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Global greenhouse gas forcing and feedbacks are the primary causes of climate change but have limited direct observations. Here we show that continuous, stable, global, hyperspectral infrared satellite measurements (2003–2021) display decreases in outgoing longwave radiation (OLR) in the CO2, CH4, and N2O absorption bands and increases in OLR in the window band and H2O absorption bands. By conducting global line‐by‐line radiative transfer simulations with 2003–2021 meteorological conditions, we show that increases in CO2, CH4, and N2O concentrations caused an instantaneous radiative forcing and stratospheric cooling adjustment that decreased OLR. The climate response, comprising surface and atmospheric feedbacks to radiative forcings and unforced variability, increased OLR. The spectral trends predicted by our climate change experiments using our general circulation model identify three bedrock principles of the physics of climate change in the satellite record: an increasing greenhouse effect, stratospheric cooling, and surface‐tropospheric warming. Plain Language Summary: Greenhouse gas emissions from human activities trap heat (radiative forcing) and cause global warming and climate change. Observing the global radiative forcing caused by well‐mixed greenhouse gases has been elusive because of irregular, uncalibrated, or limited areal measurements. However, the Atmospheric Infrared Sounder (AIRS) instrument on NASA's Aqua Satellite has recently provided the requisite data (2003–2021). These unprecedented observations provide measurements of Earth's emitted thermal heat at fine‐scale wavelengths, that is, the infrared spectrum, allowing us to pinpoint the effect of greenhouse gas concentration increases on Earth's climate. We find large increases in the heat trapped by CO2, CH4, and N2O. Directly monitoring the heat impact of these gases is therefore now possible. In response to this forcing, we found that the planet warmed and emitted extra heat to space via the water vapor absorption sections and transparent sections of the infrared spectrum. Our study therefore provides a strong validation of the physical basis for climate change. Key Points: Direct monitoring of the radiative impact of greenhouse gas changes is now possible through hyperspectral infrared satellite observationsObserved spectral trends include the signatures of an increasing greenhouse effect, stratospheric cooling, and surface‐tropospheric warmingMagnitude of forcing over 2003–2021 found to be consistent with the latest Intergovernmental Panel on Climate Change estimate [ABSTRACT FROM AUTHOR]

Cirrus clouds are highly reflective clouds that play an important role in the atmospheric radiative balance and contribute to climate change. We present the seasonal distribution of cirrus cloud occurrence, characteristics and evolution over a coastal station (Goa, 15.4°N, 73.8°E, 40 m above MSL) in India using ground-based micro pulse lidar and radiosonde observations from July 2013 to June 2015. The frequency of cirrus occurrence was approximately 44%, 33%, 17% and 6% in winter (JF), premonsoon (MAM), southwest monsoon (JJAS) and postmonsoon season (OND), respectively. Thicker cirrus was more frequent in September, with an average value of > 1.7 ± 0.89 km. The frequency of cirrus descent occurs almost similarly in monsoon and postmonsoon (42% and 43% respectively). Two extreme cirrus descending cases were observed during the monsoon: (1) on 22 August 2014 with a displacement of 9.6 km from the initial height of 14 km with a descending rate and geometrical thickness of 0.4 ms−1 and 1.5 km, respectively, and (2) on 14 September 2013 with a displacement of 7.8 km with descending rate and thickness of 0.2 ms−1 and 2.3 km respectively. Relatively high humidity (> 60%) and low (< − 20 °C) ambient temperature caused thick cirrus formation in September 2013. However, during August 2014 event, relatively thinner cirrus was observed, and the same is attributed to lower humidity conditions (45 to 60%). The wind shear associated with the tropical easterly jet (TEJ) plays a major role in cirrus displacement by modifying their vertical velocity. The positive and negative vertical velocity fields cause cirrus to have a descending shape. The gradual descending pattern of cirrus is moving parallel to the vertical velocity contour, while the rapid descending in 2014 was mainly due to the occurrence of cirrus perpendicular to the velocity contour. Overall, this study highlights the descending nature of cirrus clouds over a coastal station in India, which can impact surface radiative characteristics. [ABSTRACT FROM AUTHOR]

Accurate representation of mesoscale scale convective systems (MCSs) in climate models is of vital importance to understanding global energy, water cycles, and extreme weather. In this study, we evaluate the simulated MCS features over the United States from the newly developed GFDL global high-resolution (∼50 km) AM4 model by comparing them with the observations during spring to early summer (April–June) and late summer (July–August). The results show that the spatial distribution and seasonality of occurrence and genesis frequency of MCSs are reasonably simulated over the central United States in both seasons. The model reliably reproduces the observed features of MCS duration, translation speed, and size over the central United States, as well as the favorable large-scale circulation pattern associated with MCS development over the central United States during spring and early summer. However, the model misrepresents the amplitude and the phase of the diurnal cycle of MCSs during both seasons. In addition, the spatial distribution of occurrence and genesis frequency of MCSs over the eastern United States is substantially overestimated, with larger biases in early spring and summer. Furthermore, while large-scale circulation patterns are reasonably simulated in spring and early summer, they are misrepresented in the model during summer. Finally, we examine MCS-related precipitation, finding that the model overestimates MCS-related precipitation during spring and early summer, but this bias is insufficient to explain the significant dry bias observed in total precipitation over the central United States. Nonetheless, the dry biases in MCS-associated precipitation during late summer likely contribute to the overall precipitation deficit in the model. [ABSTRACT FROM AUTHOR]

Satellite observations show a near-zero trend in the top-of-atmosphere global-mean net cloud radiative effect (CRE), suggesting that clouds did not further cool nor heat the planet over the last two decades. The causes of this observed trend are unknown and can range from effective radiative forcing (ERF) to cloud feedbacks, cloud masking, and internal variability. We find that the near-zero NetCRE trend is a result of a significant negative trend in the longwave (LW) CRE and a significant positive trend in the shortwave (SW) CRE, cooling and heating the climate system, respectively. We find that it is exceptionally unlikely (<1% probability) that internal variability can explain the observed LW and SW CRE trends. Instead, the majority of the observed LWCRE trend arises from cloud masking wherein increases in greenhouse gases reduce OLR in all-sky conditions less than in clear-sky conditions. In SWCRE, rapid cloud adjustments to greenhouse gases, aerosols, and natural forcing agents (ERF) explain a majority of the observed trend. Over the northeast Pacific, we show that ERF, hitherto an ignored factor, contributes as much as cloud feedbacks to the observed SWCRE trend. Large contributions from ERF and cloud masking to the global-mean LW and SW CRE trends are supplemented by negative LW and positive SW cloud feedback trends, which are detectable at 80%–95% confidence depending on the observational uncertainty assumed. The large global-mean LW and SW cloud feedbacks cancel, leaving a small net cloud feedback that is unconstrained in sign, implying that clouds could amplify or dampen global warming. [ABSTRACT FROM AUTHOR]

An event‐based assessment of the sea surface temperature (SST) threshold at the genesis of tropical mesoscale convective systems (MCSs) is performed in this study. We show that this threshold (SSTG) has undergone a significant warming trend at a rate of ∼0.2°C per decade. The SSTG shows a remarkable correspondence with the tropical mean SST and upper‐tropospheric temperature on interannual and longer timescales. Using a high‐resolution global climate model that permits realistic simulations of tropical MCSs, we find that the observed features of SSTG are well simulated. Both observation and model simulations demonstrate that the upward tendency in SSTG primarily results from the environmental SST warming over MCS genesis regions rather than the changes in MCS genesis location. A continuous increase in SSTG is projected in a warming simulation, but the relationship between SSTG and upper‐tropospheric temperature remains unchanged, suggesting that the tropical tropospheric temperature generally follows a moist‐adiabatic adjustment. Plain Language Summary: The development of tropical ocean showers and thunderstorms requires a warm ocean surface as an energy source fueling the storm. As a good example, a sea surface temperature (SST) threshold of 26.5°C has long been recognized as a necessary condition for tropical cyclone genesis. Here we examine the SST threshold at the genesis of tropical mesoscale convective systems, which occur so frequently throughout the tropics that they play an important role in shaping the general circulation of the atmosphere as they redistribute moisture and energy in the atmosphere. Using satellite observations and a high‐resolution global climate model, we show that this threshold has undergone a significant warming trend during the past several decades and will continue to increase in the future. This warming trend is closely related to the changes in the tropical mean SST. A constant relationship between the SST threshold and upper‐tropospheric temperature is found in both the current climate and a warming climate, which corroborates the argument that tropospheric temperatures in the tropics approximately follow a moist adiabatic adjustment. Key Points: A significant warming trend in sea surface temperature at the genesis of tropical mesoscale convective systems (MCSs) sea surface temperature threshold (SSTG) has been observedA global climate model that can explicitly simulate MCSs captures the observed features of SSTG and projects a continuous increaseA constant relationship between SSTG and upper‐tropospheric temperature indicates the existence of a moist‐adiabatic adjustment [ABSTRACT FROM AUTHOR]

Syukoro (Suki) Manabe's Nobel Prize in Physics was awarded largely for his early work on one-dimensional models of "radiative–convective equilibrium" (RCE), which produced the first credible estimates of Earth's climate sensitivity. This article reviews that work and tries to identify those aspects that make it so distinctive. We argue that Manabe's model of RCE contained three crucial ingredients. These are (i) a tight convective coupling of the surface to the troposphere, (ii) an assumption of fixed relative humidity rather than fixed absolute humidity, and (iii) a sufficiently realistic representation of greenhouse gas radiative transfer. Previous studies had separately identified these key ingredients, but none had properly combined them. We then discuss each of these ingredients in turn, highlighting how subsequent research in the intervening decades has only cemented their importance for understanding global climate change. We close by reflecting on the elegance of Manabe's approach and its lasting value. [ABSTRACT FROM AUTHOR]

Atmospheric mineral dust is derived from arid/semi-arid region, undergoes long-range transport, and can impact surface biogeochemical processes in the pristine marine region. This study reports fluxes of mineral dust (wet and dry deposition), their clay mineralogy, and associated seasonal variability over the Northeastern Arabian Sea. Dust sources are assessed using mineral composition and their ratios as well as satellite retrievals during 2012–2015. The average wet deposition flux (293 mg m−2 day−1) of mineral dust is three times higher than dry deposition flux (96 mg m−2 day−1). A clear distinction is observed in the mineral composition of wet and dry deposited dust with smectite and palygorskite being dominant during the wet period and illite and chlorite being dominant during the dry period. Mineralogical composition and their ratios indicate that major dust sources to the Northeastern Arabian Sea are from northeast Africa, Arabian Peninsula, and southwest Asia. These observations are corroborated with the satellite retrievals, aerosol optical depth, and back trajectory analyses. [ABSTRACT FROM AUTHOR]

The selection of text features is a fundamental task and plays an important role in digital document analysis. Conventional methods in text feature extraction necessitate indigenous features. Obtaining an efficient feature is an extensive process, but a new and real-time representation of features in text data is a challenging task. Deep learning is making inroads in digital document mining. A significant distinction between deep learning and traditional methods is that deep learning learns features in a digital document in an automatic manner. In this paper, logistic regression and deep dependency parsing (LR-DDP) methods are proposed. The logistic regression token generation model generates robust tokens by means of Napierian grammar. With the robust generated tokens, a deep transition-based dependency parsing using duplex long short-term memory is designed. Experimental results demonstrate that our dependency parser achieves comparable performance in terms of digital document parsing accuracy, parsing time and overhead when compared to existing methods. Hence, these methods are found to be computationally efficient and accurate. [ABSTRACT FROM AUTHOR]

The observed trend in Earth's energy imbalance (TEEI), a measure of the acceleration of heat uptake by the planet, is a fundamental indicator of perturbations to climate. Satellite observations (2001–2020) reveal a significant positive globally-averaged TEEI of 0.38 ± 0.24 Wm−2decade−1, but the contributing drivers have yet to be understood. Using climate model simulations, we show that it is exceptionally unlikely (<1% probability) that this trend can be explained by internal variability. Instead, TEEI is achieved only upon accounting for the increase in anthropogenic radiative forcing and the associated climate response. TEEI is driven by a large decrease in reflected solar radiation and a small increase in emitted infrared radiation. This is because recent changes in forcing and feedbacks are additive in the solar spectrum, while being nearly offset by each other in the infrared. We conclude that the satellite record provides clear evidence of a human-influenced climate system. Satellite observations reveal a significant positive trend in Earth's energy imbalance, but the contributing drivers have yet to be understood. Here, the authors show that it is exceptionally unlikely that this trend can be explained by internal variability; instead, anthropogenic forcing and feedbacks cause the trend. [ABSTRACT FROM AUTHOR]

The characteristics of tropical mesoscale convective systems (MCSs) simulated with a finer-resolution (~50 km) version of the Geophysical Fluid Dynamics Laboratory (GFDL) AM4 model are evaluated by comparing with a comprehensive long-term observational dataset. It is shown that the model can capture the various aspects of MCSs reasonably well. The simulated spatial distribution of MCSs is broadly in agreement with the observations. This is also true for seasonality and interannual variability over different land and oceanic regions. The simulated MCSs are generally longer-lived, weaker, and larger than observed. Despite these biases, an event-scale analysis suggests that their duration, intensity, and size are strongly correlated. Specifically, longer-lived and stronger events tend to be bigger, which is consistent with the observations. The same model is used to investigate the response of tropical MCSs to global warming using time-slice simulations forced by prescribed sea surface temperatures and sea ice. There is an overall decrease in occurrence frequency, and the reduction over land is more prominent than over ocean. [ABSTRACT FROM AUTHOR]

Because the forcings to which Coupled Model Intercomparison Project ‐ Phase 5 (CMIP5) models were subjected were poorly quantified, recent efforts from the Radiative Forcing Model Intercomparison Project (RFMIP) have focused on developing and testing models with exacting benchmarks. Here, we focus on aerosol forcing to understand if for a given distribution of aerosols, participating models are producing a radiometrically‐accurate forcing. We apply the RFMIP experimental protocol for assessing flux biases in aerosol instantaneous radiative effect (IRE) on two participating models, GFDL AM4 and CESM 1.2.2. The latter model contains the RRTMG radiation code which is widely used among CMIP6 GCM's. We conduct a series of calculations that test different potential sources of error in these models relative to line‐by‐line benchmarks. We find two primary sources of error: two‐stream solution methods and the techniques to resolve spectral dependencies of absorption and scattering across the solar spectrum. The former is the dominant source of error for both models but the latter is more significant as a contributing factor for CESM 1.2.2. Either source of error can be addressed in future model development, and these results both demonstrate how the RFMIP protocol can help determine the origins of parameterized errors relative to their equivalent benchmark calculations for participating models, as well as highlight a viable path towards a more rigorous quantification and control of forcings for future CMIP exercises. Key Points: Using radiative forcing model intercomparison project (RFMIP's) protocol to assess biases in aerosol instantaneous radiative effect (IRE), contrasting sources of error found between the GFDL AM4 and RRTMG CESM 1.2.2 modelsTwo sources of error in modeled IRE relative to benchmarks are: scattering methods and spectral resolution of shortwave parameterizationsResults demonstrate how the RFMIP protocol can help determine the origins of model biases in aerosol IRE for other participating models [ABSTRACT FROM AUTHOR]

The Coronavirus Disease 2019 (COVID‐19) pandemic led to a widespread reduction in aerosol emissions. Using satellite observations and climate model simulations, we study the underlying mechanisms of the large decreases in solar clear‐sky reflection (3.8 W m−2 or 7%) and aerosol optical depth (0.16 W m−2 or 32%) observed over the East Asian Marginal Seas in March 2020. By separating the impacts from meteorology and emissions in the model simulations, we find that about one‐third of the clear‐sky anomalies can be attributed to pandemic‐related emission reductions, and the rest to weather variability and long‐term emission trends. The model is skillful at reproducing the observed interannual variations in solar all‐sky reflection, but no COVID‐19 signal is discerned. The current observational and modeling capabilities will be critical for monitoring, understanding, and predicting the radiative forcing and climate impacts of the ongoing crisis. Plain Language Summary: Satellite data showed large reductions in reflected sunlight and aerosol optical depth over clear (cloudless) sky off the East Asian coast in March 2020. Although these changes are consistent with a sharp cut in aerosol emissions due to the lockdown put in place to curb the spread of Coronavirus Disease 2019 (COVID‐19), one cannot rule out possible roles played by weather conditions such as winds and humidity. We use a climate model forced with past known weather to isolate the latter factor, and to describe the difference from the observation to the former. The main finding is that the pandemic‐related emission reductions are responsible for about one‐third of the observed signal. The model can largely reproduce the year‐to‐year variations in all‐sky reflection, but no influence of COVID‐19 is detected. Key Points: Solar clear‐sky reflection was observed to drop substantially over the East Asian Marginal Seas in March 2020Climate model simulations nudged with reanalysis data are used to separate the impacts of meteorology and emissionsIt is found that about one‐third of the clear‐sky anomalies can be attributed to pandemic‐related emission reductions [ABSTRACT FROM AUTHOR]

Monsoon low pressure systems (MLPSs) are among the most important synoptic-scale disturbances of the South Asian summer monsoon. Potential changes in their characteristics in a warmer climate would have broad societal impacts. Yet, the findings from a few existing studies are inconclusive. We use the Geophysical Fluid Dynamics Laboratory (GFDL) coupled climate model CM4.0 to examine the projected changes in the simulated MLPS activity under a future emission scenario. It is shown that CM4.0 can skillfully simulate the number, genesis location, intensity, and lifetime of MLPSs. Global warming gives rise to a significant decrease in MLPS activity. An analysis of several large-scale environmental variables, both dynamic and thermodynamic, suggests that the decrease in MLPS activity can be attributed mainly to a reduction in low-level relative vorticity over the core genesis region. The decreased vorticity is consistent with weaker large-scale ascent, which leads to less vorticity production through the stretching term in the vorticity equation. Assuming a fixed radius of influence, the projected reduction in MLPSs would significantly lower the associated precipitation over north-central India, despite an overall increase inmean precipitation. [ABSTRACT FROM AUTHOR]

Purpose: Metabolic alterations underlie many pathophysiological conditions, and their understanding is critical for the development of novel therapies. Although the assessment of metabolic changes in vivo has been historically challenging, recent developments in molecular imaging have allowed us to study novel metabolic research concepts directly in the living subject, bringing us closer to patients. However, in many instances, there is need for sensors that are in close proximity to the organ under investigation, for example to study vascular metabolism. Methods: In this study, we developed and validated a metabolic detection platform directly in the living subject under an inflammatory condition. The signal collected by a scintillating fiber is amplified using a photomultiplier tube and decodified by an in-house tunable analysis platform. For in vivo testing, we based our experiments on the metabolic characteristics of macrophages, cells closely linked to inflammation and avid for glucose and its analog 18F-fluorodeoxyglucose (18F-FDG). The sensor was validated in New Zealand rabbits, in which inflammation was induced by either a) high cholesterol (HC) diet for 16 weeks or b) vascular balloon endothelial denudation followed by HC diet. Results: There was no difference in weight, hemodynamics, blood pressure, or heart rate between the groups. Vascular inflammation was detected by the metabolic sensor (Inflammation: 0.60 ± 0.03 AU vs. control: 0.48 ± 0.03 AU, p = 0.01), even though no significant inflammation/atherosclerosis was detected by intravascular ultrasound, underscoring the high sensitivity of the system. These findings were confirmed by the presence of macrophages on ex vivo aortic tissue staining. Conclusion: In this study, we validated a tunable very sensitive metabolic sensor platform that can be used for the detection of vascular metabolism, such as inflammation. This sensor can be used not only for the detection of macrophage activity but, with alternative probes, it could allow the detection of other pathophysiological processes. [ABSTRACT FROM AUTHOR]

Water supply reservoir management is based on long-term management policies which depend on customer demands and seasonal hydrologic changes. However, increasing frequency and intensity of precipitation events is necessitating the short-term management of such reservoirs to reduce downstream flooding. Operational management of reservoirs at hourly/daily timescales is challenging due to the uncertainty associated with the inflow forecasts and the volumes in the reservoir. We present an ensemble-based streamflow prediction and optimization framework consisting of a regional scale hydrologic model forced with ensemble precipitation inputs to obtain probabilistic inflows to the reservoir. A multi-objective dynamic programming model was used to obtain optimized release strategies accounting for the inflow uncertainties. The proposed framework was evaluated at a water supply reservoir in the Hackensack River basin in New Jersey during Hurricanes Irene and Sandy. Hurricane Irene resulted in the overtopping of the dam despite releases made in anticipation of the event and resulted in severe downstream flooding. Hurricane Sandy was characterized by low rainfall, however, raised significant concerns of flooding given the nature of the event. The improvement in NSE for the Hurricane Irene inflows from 0.5 to 0.76 and reduction of the spread of PBIAS with decreasing lead times resulted in improvements in the forecast informed releases. This study provides perspectives on the benefits of the proposed forecasting and optimization framework in reducing the decision making burden on the operator by providing the uncertainties associated with the inflows, releases and the water levels in the reservoir. [ABSTRACT FROM AUTHOR]

The clear sky greenhouse effect (G) is defined as the trapping of infrared radiation by the atmosphere in the absence of clouds. The magnitude and variability of G is an important element in the understanding of Earth's energy balance; yet the quantification of the governing factors of G is poor. The global mean G averaged over 2000 to 2016 is 130–133 W m−2 across data sets. We use satellite observations from Clouds and the Earth's Radiant Energy System Energy Balance and Filled (CERES EBAF) to calculate the monthly anomalies in the clear sky greenhouse effect (ΔG). We quantify the contributions to ΔG due to changes in surface temperature, atmospheric temperature, and water vapor by performing partial radiation perturbation experiments using ERA‐Interim and Geophysical Fluid Dynamics Laboratory's Atmospheric Model 4.0 climatological data. Water vapor in the middle troposphere and upper troposphere is found to contribute equally to the global mean and tropical mean ΔG. Holding relative humidity (RH) fixed in the radiative transfer calculations captures the temporal variability of global mean ΔG while variations in RH control the regional ΔG signal. The variations in RH are found to help generate the clear sky super greenhouse effect (SGE). Thirty‐six percent of Earth's area exhibits SGE, and this disproportionately contributes to 70% of the globally averaged magnitude of ΔG. In the global mean, G's sensitivity to surface temperature is 3.1–4.0 W m−2 K−1, and the clear sky longwave feedback parameter is 1.5–2.0 W m−2 K−1. Observations from CERES EBAF lie at the more sensitive ends of these ranges and the spread arises from its cloud removal treatment, suggesting that it is difficult to constrain clear sky feedbacks. Key Points: Global mean clear sky greenhouse effect (G) over 2000–2016 is 130–133 W m−2 and its sensitivity to surface temperature is 3.1–4.0 W m−2 K−1Middle and upper tropospheric water vapor contribute equally to anomalies in global G, while regionally midtroposphere dominatesVariations in relative humidity are crucial in explaining regional anomalies in G and help generate the clear sky super greenhouse effect [ABSTRACT FROM AUTHOR]

The 2018 tropical cyclone (TC) season in the North Pacific was very active, with 39 tropical storms including eight typhoons/hurricanes. This activity was successfully predicted up to 5 months in advance by the Geophysical Fluid Dynamics Laboratory Forecast‐Oriented Low Ocean Resolution (FLOR) global coupled model. In this work, a suite of idealized experiments with three dynamical global models (FLOR, Nonhydrostatic Icosahedral Atmospheric Model, and Meteorological Research Institute Atmospheric General Circulation Model) was used to show that the active 2018 TC season was primarily caused by warming in the subtropical Pacific and secondarily by warming in the tropical Pacific. Furthermore, the potential effect of anthropogenic forcing on the active 2018 TC season was investigated using two of the global models (FLOR and Meteorological Research Institute Atmospheric General Circulation Model). The models projected opposite signs for the changes in TC frequency in the North Pacific by an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty and model dependence in the possible impact of anthropogenic forcing on Pacific TC activity. Plain Language Summary: The potential causes of the active 2018 tropical cyclone season in the North Pacific were explored with a series of high‐resolution climate model experiments, revealing that this active cyclone season was mainly induced by subtropical Pacific warming and secondarily by tropical Pacific warming. The possible effect of anthropogenic forcing on the active cyclone season was also investigated. However, diverse results were obtained regarding projected changes in storm frequency in the case of an increase in anthropogenic forcing, thereby highlighting the substantial uncertainty in the possible impact of anthropogenic forcing on the 2018 active cyclone season. Key Points: The 2018 active tropical cyclone season in the North Pacific was successfully predicted 5 months in advanceThe subtropical Pacific warming in 2018 mainly caused the active storm season in the North PacificThe potential effect of anthropogenic forcing on the 2018 active storm season is uncertain [ABSTRACT FROM AUTHOR]

The objective of this work was to evaluate the benefits of using multi-model meteorological ensembles in representing the uncertainty of hydrologic forecasts. An inter-comparison experiment was performed using meteorological inputs from different models corresponding to Hurricane Irene (2011), over three sub-basins of the Hudson River basin. The ensemble-based precipitation inputs were used as forcing in a hydrological model to retrospectively forecast hourly streamflow, with a 96-hour lead time. The inputs consisted of 73 ensemble members, namely one high-resolution ECMWF deterministic member, 51 ECMWF members and 21 NOAA/ESRL (GEFS Reforecasts v2) members. The precipitation inputs were resampled to a common grid using the bilinear resampling method that was selected upon analysing different resampling methods. The results show the advantages of forcing hydrologic forecasting systems with multi-model ensemble forecasts over using deterministic and single model ensemble forecasts. The work showed that using the median of all 73 ensemble streamflow forecasts relatively improved the Nash-Sutcliffe Efficiency and lowered the biases across the examined sub-basins, compared with using the ensemble median from an individual model. This research contributes to the growing literature that demonstrates the promising capabilities of multi-model systems to better describe the uncertainty in streamflow predictions. [ABSTRACT FROM AUTHOR]

Abstract: Climate observations are needed to address a large range of important societal issues including sea level rise, droughts, floods, extreme heat events, food security, and freshwater availability in the coming decades. Past, targeted investments in specific climate questions have resulted in tremendous improvements in issues important to human health, security, and infrastructure. However, the current climate observing system was not planned in a comprehensive, focused manner required to adequately address the full range of climate needs. A potential approach to planning the observing system of the future is presented in this article. First, this article proposes that priority be given to the most critical needs as identified within the World Climate Research Program as Grand Challenges. These currently include seven important topics: melting ice and global consequences; clouds, circulation and climate sensitivity; carbon feedbacks in the climate system; understanding and predicting weather and climate extremes; water for the food baskets of the world; regional sea‐level change and coastal impacts; and near‐term climate prediction. For each Grand Challenge, observations are needed for long‐term monitoring, process studies and forecasting capabilities. Second, objective evaluations of proposed observing systems, including satellites, ground‐based and in situ observations as well as potentially new, unidentified observational approaches, can quantify the ability to address these climate priorities. And third, investments in effective climate observations will be economically important as they will offer a magnified return on investment that justifies a far greater development of observations to serve society's needs. [ABSTRACT FROM AUTHOR]

A new paradigm in benchmark absorption-scattering radiative transfer is presented that enables both the globally averaged and spatially resolved testing of climate model radiation parameterizations in order to uncover persistent sources of biases in the aerosol instantaneous radiative effect (IRE). A proof of concept is demonstrated with the Geophysical Fluid Dynamics Laboratory AM4 and Community Earth System Model 1.2.2 climate models. Instead of prescribing atmospheric conditions and aerosols, as in prior intercomparisons, native snapshots of the atmospheric state and aerosol optical properties from the participating models are used as inputs to an accurate radiation solver to uncover model-relevant biases. These diagnostic results show that the models' aerosol IRE bias is of the same magnitude as the persistent range cited (~1 W/m2) and also varies spatially and with intrinsic aerosol optical properties. The findings underscore the significance of native model error analysis and its dispositive ability to diagnose global biases, confirming its fundamental value for the Radiative Forcing Model Intercomparison Project. [ABSTRACT FROM AUTHOR]

East Asia has some of the largest concentrations of absorbing aerosols globally, and these, along with the region's scattering aerosols, have both reduced the amount of solar radiation reaching Earth's surface regionally (solar dimming) and increased shortwave absorption within the atmosphere, particularly during the peak months of the East Asian summer monsoon (EASM). This study analyzes how atmospheric absorption and surface solar dimming compete in driving the response of regional summertime climate to anthropogenic aerosols, which dominates, and why-issues of particular importance for predicting how East Asian climate will respond to projected changes in absorbing and scattering aerosol emissions in the future. These questions are probed in a state-of-the-art general circulation model using a combination of realistic and novel idealized aerosol perturbations that allow analysis of the relative influence of absorbing aerosols' atmospheric and surface-driven impacts on regional circulation and climate. Results show that even purely absorption-driven dimming decreases EASM precipitation by cooling the land surface, counteracting climatological land-sea contrast and reducing ascending atmospheric motion and onshore winds, despite the associated positive top-of-the-atmosphere regional radiative forcing. Absorption-driven atmospheric heating does partially offset the precipitation and surface evaporation reduction from surface dimming, but the overall response to aerosol absorption more closely resembles the response to its surface dimming than to its atmospheric heating. These findings provide a novel decomposition of absorbing aerosol's impacts on regional climate and demonstrate that the response cannot be expected to follow the sign of absorption's top-of-the-atmosphere or even atmospheric radiative perturbation. [ABSTRACT FROM AUTHOR]

The aim of this study was to assess the culture and climate for diversity and inclusion and the humanistic learning environment for students, faculty, and staff at the University of Michigan School of Dentistry. From July 2014 to June 2015, two committees of 16 faculty members, staff members, and students, in partnership with trained program evaluators, used a participatory program evaluation (PPE) process to conduct the assessment using key informant interviews, surveys, and focus groups. The topics addressed were humanistic environment, learning environment, diversity and inclusion, microaggressions and bullying, and activities and space. All staff members, all faculty members (both full- and part-time), and all students in all four years were invited to participate in the parallel but distinctive versions of the survey from November 10 to 25, 2014. Response rates for each group were as follows: 50% (318/642) for students, 68% (217/320) for staff, and 40% (147/366) for faculty; numbers responding to individual items varied. Among the respondents, the majority (76% faculty, 67% staff, 80% students) agreed that the environment fostered learning and personal growth and that a humanistic environment was important (97% faculty, 95% staff, 94% students). Many reported having experienced/witnessed a micro-aggression or bullying. Many also reported having "ever had" dissatisfaction with the learning environment (44% faculty, 39% staff, 68% students). The students sought better relationships with the faculty; the staff and faculty members sought opportunities for professional development and mentoring. Recommendations included cultural sensitivity training, courses for interpersonal skills, leadership and team-building efforts, addressing microaggressions and bullying, creating opportunities for collaboration, and increasing diversity of faculty, staff, and students. These recommendations were incorporated into the school's strategic plan. In this study, a utilization-focused PPE process using mixed methods was effective for evaluating the dental school's climate for diversity and inclusion, as well as the learning environment for faculty, staff, and students. [ABSTRACT FROM AUTHOR]

Global-mean radiances observed by the Atmospheric Infrared Sounder (AIRS) and the Advanced Microwave Sounding Unit A (AMSU-A) are analyzed from 2003 to 2012. The focus of this study is on channels sensitive to emission and absorption in the stratosphere. Optimal fingerprinting is used to obtain estimates of changes of stratospheric temperature in five vertical layers due to external forcing in the presence of natural variability. Natural variability is estimated using synthetic radiances based on the 500-yr GFDL CM3 and 240-yr HadGEM2-CC control runs. The results show a cooling rate of 0.65 ± 0.11 (2 σ) K decade−1 in the upper stratosphere above 6 hPa, approximately 0.46 ± 0.24 K decade−1 in two midstratospheric layers between 6 and 30 hPa, and 0.39 ± 0.32 K decade−1 in the lower stratosphere (30-60 hPa). The cooling rate in the lowest part of the stratosphere (60-100 hPa) is −0.014 ± 0.22 K decade−1, which is smallest among all five layers and statistically insignificant. The synergistic use of well-calibrated passive infrared and microwave radiances permits disambiguation of trends of carbon dioxide and stratospheric temperature, increases vertical resolution of detected stratospheric temperature trends, and effectively reduces uncertainties of estimated temperature trends. [ABSTRACT FROM AUTHOR]

Arctic haze has a distinct seasonal cycle with peak concentrations in winter but pristine conditions in summer. It is demonstrated that the Geophysical Fluid Dynamics Laboratory (GFDL) atmospheric general circulation model (AM3) can reproduce the observed seasonality of Arctic black carbon (BC), an important component of Arctic haze. The model is used to study how large-scale circulation and removal drive the seasonal cycle of Arctic BC. It is found that despite large seasonal shifts in the general circulation pattern, the transport of BC into the Arctic varies little throughout the year. The seasonal cycle of Arctic BC is attributed mostly to variations in the controlling factors of wet removal, namely the hydrophilic fraction of BC and wet deposition efficiency of hydrophilic BC. Specifically, a confluence of low hydrophilic fraction and weak wet deposition, owing to slower aging process and less efficient mixed-phase cloud scavenging, respectively, is responsible for the wintertime peak of BC. The transition to low BC in summer is the consequence of a gradual increase in the wet deposition efficiency, whereas the increase of BC in late fall can be explained by a sharp decrease in the hydrophilic fraction. The results presented here suggest that future changes in the aging and wet deposition processes can potentially alter the concentrations of Arctic aerosols and their climate effects. [ABSTRACT FROM AUTHOR]

This paper investigates changes in the tropical tropopause layer (TTL) in response to carbon dioxide increase and surface warming separately in an atmospheric general circulation model, finding that both effects lead to a warmer tropical tropopause. Surface warming also results in an upward shift of the tropopause. A detailed heat budget analysis is performed to quantify the contributions from different radiative and dynamic processes to changes in the TTL temperature. When carbon dioxide increases with fixed surface temperature, a warmer TTL mainly results from the direct radiative effect of carbon dioxide increase. With surface warming, the largest contribution to the TTL warming comes from the radiative effect of the warmer troposphere, which is partly canceled by the radiative effect of the moistening at the TTL. Strengthening of the stratospheric circulation following surface warming cools the lower stratosphere dynamically and radiatively via changes in ozone. These two effects are of comparable magnitudes. This circulation change is the main cause of temperature changes near 63 hPa but is weak near 100 hPa. Contributions from changes in convection and clouds are also quantified. These results illustrate the heat budget analysis as a useful tool to disentangle the radiative-dynamical-chemical-convective coupling at the TTL and to facilitate an understanding of intermodel difference. [ABSTRACT FROM AUTHOR]

This paper investigates the uncertainties in hourly streamflow ensemble forecasts for an extreme hydrological event using a hydrological model forced with short-range ensemble weather prediction models. A state-of-the art, automated, short-term hydrologic prediction framework was implemented using GIS and a regional scale hydrological model (HEC-HMS). The hydrologic framework was applied to the Hudson River Basin, USA (~ 36,000 km²) using gridded precipitation data from the National Centers for Environmental Prediction (NCEP) North American Regional Reanalysis (NARR) and was validated against streamflow observations from the United States Geologic Survey (USGS). Finally, 21 precipitation datasets reforecast by the 21 ensemble members of the latest Global Ensemble Forecast System (GEFS/R) were used to generate a retrospective streamflow ensemble forecast for an extreme hydrological event, Hurricane Irene. The work shows that ensemble stream discharge forecasts provide improved predictions and useful information about associated uncertainties, thus improving the assessment of risks when compared with deterministic forecasts. The uncertainties in weather inputs may result in false warnings and missed river flooding events, reducing the potential to effectively mitigate flood damage. The findings demonstrate how errors in the ensemble median streamflow forecast and time of peak, as well as the ensemble spread (uncertainty) are reduced 48 hours pre-event utilizing the ensemble framework. The methodology and implications of this work benefit efforts of short-term streamflow forecasts at regional scales, notably regarding the peak timing of an extreme hydrologic event when combined with a flood threshold exceedance diagram. Although the modelling framework was implemented on the Hudson River basin, it is flexible and re-locatable in other parts of the world. [ABSTRACT FROM AUTHOR]

Uncertainty in equilibrium climate sensitivity impedes accurate climate projections. While the intermodel spread is known to arise primarily from differences in cloud feedback, the exact processes responsible for the spread remain unclear. To help identify some key sources of uncertainty, the authors use a developmental version of the next-generation Geophysical Fluid Dynamics Laboratory global climate model (GCM) to construct a tightly controlled set of GCMs where only the formulation of convective precipitation is changed. The different models provide simulation of present-day climatology of comparable quality compared to the model ensemble from phase 5 of CMIP (CMIP5). The authors demonstrate that model estimates of climate sensitivity can be strongly affected by the manner through which cumulus cloud condensate is converted into precipitation in a model's convection parameterization, processes that are only crudely accounted for in GCMs. In particular, two commonly used methods for converting cumulus condensate into precipitation can lead to drastically different climate sensitivity, as estimated here with an atmosphere-land model by increasing sea surface temperatures uniformly and examining the response in the top-of-atmosphere energy balance. The effect can be quantified through a bulk convective detrainment efficiency, which measures the ability of cumulus convection to generate condensate per unit precipitation. The model differences, dominated by shortwave feedbacks, come from broad regimes ranging from large-scale ascent to subsidence regions. Given current uncertainties in representing convective precipitation microphysics and the current inability to find a clear observational constraint that favors one version of the authors' model over the others, the implications of this ability to engineer climate sensitivity need to be considered when estimating the uncertainty in climate projections. [ABSTRACT FROM AUTHOR]