Uncertainty In Climate Predictions Research Articles

Long-term relationships between summer clouds and aerosols over mid-high latitudes of the Northern Hemisphere

While the short-term relationship between clouds and aerosols is well known, no adequate data is available to verify the longer-term, annual to decadal, relationship. It is important to quantify the aerosol–cloud interaction (ACI) for mitigating uncertainty in climate prediction. Here the long-term ACI over the mid-to-high latitudes of the Northern Hemisphere was analyzed by using seasonally-resolved ion fluxes reconstructed from a southeastern Greenland ice core (SE-Dome ice core) as aerosol proxies, and satellite-based summer cloud amount between 1982 and 2014. As a result, SO42− flux in the ice core shows significant positive correlation with total cloud amounts (CCT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CC}_{T}$$\\end{document}) and cloud droplet concentration (Nd\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${N}_{d}$$\\end{document}) in the summer over the southeastern Greenland Sea, implying that the sulfate aerosols may contribute to the variability of CCT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CC}_{T}$$\\end{document} via microphysical cloud processes. Significant positive correlations are persistent even under the constrained conditions when cloud formation factors such as relative humidity, air temperature at cloud height, and summer North Atlantic Oscillation are limited within ± 1σ variability. Hence sulfate aerosols should control the interannual variability of summer CCT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CC}_{T}$$\\end{document} In terms of decadal changes, CCT\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${CC}_{T}$$\\end{document} was approximately 3–5% higher in the 1960s–1970s than in the 1990s–2000s, which can be explained by changes in the, SO42-\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$${{{\ ext{SO}}}_{4}}^{2-}$$\\end{document} flux preserved in the SE-Dome ice core.

Comparison of Coupled Model Intercomparison Project Phases 5 and 6 in Simulating Diurnal Cloud Cycle

Cloud dynamics and their response to future climate change continue to present a significant source of uncertainty in climate predictions. Besides the average cloud properties, the diurnal cloud cycle (DCC) exerts a substantial influence on Earth’s energy balance by reflecting solar radiation during the daytime and continuously absorbing and reemitting longwave radiation throughout the whole day. Previous studies have demonstrated that climate models exhibit certain discrepancies in simulating the DCC; however, less research attention has been paid to the patterns of these DCC biases and their impacts on modeling the Earth’s energy balance. Here, we employ satellite data to compare DCC patterns in Coupled Model Intercomparison Project Phase 5 (CMIP5) and their latest versions in CMIP6 at both regional and global scales. We found that some of the latest climate models tend to have larger DCC biases when using satellite observations as the references, and the radiative effects due to DCC changes account for nearly 50% of the changes in total cloud radiative effects (CREs), suggesting that the DCC biases play a significant role in modelingthe global energy budget. We therefore call for improving cloud parameterization schemes with particular attention to their diurnal cycle to reduce their impacts on future climate projections.

Aqueous-phase chemistry of glyoxal with multifunctional reduced nitrogen compounds: a potential missing route for secondary brown carbon

Abstract. The aqueous-phase chemistry of glyoxal (GL) with reduced nitrogen compounds (RNCs) is a significant source for secondary brown carbon (SBrC), which is one of the largest uncertainties in climate predictions. However, a few studies have revealed that SBrC formation is affected by multifunctional RNCs, which have a non-negligible atmospheric abundance. Hence, we assessed theoretical and experimental approaches to investigate the reaction mechanisms and kinetics of the mixtures for ammonium sulfate (AS), multifunctional amine monoethanolamine (MEA), and GL. Our experiments indicate that light absorption and growth rate are enhanced more efficiently in the MEA–GL mixture relative to AS–GL and MEA–AS–GL mixtures and MEA reactions of the chromophores than in the analogous AS reactions. Quantum chemical calculations show that the formation and propagation of oligomers proceed via four-step nucleophilic addition reactions in three reaction systems. The presence of MEA provides the two extra branched chains that affect the natural charges and steric hindrance of intermediates, facilitating the formation of chromophores. Molecule dynamics simulations reveal that the interfacial and interior attraction on the aqueous aerosols with MEA is more pronounced for small α-dicarbonyls to facilitate further engagement in the aqueous-phase reactions. Our results show a possible missing source for SBrC formation on urban, regional, and global scales.

Examining Antarctic sea ice bias sensitivity in the multi-variate parameter space using a global coupled climate modelling system

Coupled global numerical climate models (GCMs) typically underestimate mean Antarctic sea ice area and extent, particularly during the austral summer months, contributing to uncertainties in climate prediction. This study examines the climatological behaviour of Antarctic sea ice in a coupled GCM in the multivariate sea ice model parameter space. Individual parameters dominate the ice response in different seasons and regions, with a compensatory effect in some parameter combinations and an amplified effect in others; however, certain parameter combinations are found to improve aspects of Antarctic sea ice climatology well beyond the limitations of a univariate approach. For example, the disparity between observed and simulated summer sea ice extent and area is halved, and the tendency towards very low-concentration ice (<15 %) reduced in favour of a more compact summer and autumn ice pack. Regardless, clear limitations in the extent to which a coupled GCM can be calibrated with sea-ice model parameters also emerge. Relatively unconsolidated winter ice cover persists and, in some experiments, becomes looser still, exacerbating the already overestimated maximum sea ice extent. Furthermore, the seasonal evolution of sea ice and the exaggerated asymmetry of the seasonal cycle, with the onset of ice advance too slow and maximum sea ice reached too late, sees negligible improvements. We note that, even with the large gains under certain parameter combinations, bias and other deficiencies still remain. Using coupled data assimilation to optimise parameters in both sea-ice and ocean models will likely assist in contributing to further model improvements.

Assessment of the potential impacts of climate changes on Syr Darya watershed: A hybrid ensemble analysis method

Study regionSyr Darya watershed, Central Asia. Study focusClimate change has the potential to significantly impact the precipitation patterns, available water resources, food security, and ecosystem balance of a watershed. However, limited understanding exists of the temperature, precipitation, and streamflow trends in the Syr Darya watershed under the influence of climate change due to the uncertainties associated with climate change and the incompleteness of observational data. In this study, a multi-GCMs based statistical ensemble analysis (MGSEA) method is developed to effectively reflect the uncertainty of climate predictions and comprehensively assess the impact of climate change on the Syr Darya watershed during the period from 2021 to 2100. New hydrological insights for the region: The variations in temperature, precipitation, and streamflow were assessed by analyzing the projection results of various scenario combinations. Compared to the baseline (1960–2005), the findings reveal a rise in the annual average temperature ranging from 0.2 to 3.8 ℃ for RCP 4.5 and from 1.4 to 5.5 ℃ for RCP 8.5 in the 2080s. Additionally, the research confirms a downward trend in annual precipitation, with decreases of 3.7–27.8% for RCP 4.5 and 5.1–47.7% for RCP 8.5. The results of streamflow analysis exhibit an increasing trend during winter and autumn and a decreasing trend during summer and spring. The research outcomes obtained from MGSEA can be utilized for supporting water resources planning and management.

Chemical precursors of new particle formation in coastal New Zealand

Abstract. To reduce uncertainties in climate predictions, we need to better understand aerosol formation in different environments. An important part of this is studying which chemical species are responsible for particle formation. While many advances have been made in this field, measurements are especially lacking from marine environments. Here, we measured the chemical composition of ambient anions over 7 months at Baring Head station, located in coastal New Zealand. This adds to our previous work which reported the aerosol size distribution measurements and investigated new particle formation and environmental conditions favouring new particle formation at the station. By combining the information on ion chemical composition with our previous work, we were able to study the chemical precursors of new particle formation. Our results showed that while over land new particle formation is likely driven by sulfuric acid and organic species, in clean marine air iodine oxoacids and sulfur species are likely important drivers of particle formation processes. These data were also used to characterise the diurnal and seasonal cycles of the most important anion groups and their geographical source regions. Bisulfate anions displayed a clear daytime maximum where as iodine oxoacids had morning and evening maximums. Highly oxygenated organic molecules, on the other hand, were most abundant during the night when the air was land-influenced. This data set is highly valuable and our results provide important information on the chemical species driving new particle formation at a remote Southern Hemisphere coastal site.

The Stochastic Ice-Sheet and Sea-Level System Model v1.0 (StISSM v1.0)

Abstract. We introduce the first version of the Stochastic Ice-sheet and Sea-level System Model (StISSM v1.0), which adds stochastic parameterizations within a state-of-the-art large-scale ice sheet model. In StISSM v1.0, stochastic parameterizations target climatic fields with internal variability, as well as glaciological processes exhibiting variability that cannot be resolved at the spatiotemporal resolution of ice sheet models: calving and subglacial hydrology. Because both climate and unresolved glaciological processes include internal variability, stochastic parameterizations allow StISSM v1.0 to account for the impacts of their high-frequency variability on ice dynamics and on the long-term evolution of modeled glaciers and ice sheets. StISSM v1.0 additionally includes statistical models to represent surface mass balance and oceanic forcing as autoregressive processes. Such models, once appropriately calibrated, allow users to sample irreducible uncertainty in climate prediction without the need for computationally expensive ensembles from climate models. When combined together, these novel features of StISSM v1.0 enable quantification of irreducible uncertainty in ice sheet model simulations and of ice sheet sensitivity to noisy forcings. We detail the implementation strategy of StISSM v1.0, evaluate its capabilities in idealized model experiments, demonstrate its applicability at the scale of a Greenland ice sheet simulation, and highlight priorities for future developments. Results from our test experiments demonstrate the complexity of ice sheet response to variability, such as asymmetric and/or non-zero mean responses to symmetric, zero-mean imposed variability. They also show differing levels of projection uncertainty for stochastic variability in different processes. These features are in line with results from stochastic experiments in climate and ocean models, as well as with the theoretical expected behavior of noise-forced non-linear systems.

Flexibility for intergenerational justice in climate resilience decision-making: an application on sea-level rise in the Netherlands

To adapt to a changing climate, decision-makers design, evaluate, and implement measures that have an implication of justice on citizens in the present and well into the future. Decision-makers are often required to make decisions without certainty of the consequences and understanding their effects on intergenerational justice. Thus, managing the impacts of climate change requires novel decision-aiding approaches that consider climate impacts’ temporal and spatial heterogeneity and the uncertainty in climate predictions, preferences, and values. We reviewed the literature on the extent to which principles of intergenerational justice—conservation of options and resources for future generations—have been integrated to traditional approaches in climate resilience decision-making. We explore the extent to which flexibility, i.e., the conservation and expansion of options in subsequent decision periods, can contribute to upholding the principles of intergenerational justice under uncertainty. We illustrate the approach in the case of the Delta Programme in the Netherlands, a complex system designed to protect against sea-level rise (SLR). Designing adaptation strategies to SLR with flexibility as a core concept brings significant advantages in circumstances of uncertainty. The conservation of options in flexible pathways, in this case, contributes to the principles of intergenerational justice. Our civilization’s long-term sustainability and survival may depend on the extent to which individuals can see beyond their gains and toward the gains of the collective society at an intergenerational scale.

Use of Machine Learning to Reduce Uncertainties in Particle Number Concentration and Aerosol Indirect Radiative Forcing Predicted by Climate Models

AbstractThe radiative forcing of anthropogenic aerosols associated with aerosol‐cloud interactions (RFaci) remains the largest source of uncertainty in climate prediction. The calculation of particle number concentration (PNC), one of the critical parameters affecting RFaci, is generally simplified in climate models. Here we employ outputs from long‐term (30‐year) simulations of a global size‐resolved (sectional) aerosol microphysics model and a machine‐learning tool to develop a Random Forest Regression Model (RFRM) for PNC. We have implemented the PNC RFRM in GISS‐ModelE2.1 with a mass‐based One‐Moment Aerosol module, which is one of CMIP6 models. Compared to the default setting, the GISS‐ModelE2.1 simulation based on RFRM reduces the changes of cloud droplet number concentration associated with anthropogenic emissions, and decreases the RFaci from −1.46 to −1.11 W·m−2. This work highlights a promising approach based on machine learning to reduce uncertainties of climate models in predicting PNC and RFaci without compromising their computing efficiency.

Phytoplankton Impact on Marine Cloud Microphysical Properties Over the Northeast Atlantic Ocean.

The current understanding of the impact of natural cloud condensation nuclei (CCN) variability on cloud properties in marine air is low, thus contributing to climate prediction uncertainty. By analyzing cloud remote sensing observations (2009–2015) at Mace Head (west coast of Ireland), we show the oceanic biota impact on the microphysical properties of stratiform clouds over the Northeast Atlantic Ocean. During spring to summer (seasons of enhanced oceanic biological activity), clouds typically host a higher number of smaller droplets resulting from increased aerosol number concentration in the CCN relevant‐size range. The induced increase in cloud droplet number concentration (+100%) and decrease in their radius (−14%) are comparable in magnitude to that generated by the advection of anthropogenically influenced air masses over the background marine boundary layer. Cloud water content and albedo respond to marine CCN perturbations with positive adjustments, making clouds brighter as the number of droplets increases. Cloud susceptibility to marine aerosols overlaps with a large variability of cloud macrophysical and optical properties primarily affected by the meteorological conditions. The above findings suggest the existence of a potential feedback mechanism between marine biota and the marine cloud‐climate system.

Aerosol Optical Thickness Retrieval in Presence of Cloud: Application to S3A/SLSTR Observations

The Combined Inversion of Surface and AeRosols (CISAR) algorithm for the joint retrieval of surface and aerosol single scattering properties has been further developed in order to extend the retrieval to clouds and overcome the need for an external cloud mask. Pixels located in the transition zone between pure cloud and pure aerosol are often discarded by both aerosol and cloud algorithms, despite being essential for studying aerosol–cloud interactions, which still represent the largest source of uncertainty in climate predictions. The proposed approach aims at filling this gap and deepening the understanding of aerosol properties in cloudy environments. The new CISAR version is applied to Sentinel-3A/SLSTR observations and evaluated against different satellite products and ground measurements. The spatial coverage is greatly improved with respect to algorithms processing only pixels flagged as clear sky by the SLSTR cloud mask. The continuous retrieval of aerosol properties without any safety zone around clouds opens new possibilities for studying aerosol properties in cloudy environments.

Uncovering the Large-Scale Meteorology That Drives Continental, Shallow, Green Cumulus Through Supervised Classification.

One of the major sources of uncertainty in climate prediction results from the limitations in representing shallow cumulus (Cu) in models. Recently, a class of continental shallow convective Cu was shown to share distinct morphological properties and to emerge globally mostly over forests and vegetated areas, thus named greenCu. Using machine‐learning supervised classification, we identify greenCu fields over three regions, from the tropics to mid‐ and higher‐latitudes, and establish a novel satellite‐based data set called greenCuDb, consisting of 1° × 1° sized, high‐resolution MODIS images. Using greenCuDb in conjunction with ERA5 reanalysis data, we create greenCu composites for different regions and reveal that greenCu are driven by similar large‐scale meteorological conditions, regardless of their geographical locations throughout the world's continents. These conditions include distinct profiles of temperature, humidity and large‐scale vertical velocity. The boundary layer is anomalously warm and moderately humid, and is accompanied by a strong large‐scale subsidence in the free troposphere.

Lidar and Radar Signal Simulation: Stability Assessment of the Aerosol–Cloud Interaction Index

Aerosol–cloud interactions (ACI) are in the spotlight of atmospheric science since the limited knowledge about these processes produces large uncertainties in climate predictions. These interactions can be quantified by the aerosol–cloud interaction index (ACI index), which establishes a relationship between aerosol and cloud microphysics. The experimental determination of the ACI index through a synergistic combination of lidar and cloud radar is still quite challenging due to the difficulties in disentangling the aerosol influence on cloud formation from other processes and in retrieving aerosol-particle and cloud microphysics from remote sensing measurements. For a better understanding of the ACI and to evaluate the optimal experimental conditions for the measurement of these processes, a Lidar and Radar Signal Simulator (LARSS) is presented. LARSS simulate vertically-resolved lidar and cloud-radar signals during the formation process of a convective cloud, from the aerosol hygroscopic enhancement to the condensation droplet growth. Through LARSS simulations, it is observed a dependence of the ACI index with height, associated with the increase in number (ACINd) and effective radius (ACIreff) of the droplets with altitude. Furthermore, ACINd and ACIreff for several aerosol types (such as ammonium sulfate, biomass burning, and dust) are estimated using LARSS, presenting different values as a function of the aerosol model. Minimum ACINd values are obtained when the activation of new droplets stops, while ACIreff reaches its maximum values several meters above. These simulations are carried out considering standard atmospheric conditions, with a relative humidity of 30% at the surface, reaching the supersaturation of the air mass at 3500 m. To assess the stability of the ACI index, a sensitivity study using LARSS is performed. It is obtained that the dry modal aerosol radius presents a strong influence on the ACI index fluctuations of 18% cause an ACI variability of 30% while the updraft velocity within the cloud and the wet modal aerosol radius have a weaker impact. LARSS ACI index uncertainty is obtained through the Monte Carlo technique, obtaining ACIreff uncertainty below 16% for the uncertainty of all LARSS input parameters of 10%. Finally, a new ACI index is introduced in this study, called the remote-sensing ACI index (ACIRs), to simplify the quantification of the ACI processes with remote sensors. This new index presents a linear relationship with the ACIreff, which depends on the Angstrom exponent. The use of ACIRs to derive ACIreff presents the advantage that it is possible to quantify the aerosol–cloud interaction without the need to perform microphysical inversion retrievals, thus reducing the uncertainty sources.

Shallow Cumulus Properties as Captured by Adiabatic Fraction in High-Resolution LES Simulations

AbstractShallow convective clouds are important players in Earth’s energy budget and hydrological cycle, and are abundant in the tropical and subtropical belts. They greatly contribute to the uncertainty in climate predictions due to their unresolved, complex processes that include coupling between the dynamics and microphysics. Analysis of cloud structure can be simplified by considering cloud motions as a combination of moist adiabatic motions like adiabatic updrafts and turbulent motions leading to deviation from adiabaticity. In this work, we study the sizes and occurrence of adiabatic regions in shallow cumulus clouds during their growth and mature stages, and use the adiabatic fraction (AF) as a continuous metric to describe cloud processes and properties from the core to the edge. To do so, we simulate isolated trade wind cumulus clouds of different sizes using the System of Atmospheric Modeling (SAM) model in high resolution (10 m) with the Hebrew University spectral bin microphysics (SBM). The fine features in the clouds’ dynamics and microphysics, including small near-adiabatic volumes and a thin transition zone at the edge of the cloud (∼20–40 m in width), are captured. The AF is shown to be an efficient measure for analyzing cloud properties and key processes determining the droplet-size distribution formation and shape during the cloud evolution. Physical processes governing the properties of droplet size distributions at different cloud regions (e.g., core, edge) are analyzed in relation to AF.Significance Statement1) This study investigates the evolution of cumulus clouds (Cu) using a 10-m-resolution LES model with spectral bin microphysics. 2) The study improves the understanding of the mutual effects of adiabatic updrafts and lateral entrainment and mixing. 3) The study demonstrates the existence of an adiabatic core in nonprecipitating Cu. 4) Shapes of the droplet size distributions are closely related to the adiabatic fraction values. 5) Utilization of high resolution reveals the existence of physically significant small features in the cloud structure, such as a narrow cloud interface zone and small adiabatic volumes.

Evaluation and selection of CORDEX-SA datasets and bias correction methods for a hydrological impact study in a humid tropical river basin, Kerala

AbstractIt is well recognised that the performance of climate model simulations and bias correction methods is region specific, and, therefore, careful validation should always be performed. This study evaluates the performance of five general circulation model–regional climate model (GCM–RCM) combinations selected from CORDEX–SA datasets over a humid tropical river basin in Kerala, India, for climate variables such as precipitation, maximum and minimum temperatures. This involves ranking of the selected climate models based on an EDAS (Evaluation Based on Distance from Average Solution) method and the selection of an appropriate bias correction method for the selected three climate variables. A range of indices are used to evaluate the performance of the bias-corrected climate models to simulate observed climate data. Finally, the hydrological impact of the bias-corrected ranked models is assessed by simulating streamflow over the river basin using individual models and different combinations of models based on rank. According to the findings, hydrological simulation using an average of all GCM–RCM pairs provides the best model output in simulating streamflow, with an NSE value of 0.72. The results confirm the importance of a multimodel ensemble for improving the reliability and minimising the uncertainty of climate predictions for impact studies.

Modeling the Impacts of Climate Change on Groundwater Resources: A Review

Global atmospheric general circulation models (GCMs) were developed to simulate the current climate and are used to predict climate change. Several Global Climate Models (GCM’s) are available for understanding and projecting climate change. GCM requires to be downscale on a basin-scale and combined with applicable hydrological models considering all components of the hydrologic process. The performance of such coupling models, such as groundwater recharge quantification, should help to make correct adaptation strategies. Climate change has the ability to affect both the quality and quantity of available groundwater, mainly through impact on recharge, evapotranspiration, pump-age and abstraction. As a consequence, groundwater is a significant contributor to the streamflow in areas with fairly shallow water resources, knowing how climate change could impact groundwater supplies is crucial for long-term water resource management. The effect of climate change on groundwater systems is very difficult to predict. Part of the uncertainty of climate predictions is embedded of possibilities. Better insights, a more profound knowledge of mechanisms and modeling skills are required to determine this critical resource’s potential in the face of predicted climate change.

Linking Marine Biological Activity to Aerosol Chemical Composition and Cloud‐Relevant Properties Over the North Atlantic Ocean

AbstractThe ways in which marine biological activity affects climate, by modifying aerosol properties, are not completely understood, causing high uncertainties in climate predictions. In this work, in situ measurements of aerosol chemical composition, particle number size distribution, cloud condensation nuclei (CCN), and ice‐nucleating particle (INP) number concentrations are combined with high‐resolution sea surface chlorophyll‐a concentration (CHL) and back‐trajectory data to elucidate the relationship between oceanic biological activity and marine aerosol. The measurements were performed during an intensive field campaign conducted in late summer (August–September) 2015 at the Mace Head Research Station (MHD). At the short time scale (1–2 months) of the experiment, we observed a clear dependency of the main aerosol physicochemical and cloud‐relevant properties on the patterns of biological activity, in specific oceanic regions with a delayed response of about 1–3 weeks. The oceanic region comprised between 47°–57°N and 14°–30°W was identified as the main source of biogenic aerosols during the campaign, with hints of some minor influence of waters up to the Greenland coast. These spatial and temporal relationships demonstrate that the marine biota influences aerosol properties under a variety of features up to the most cloud‐relevant properties. Such dependency of aerosol properties with oceanic biological activity was previously reported over the North Atlantic Ocean only for multiyear data sets, where the correlation may be enhanced by coincident seasonalities. A better knowledge of these short time scale interactions may lead to a significant improvement in understanding the ocean‐atmosphere‐cloud system, with important impacts on climate science.

Modelling potential impact of climate change and uncertainty on streamflow projections: a case study

AbstractThis study presents climate change impacts on streamflow for the Subarnarekha basin at two gauging locations, Jamshedpur and Ghatshila, using the Soil and Water Assessment Tool (SWAT) model driven by an ensemble of four regional climate models (RCMs). The basin's hydrological responses to climate forcing in the projected period are analysed under two representative concentration pathways (RCPs). Trends in the projected period relative to the reference period are determined for medium, high and low flows. Flood characteristics are estimated using the threshold level approach. The analysis of variance technique (ANOVA) is used to segregate the contribution from RCMs, RCPs, and internal variability (IV) to the total uncertainty in streamflow projections. Results show a robust positive trend for streamflows. Flood volumes may increase by 11.7% in RCP4.5 (2006–2030), 76.4% in RCP4.5 (2025–2049), 20.3% in RCP8.5 (2006–2030), and 342.4% in RCP8.5 (2025–2049), respectively, for Jamshedpur. For Ghatshila, increment in flow volume is estimated as 15.7% in RCP4.5 (2006–2025), 24.2% in RCP4.5 (2025–2049), 35.9% in RCP8.5 (2006–2030), and 224.6% in RCP8.5 (2025–2049), respectively. Segregation results suggests that the uncertainty in climate prediction is dominated by RCMs followed by IV. These findings will serve as an early warning for the alarming extreme weather events India is currently facing.

Aerosol–photolysis interaction reduces particulate matter during wintertime haze events

Aerosol-radiation interaction (ARI) plays a significant role in the accumulation of fine particulate matter (PM2.5) by stabilizing the planetary boundary layer and thus deteriorating air quality during haze events. However, modification of photolysis by aerosol scattering or absorbing solar radiation (aerosol-photolysis interaction or API) alters the atmospheric oxidizing capacity, decreases the rate of secondary aerosol formation, and ultimately alleviates the ARI effect on PM2.5 pollution. Therefore, the synergetic effect of both ARI and API can either aggravate or even mitigate PM2.5 pollution. To test the effect, a fully coupled Weather Research and Forecasting (WRF)-Chem model has been used to simulate a heavy haze episode in North China Plain. Our results show that ARI contributes to a 7.8% increase in near-surface PM2.5 However, API suppresses secondary aerosol formation, and the combination of ARI and API results in only 4.8% net increase of PM2.5 Additionally, API increases the solar radiation reaching the surface and perturbs aerosol nucleation and activation to form cloud condensation nuclei, influencing aerosol-cloud interaction. The results suggest that API reduces PM2.5 pollution during haze events, but adds uncertainties in climate prediction.

Shallow-cloud impact on climate and uncertainty: A simple stochastic model

AbstractShallow clouds are a major source of uncertainty in climate predictions. Several different sources of the uncertainty are possible—e.g., from different models of shallow cloud behavior, which could produce differing predictions and ensemble spread within an ensemble of models, or from inherent, natural variability of shallow clouds. Here, the latter (inherent variability) is investigated, using a simple model of radiative statistical equilibrium, with oceanic and atmospheric boundary layer temperatures,ToandTa, and with moistureqand basic cloud processes. Stochastic variability is used to generate a statistical equilibrium with climate variability. The results show that the intrinsic variability of the climate is enhanced due to the presence of shallow clouds. In particular, the on-and-off switching of cloud formation and decay is a source of additional climate variability and uncertainty, beyond the variability of a cloud-free climate. Furthermore, a sharp transition in the mean climate occurs as environmental parameters are changed, and the sharp transition in the mean is also accompanied by a substantial enhancement of climate sensitivity and uncertainty. Two viewpoints of this behavior are described, based on bifurcations and phase transitions/statistical physics. The sharp regime transitions are associated with changes in several parameters, including cloud albedo and longwave absorptivity/carbon dioxide concentration, and the climate state transitions between a partially cloudy state and a state of full cloud cover like closed-cell stratocumulus clouds. Ideas of statistical physics can provide a conceptual perspective to link the climate state transitions, increased climate uncertainty, and other related behavior.