapour distribution over East Asia, especially phenomena such as typhoons or atmospheric rivers, and could thus represent another valuable data source, e.g. for nowcasting systems of natural hazards.In this study, we show the first total column water vapour (TCWV) results retrieved from GEMS UV/vis spectra based on the algorithm of Borger et al. (2020). In addition, we also present an update of the existing algorithm, which, for example, replaces the previous simplified determination of the a priori water vapour profile with a deep neural network. We also compare our results to different reference data sets from ground-based in situ and remote sensing observations, reanalysis models, and satellite measurements.

Water-soluble brown carbon in atmospheric aerosols along the transport pathway of Asian dust: Optical properties, chemical compositions, and potential sources

Several large-scale climate patterns influenced climate conditions and weather patterns across the globe during 2010. The transition from a warm El Niño phase at the beginning of the year to a cool La Niña phase by July contributed to many notable events, ranging from record wetness across much of Australia to historically low Eastern Pacific basin and near-record high North Atlantic basin hurricane activity. The remaining five main hurricane basins experienced below- to well-below-normal tropical cyclone activity. The negative phase of the Arctic Oscillation was a major driver of Northern Hemisphere temperature patterns during 2009/10 winter and again in late 2010. It contributed to record snowfall and unusually low temperatures over much of northern Eurasia and parts of the United States, while bringing above-normal temperatures to the high northern latitudes. The February Arctic Oscillation Index value was the most negative since records began in 1950. The 2010 average global land and ocean surface temperature was among the two warmest years on record. The Arctic continued to warm at about twice the rate of lower latitudes. The eastern and tropical Pacific Ocean cooled about 1°C from 2009 to 2010, reflecting the transition from the 2009/10 El Niño to the 2010/11 La Niña. Ocean heat fluxes contributed to warm sea surface temperature anomalies in the North Atlantic and the tropical Indian and western Pacific Oceans. Global integrals of upper ocean heat content for the past several years have reached values consistently higher than for all prior times in the record, demonstrating the dominant role of the ocean in the Earth's energy budget. Deep and abyssal waters of Antarctic origin have also trended warmer on average since the early 1990s. Lower tropospheric temperatures typically lag ENSO surface fluctuations by two to four months, thus the 2010 temperature was dominated by the warm phase El Niño conditions that occurred during the latter half of 2009 and early 2010 and was second warmest on record. The stratosphere continued to be anomalously cool. Annual global precipitation over land areas was about five percent above normal. Precipitation over the ocean was drier than normal after a wet year in 2009. Overall, saltier (higher evaporation) regions of the ocean surface continue to be anomalously salty, and fresher (higher precipitation) regions continue to be anomalously fresh. This salinity pattern, which has held since at least 2004, suggests an increase in the hydrological cycle. Sea ice conditions in the Arctic were significantly different than those in the Antarctic during the year. The annual minimum ice extent in the Arctic—reached in September—was the third lowest on record since 1979. In the Antarctic, zonally averaged sea ice extent reached an all-time record maximum from mid-June through late August and again from mid-November through early December. Corresponding record positive Southern Hemisphere Annular Mode Indices influenced the Antarctic sea ice extents. Greenland glaciers lost more mass than any other year in the decade-long record. The Greenland Ice Sheet lost a record amount of mass, as the melt rate was the highest since at least 1958, and the area and duration of the melting was greater than any year since at least 1978. High summer air temperatures and a longer melt season also caused a continued increase in the rate of ice mass loss from small glaciers and ice caps in the Canadian Arctic. Coastal sites in Alaska show continuous permafrost warming and sites in Alaska, Canada, and Russia indicate more significant warming in relatively cold permafrost than in warm permafrost in the same geographical area. With regional differences, permafrost temperatures are now up to 2°C warmer than they were 20 to 30 years ago. Preliminary data indicate there is a high probability that 2010 will be the 20th consecutive year that alpine glaciers have lost mass. Atmospheric greenhouse gas concentrations continued to rise and ozone depleting substances continued to decrease. Carbon dioxide increased by 2.60 ppm in 2010, a rate above both the 2009 and the 1980–2010 average rates. The global ocean carbon dioxide uptake for the 2009 transition period from La Niña to El Niño conditions, the most recent period for which analyzed data are available, is estimated to be similar to the long-term average. The 2010 Antarctic ozone hole was among the lowest 20% compared with other years since 1990, a result of warmer-than-average temperatures in the Antarctic stratosphere during austral winter between mid-July and early September.

Latent heat flux (LE) is a measure of the water exchange between Earth's surface and atmosphere, also known as evapotranspiration. It is a fundamental component in the Earth's energy budget and hydrological cycle and plays an important role in regulating the weather and climate. Moderate Resolution Imaging Spectroradiometer (MODIS) offers a gap‐filled biophysical product for LE at 8‐day temporal and 500‐meter spatial resolutions. Nonetheless, validation against the in situ eddy covariance measurement reveals significant errors in MODIS LE estimation. Our study integrates ground‐measured, reanalysis and satellite data to predict LE by leveraging the advantage of the data‐driven method. The study draws upon flux data derived from the AsiaFlux database, alongside reanalysis datasets from the Indian Monsoon Data Assimilation and Analysis (IMDAA) and the European Centre for Medium‐Range Weather Forecasts (ERA5) products, as well as biophysical measurements from the MODIS satellite. An analysis of the annual water budget, based on ERA5 precipitation data, highlights net positive water balances across the study sites. By harnessing diverse datasets, we employ various machine learning regression algorithms. We find the support vector regression superior to linear, lasso, random forest, adaptive boosting and gradient boosting algorithms. This study highlights the robustness of support vector regression and accentuates the impact of climatic and environmental conditions on model performance, ultimately contributing to more precise predictions of latent heat flux.

Estimation of the perturbation to the Earth's energy budget by contrail outbreaks is required for estimating the climate impact of aviation and verifying the climate benefits of proposed contrail avoidance strategies such as aircraft rerouting. Here we identified two successive large‐scale contrail outbreaks developing in clear‐sky conditions in geostationary and polar‐orbiting satellite infrared images of Western Europe lasting from 22–23 June 2020. Their hourly cloud radiative effect, obtained using geostationary satellite cloud retrievals and radiative transfer calculations, is negative or weakly positive during daytime and positive during nighttime. The cumulative energy forcing of the two outbreaks is 7 PJ and −8.5 PJ, with uncertainties of 3 PJ, stemming each from approximately 15–20 flights over periods of 19 and 7 hr, respectively. This study suggests that an automated quantification of contrail outbreak radiative effect is possible, at least for contrails forming in clear sky conditions.

Mineral dust is a dominant aerosol type in northwestern China and can mix with other air pollutants during its long‐range transport, playing an important role in Earth's energy budget and hydrological cycle on both regional and global scales. In this study, the optical properties of pure dust and polluted dust were compared by using ground‐based lidar and sun photometer data at SACOL (Semi‐Arid Climate and Environment Observatory of Lanzhou University) from August 2009 to August 2012. The total attenuated backscattering coefficient at 532 nm, the volume depolarization ratio (VDR) and the color ratio (CR) derived from the L2S‐SM‐II dual‐band polarization lidar and aerosol optical depth (AOD) and Angstrom exponent from sun photometer data were used to identify pure dust and polluted dust. The results showed that AOD at 440 nm of pure dust was widely distributed within the range of 0.4–1.1 but the AOD of polluted dust was generally less than 0.6. The frequency distribution of VDR showed that the mean value of pure dust was greater than that of polluted dust and the peak values were 0.31 and 0.21, respectively. The frequency distribution of CR showed that the mean value of pure dust was larger than that of polluted dust, and it was mostly pure dust aerosol when color ratio was greater than 0.8. The VDR showed a positive correlation with CR. Using the frequency distribution of VDR, a threshold value of 0.25 was proposed to differentiate pure dust from polluted dust. The rates of misclassification were 20.7% and 18.6% for pure dust and polluted dust, respectively. This work will be helpful for the classification of aerosol types in satellite observations and numerical modeling.

The elusive nature of the post‐2004 upper ocean warming has exposed uncertainties in the ocean's role in the Earth's energy budget and transient climate sensitivity. Here we present the time evolution of the global ocean heat content for 1958 through 2009 from a new observation‐based reanalysis of the ocean. Volcanic eruptions and El Niño events are identified as sharp cooling events punctuating a long‐term ocean warming trend, while heating continues during the recent upper‐ocean‐warming hiatus, but the heat is absorbed in the deeper ocean. In the last decade, about 30% of the warming has occurred below 700 m, contributing significantly to an acceleration of the warming trend. The warming below 700 m remains even when the Argo observing system is withdrawn although the trends are reduced. Sensitivity experiments illustrate that surface wind variability is largely responsible for the changing ocean heat vertical distribution.

In this presentation, I will outline the fundamentals of climate change science with respect to ‘real-world’ climate observations, and evidence from the Earth's paleoclimate (climate history) record. In particular, I will highlight evidence showing that the Earth's energy budget is no longer in balance (i.e. there is more energy coming into the earth-atmospheric system than going out), and outline the risks of non-linear change in the Earth's climate system – even below the United Nations temperature target to avoid “dangerous anthropogenic (man-made) climate change” - as defined by two degrees centigrade of warming above pre-industrial temperatures. I will be summarize the latest climate data for 2016, and pose the question: Does the COP 21 Paris Agreement go far enough to avoid dangerous climate change?

The daily cloud cycle (DCC) and its response to global warming are critical to the Earth's energy budget, but their radiative effects have not been systematically quantified. Toward this goal, here we analyze the radiation at the top of the atmosphere and propose a measure of the DCC radiative effect (DCCRE) as the difference between the total radiative fluxes with the full cloud cycle and its uniformly distributed cloud counterpart. We apply it to the cloud fraction from four climate models that participated in the second phase of the Cloud Feedback Model Inter-comparison Project. The results allow us to objectively compare inter-model differences in the daily cycle of cloud fraction and their influences on the global energy balance. In particular, the spatial patterns of the DCC response to global warming obtained in our analysis suggest possible impacts on large-scale circulation. Such a framework can be used for conducting a more systematic evaluation of the DCC in climate models and observations with the goal to reduce uncertainty in climate projections.