bluuebunny/crossref_metadata_2025 huggingface dataset:
Compound climate extremes: attribution and risk assessment to different compound extreme events in Africa
Authors: Engdaw Mastawesha , Hegerl Gabriele C. , Ballinger Andrew P. , Steiner Andrea K.
Published: February 2023

Detection and attribution of climate extremes in Africa has predominantly focused on univariate analysis of drought events and heat waves in different regions of the continent, although there have been unusual extreme events such as the Ethiopian heavy rainfall and flooding events in 2020, the 2022 flood in Nigeria and the 2020-2022 persistent drought event over Eastern Africa. Furthermore, the compounding of such type of extremes has not been investigated on the African continent so far.Decision makers and climate practitioners need context-specific and reliable information about the magnitude of climate risk for humans, socioeconomic and environmental sectors. Compound climate extremes (different combinations of heavy precipitation, floods, heat waves, droughts, and high discharge) are known for their large devastating impacts resulting from their concurrent or in-close-succession occurrence. Furthermore, the conventional univariate risk assessment can lead to under underestimated risk. Although risk analysis for compound extremes is a recent research topic globally, lack of such information is more acute for developing regions.Therefore, in this study, we conduct a detection and attribution study on the recent extreme events using a univariate analysis. In addition, we assess whether there was compounding of multiple extremes events using event coincidence analysis in different regions of the African continent. We use bivariate fraction of attributable risk method to take dependence between compounding events into account. We also assess sector-specific risks of compound climate extremes in the historical period and in the future using climate projections over Africa. Our risk analysis will be based on the recently proposed bottom-up approach and multi-criteria risk assessment, and follow procedures: prioritizing specific aspects of a sector of interest based on national and regional priorities, examining relevant climate and societal drivers, defining compound extremes, vulnerability assessment, preliminary analysis of risk assessment and refining the analysis for a detailed risk assessment.   Keywords: detection and attribution, risk and vulnerability assessment, compound climate extremes, heat waves, droughts, floods, climate change
Is there an upper extent to systematic bias correction of climate model simulations? Application to low-frequency variability within the Niño3.4 region
Authors: Kusumastuti Cilcia , Mehrotra Rajeshwar , Sharma Ashish
Published: February 2023

Most systematic bias correction approaches which are developed based on the bias of the statistical properties of interest perform well to bias correct the current climate simulations with respect to observations. However, the significance of the application of systematic bias correction approaches on the raw output of climate model simulations remains a debate due to the unavailability of future climate observation to validate the approach.The output of a recent ultra-high resolution climate model simulation, UHR-CESM, demonstrates the best performance to simulate variability of sea surface temperature (SST) in the tropical Pacific with an exception of a small bias in mean. This knowledge encouraged us to use the outputs of the model to represent the truth both in current and future climates. We use the output of the model in response to the current climate CO2 concentration as the representative of the current climate. While the outputs of model simulation in response to doubling and quadrupling CO2 concentrations are used as the representative of the truth of future climates.We bias correct monthly SST simulations for 8 (eight) Coupled Model Intercomparison Project 6 (CMIP6) over the Niño 3.4 region having the same CO2 concentration as our reference model using a novel time-frequency continuous wavelet-based bias correction (CWBC). The results show a nearly perfect correction of distributional, trend, and spectral attributes biases in the 8 (eight) climate model simulations in the current climate and a consistent reduction of the biases in the model simulation in response to doubled CO2 concentration. Although the overall quality of the statistical attributes is improved after the application of bias correction in response to the more extreme change of quadrupled CO2 concentration, a degradation in the spectral attributes is observed. It shows that a systematic bias correction approach has its upper limit. Therefore, while the application of bias correction approaches is recommended prior to the further use of raw climate model simulations, up to what extent future climate simulations are reliably bias corrected should be handled carefully.
Transforming cloudy air masses and surface impacts: a case study confronting MOSAiC observations, reanalyses and coupled model simulations
Authors: Dahlke Sandro , Solbès Amélie , Shupe Matthew D. , Cox Christopher J. , Maturilli Marion , Rinke Annette , Dorn Wolfgang , Rex Markus D.
Published: February 2023

Variability in the components of the Arctic surface energy budget and the atmospheric boundary layer (ABL) structure are to a large extent controlled by synoptic-scale changes and associated air mass properties. The transition of air masses between the radiatively clear and cloudy states, along with their characteristic surface impacts in radiation and ABL structure, can occur in either direction and on short time scales. In both states as well as during the transition, insufficient model representation of radiative processes and cloud microphysical properties cause biases in numerical weather prediction- and climate models. We employ observations from radiosondes, MET tower, and the ShupeTurner cloud microphysics product, which itself synthesizes a wealth of instruments, for the classification of an event of transition between low-level mixed phase cloud and clear conditions. The observed air mass properties and transition process are compared to ERA5 reanalysis data and output from a simulation of the coupled regional climate model HIRHAM-NAOSIM which applied non-spectral nudging to ERA5 in order to reproduce the observed synoptic-scale changes. The approach highlights the potential of event-based analysis of transformations of cloudy Arctic air masses by confronting models with observations. 
Analysis of Precipitation Extremes Using High Resolution Ensemble-based Dataset for India
Authors: Peringiyil Anagha , Saharia Manabendra
Published: February 2023

Grid-based meteorological estimates are indispensable in a wide variety of contexts. In India, most of the existing precipitation datasets are deterministic and have limitations when it comes to expressing the inherent uncertainties in data. Existing gridded datasets for India were created using a similar process, which comprised a multi-stage quality check, followed by methods such as Shepard's interpolation and probabilistic interpolation. This paper focus on the development of ensemble based gridded product for India named as, Indian Meteorological Ensemble Dataset (IMED) for 30 years. Additionally, this paper also discusses the analysis of precipitation extremes over Indian region. IMED (Indian Meteorological Ensemble Dataset) creates a daily ensemble precipitation product for the specified grid using gauge station readings as input, together with spatial variables such as latitude, longitude, elevation, and slope for the period of 30 years from 1991 to 2020. Daily, thirty distinct ensemble members are generated with a resolution of 0.25 degrees. IMD (Indian Meteorological Department) gridded precipitation data, CHIRPS gridded precipitation data and ERA5 land precipitation are compared with the mean of the developed ensemble members. In addition, a sensitivity analysis carried out to find out the possible combination of input parameters such as search radius, number of neighbouring stations to be considered, and number of ensembles to be used etc. and found that the combination 80, 25, 30 respectively gives better performance in terms of the quality of developed dataset as well as the time complexity. The generated ensemble has generally strong reliability and discrimination of events of different magnitudes and it is comparable to other widely used hydrometeorological datasets, although there are significant distinctions. The correlation coefficient between IMED and station precipitation data is 0.972, which is greater than the correlation coefficient between IMD gridded precipitation data and station precipitation data. Ensemble precipitation datasets are especially useful in places with substantial meteorological uncertainty, since practically all available deterministic datasets encounter formidable difficulties in obtaining reliable estimations.
A diagnosis of surface currents and sea surface heights in a coastal region
Authors: Kim Sung Yong , Lee Eun Ae
Published: February 2023

Upcoming satellite missions will observe the sea surface height (SSH) fields at a very high spatial resolution, which has generated an urgent need to understand better how well geostrophy can represent the ocean current field at finer scales, particularly in coastal regions characterized by complex flow geometry. We conduct statistical and spectral analyses of high-resolution surface currents and SSHs off the Oregon coast to examine the relative contribution of geostrophy and ageostrophy in coastal ocean currents. We analyze forward numerical simulations based primarily on a regional ocean model (ROMS) and use regional observations of high-frequency radar (HFR)-derived surface currents and altimeter-derived geostrophic currents and a subset of global domain numerical simulations (MITgcm) as secondary resources. Regional submesoscale ageostrophic currents account for up to 50% of the total variance and are primarily associated with near-inertial currents and internal tides. Geostrophy becomes dominant at time scales longer than 3 to 10 days and at spatial scales longer than 50 km, and is dependent on the depth and distance from the coast in the cross-shore direction. Ageostrophy dominates in the near-inertial and super-inertial frequency bands, which correspond to near-inertial motions (Coriolis force dominates) and high-frequency internal waves/tides (pressure gradient dominates), respectively. Because of ageostrophy, it may not be possible to estimate submesoscale currents from SSHs obtained from upcoming satellite missions using the geostrophic relationship. Thus, other concurrent high-resolution in-situ observations such as HFR-derived surface currents, together with data assimilation techniques, should be used for constructive data integration to resolve submesoscale currents.
Variability and the odds of Total and Pathogenic Vibrio abundance in Chesapeake Bay
Authors: Gangwar Mayank , Brumfield Kyle , Usmani Moiz , Jamal Yusuf , Jutla Antar , Huq Anwar , Colwell Rita
Published: February 2023

Vibrio spp. is typically found in salty waters and is indigenous to coastal environments.  V. vulnificus and V. parahaemolyticus frequently causes food-borne and non-food-borne infections in the United States. Vibrio spp. is sensitive to changes in environmental conditions and various studies have explored their relationship with the environment and have identified water temperature as the strongest environmental predictor with salinity also affecting the abundance in some cases. It is unclear how additional environmental factors will affect intra-seasonal variance as well as the seasonal cycle. This study investigated the intra-seasonal variations in total and pathogenic V. parahaemolyticus and V. vulnificus organisms in oysters and surrounding waters from 2009 to 2012 at a few locations in the Chesapeake Bay. V. Vulnificus is always pathogenic, but it has been observed that there was greater sample-to-sample variability in pathogenic V. parahaemolyticus than in total V. parahaemolyticus. To determine the increase in the likelihood of vibrio presence when the value of a certain environmental parameter has changed, the odds ratio is examined for various values of environmental factors. The odds ratio that we employed measures the likelihood that the desired outcome would occur in samples with the vibrio in comparison to the likelihood that the desired outcome will occur in samples without the vibrio. This technique will give us the threshold value of the environmental variable above which the likelihood of vibrio spp. presence has increased drastically. With changing climate and environmental conditions, vibrio is posing increasing risks to human health. The findings of this study will demonstrate the effectiveness of the odds ratio technique in estimating the likelihood that vibrio abundance would increase when environmental conditions change, which can then be incorporated into prediction models to reduce the danger to the public's health.
The development of multi-hazard risk scenarios for use in sector specific analyses in Europe and beyond
Authors: Daniell James , Schaefer Andreas , Claassen Judith , Ward Philip , de Ruiter Marleen , Khazai Bijan , Mysiak Jaroslav , Stuparu Dana , Girard Trevor
Published: February 2023

The topic of multiple hazards at sites has been in the mainstream for the past 50 years within Europe, however most studies have focussed on only a few key independent hazards and have been mostly limited to either buildings, specific sites like nuclear power plants or large scale studies. Less emphasis has been put on the application of these multiple hazards on particular sectors, and even less research has been done on the application of dependent hazard combinations.

The use of stochastic event sets or empirical data for natural peril events provide a useful basis to determine potential pairs or group events affecting a site, however the interactions with the built infrastructure and even further to the socio-economic interactions in a sector such as tourism are usually very difficult to quantify. Companion abstracts present empirical data overlaps (Claassen et al.) and the stochastic event sets and probabilistic outputs (Schaefer et al.) in this EGU23.

End-to-end independent and dependent scenarios are tested for a key sector, tourism, at various MYRIAD-EU test case sites, in order to examine which combinations merit quantitative, semi-quantitative or qualitative risk scenario methods. Quantitative and qualitative solutions are needed depending on the hazard combination, modellability, uncertainties and vulnerability and exposure feedbacks throughout the risk chain and risk metrics being examined. This is an extension from the work of the NARSIS project for nuclear power plants where a multi-hazard framework was used for sites across Europe.

The use of the EEA-CATDAT database (also a separate abstract in EGU23) is made in order to look at the historical impacts of empirical events are examined which have affected the tourism sector for the Canary Islands, Veneto and the Danube region in order to gain insights into the depth of multi-hazard analyses required to properly understand an event impact chain. In addition, external and simultaneous factors such as COVID-19 and/or other health events, wars and shocks are also examined. This work is envisaged to be able to be used in Europe and potentially extended globally.
Population Exposure to Rainfall-Driven Flooding from Hurricane Maria in Puerto Rico
Authors: Archer Leanne , Neal Jeffrey , Bates Paul , Carroll Dereka , Weaver Scott
Published: February 2023

Climate change is making rainfall associated with tropical cyclones more extreme. Some of the places most affected by tropical cyclones are small islands, such as Puerto Rico in the Caribbean which was severely impacted by Hurricane Maria in 2017. However, we know very little about how sensitive flooding in small islands is to changing rainfall characteristics, or how population exposure to flooding might change in the future. This is due to the limited data availability necessary to produce high-resolution flood hazard and population exposure estimates for a wide range of possible scenarios. Using an island-scale (~9000km2) event-based rainfall-driven hydrodynamic flood model at 20m resolution for the island of Puerto Rico, we simulate a range of observed rainfall grids from Hurricane Maria across time and space (such as IMERG and NCEP Stage IV). We assess how the current population exposure to rainfall-driven flooding changes across the range of observation rainfall footprints to determine how sensitive the flood extent and population exposure is to different rainfall inputs. We also compare these outputs to flood extents produced using an event set of synthetic hurricane rainfall events that share similar rainfall and track characteristics to Hurricane Maria under current and future climate scenarios (1.5°C and 2°C). Additionally, we utilise high-resolution (90m) gridded estimates of future population in Puerto Rico (FuturePop), to determine how an event with the same extreme magnitude as Hurricane Maria would impact population exposure to flooding under different future Shared Socioeconomic Pathway scenarios. The results of this analysis aim to improve understanding regarding the range of plausible estimates of current and future population exposure to flooding in Puerto Rico. These results will help inform adaptation to more extreme flood risk in Puerto Rico under current and future climate change.
Fossil war impact on atmosphere air, terrestrial ecosystems, and climate: involvement of master’s degree and post-graduate students in Ukrainian Polissia case study
Authors: Karamushka Viktor , Boychenko Svitlana , Nazarova Olga
Published: February 2023

Since the beginning of the military aggression of the Russian Federation in Ukraine on 24 February, 2022, military actions and rocket attacks caused a powerful devastating impact on the objects of the residential sector and industry, infrastructure of life support and energy sector as well as on the natural environment, ecosystems, life, and health of people. This factor had a significant impact on the educational process in the Kyiv-Mohyla Academy. On the one hand, aspects of the methodology and practice of assessing the environmental impact of military operations are reflected in the subjects taught to students of the master's program in Ecology. On the other hand, students of master's and PhD programs are involved in real surveys and environmental assessments of the state of deteriorated and polluted territories. The purpose of this study with students’ participation was to analyze the consequences of rocket and artillery shelling of oil depots in the villages of Kalynivka and Kryachky, Kyiv region, and the oil depot of the Aistra Enterprise in the city of Chernihiv. The article presents the results of the analysis of the impact of fires caused by shelling on atmospheric air, terrestrial ecosystems, and climatic characteristics of the Polissia region. The research and learning experience of the students are discussed additionally in terms of the capacity building of young researchers in extraordinary conditions.
A landslide risk management framework considering the role of resilience
Authors: Tian Naiman , Lan Hengxing
Published: February 2023

Landslides are causing a large number of fatalities and property damage around the world. In response to complex and unavoidable landslide disasters, actively enhancing the resilience of social systems is an essential disaster reduction strategy. Landslide risk management estimates the losses caused by landslides, evaluates the acceptability of those losses, and conducts risk control strategies to reduce those losses. Resilience, as an ability to cope with disasters, could significantly influence landslide risk. However, the traditional landslide risk management framework does not pay sufficient attention to the role of resilience. Hence, in this study, the role of resilience in a landslide risk management framework is systematically discussed after the relationship between resilience and vulnerability from a landslide risk perspective has been clarified. Then, we constructed a landslide risk management framework based on the role of resilience. In this framework, we have developed a landslide risk estimation method and a landslide risk evaluation method that take resilience into account. The consideration of resilience in risk estimation aids in the calculation of indirect losses and improves the results of direct loss analysis. Considering the resilience of social systems in risk evaluation can promote the establishment of a reasonable landslide risk tolerance threshold. As an indispensable role, resilience enables more rational landslide risk management for social sustainability.

We published a portion of this research in the journal Geography and Sustainability (Citation: Tian, N.M., Lan, H.X., 2023. The indispensable role of resilience in rational landslide risk management for social sustainability. Geography and Sustainability. https://doi.org/10.1016/j.geosus.2022.11.007). In the above paper, we discussed the role of resilience in the key steps of a landslide risk management framework and constructed a landslide risk management framework considering resilience. On the basis of this, we do an extensive study and focus on developing methods for carrying out the suggested framework.
Dynamic change of dissolved Cs-137 from headwaters to downstream in the Kuchibuto River catchment
Authors: Onda Yuichi , Kawano Taichi , Taniguchi Keisuke , Takahashi Junko
Published: February 2023

The Fukushima Daiichi Nuclear Power Plant (FDNPP) accident on March 11, 2011 resulted in the release of large amounts of radioactive cesium-137 (137Cs) into the environment. It is important to characterize the Cs-137 dynamics throughout the river from the headwaters to the downstream. Previous studies have suggested the importance of dissolved forms of Cs-137 in organic matter in small watersheds and dissolved forms in suspended solids in large watersheds. Since the concentration of suspended-form Cs has been shown to decrease significantly after decontamination in evacuated areas (Feng et al. 2022), this rapid decrease in suspended-form Cs-137 concentration can be used to determine the cause of dissolved-form Cs. Therefore, we attempted to evaluate whether the dissolved Cs-137 was derived from organic matter or suspended solids by comparing data before and after decontamination. The objective of this study is to compare the decreasing trends of Cs-137 concentrations in decontaminated and undecontaminated areas based on long-term monitoring of suspended solids, dissolved solids, and coarse organic matter Cs-137 concentrations since 2011. The study area includes four headwater basins and four river basins (eight sites in total) in the Kuchibuto River watershed in the Yamakiya district of Fukushima Prefecture, located approximately 35 km northwest of the FDNPP.In the Kuchibuto River watershed, a large inflow of decontaminated soil with low Cs-137 concentrations due to an increase in the amount of bare land caused by decontamination resulted in a rapid decrease in the concentration of suspended-form 137Cs in the decontaminated area in the headwaters and in the upper reaches of the river. However, no clear effect of decontamination was observed in the concentrations of dissolved Cs-137 and Cs-137 in coarse organic matter. Comparison of the slopes of Cs-137 concentrations in the suspended, dissolved, and coarse organic matter showed that the slope of the dissolved form was similar to that of the coarse organic matter in the source watersheds, and similar to that of the SS in the downstream watersheds. These results suggest that the contribution of dissolved Cs-137 from organic matter in small watersheds and that from suspended solids in large watersheds is significant.
A versatile Lagrangian-data aggregation framework for marine biological dispersal studies
Authors: Rath Willi , Schmittmann Lara , Trahms Carola , Kirch Felix , Mock Leon , Biastoch Arne
Published: February 2023

Lagrangian particle dispersal simulations are widely used for studying directed connectivity between different locations in the ocean. They are used, both, for the understanding of ocean physics and for interdisciplinary questions. One biological example is the dispersal of passively drifting marine organisms.The typical modus operandi of such “bio-physical” studies is to design an underlying Lagrangian simulation in close synchronisation with a specific biological research question. This leads to a conflation of concerns between physical and biological aspects of the study. This conflation might result in repeated and slow development cycles of re-calculation for different scenarios and hence inhibit scientific progress.We aim at improving the separation of concerns between biological and physical components for bio-physical Lagrangian studies, by aggregating physical Lagrangian data into directed multigraphs encoding locations as nodes and multiple parallel pathways as directed edges. Those graphs condense the physics-based information on directed oceanic relations and thus serve as a basis for simultaneously answering various biological questions on connectivity. As the proposed aggregation retains the distinction of different pathways between locations, it can, to some extent, also provide information of underway environmental conditions. This greatly enhances the range of applications of our approach over existing aggregations of Lagrangian data as connectivity probability graphs.We present a specific set of biological case studies — the multi-year spreading of two oyster diseases in the North Sea — and develop a framework that facilitates efficiently and simultaneously testing multiple biological hypotheses for marine diseases of various species based on the same processed physical data set.
Global validation of the SoilClim soil moisture estimates using in-situ and remote sensing observations
Authors: Podebradska Marketa , Fischer Milan , Balek Jan , Trnka Miroslav
Published: February 2023

Soil moisture is a key factor for plant growth and agricultural production. Therefore, it has become a fundamental part of agricultural drought monitoring systems developed for various spatial scales ranging from local to regional and global. Despite the development of large-scale soil moisture monitoring systems, in-situ soil moisture observations still remain inadequate for precise soil moisture monitoring, especially in remote areas, where there is a limited number of monitoring stations. Together with remote sensing technologies soil moisture modeling may provide an alternative to in-situ measurements that delivers spatially continuous estimates over large geographic areas. SoilClim is a semi-empirical water balance model that, together with other outputs (e.g., reference and actual evapotranspiration, soil temperature), estimates daily soil moisture in various depths of soil profile. The model has previously been validated on a total of 20 sites (5 in Central Europe and 15 in central USA) and is now used for global monitoring and prediction of soil moisture and drought intensity in an operational and interactive web platform (www.windy.com). Our study evaluates the SoilClim soil moisture global measurements with 0.1° spatial resolution using two independent sources of information: i) in-situ soil moisture measurements from the International Soil Moisture Network, and ii) the soil moisture derived from the Metop ASCAT sensors on Metop-A and Metop-B satellites. In the conference presentation we will introduce the SoilClim model and present results of the global validation including statistical spatial analysis and triple collocation.Acknowledgement: This study was conducted with support of SustES - Adaptation strategies for sustainable ecosystem services and food security under adverse environmental conditions (CZ.02.1.01/0.0/0.0/16_019/0000797).
A CNN - SVR model for NO2 profile Prediction based on MAX-DOAS observation
Authors: Tian Xin , Pan Yifeng , Xie Pinhua , Xu Jin , Li Ang , Wang Zijie , Hu Zhaokun , Zheng Jiangyi
Published: February 2023

In the Multi-Axis Differential Optical Absorption Spectroscopy (MAX-DOAS) trace gases profile retrieval, it needs to obtain the vertical profile of aerosols as the a priori, and depends on the atmospheric radiative transfer model (RTM). Therefore, a data mining method named CNN-SVR was adopted to achieve the prediction of NO2 profile, which combines the advantages of convolutional neural network (CNN), support vector regression (SVR) and MAX-DOAS. The optimization core of the hybrid model is embodied in three aspects. (1) CNN extracting the effective features of MAX-DOAS spectral data. The input data are MAX-DOAS spectrum, wind direction, wind speed, season, temperature, relative humidity, aerosol optical depth (AOD), cloud cover. Feature variables of MAX-DOAS spectra were extracted by CNN. The output data set is the NO2 profile retrieved by MAX-DOAS profile inversion algorithm PriAM. The data set is processed by normalization to unify the dimensions to ensure the accelerated convergence of the program. (2) The mean impact value (MIV) method selecting the input variables sensitive to NO2 profile forecasting. The MAX-DOAS spectral data, temperature, AOD and low cloud cover are finally determined as the best input parameters of the prediction model. (3) The hybrid forecasting method. Combined with the advantages that CNN can reduce the amount of data processing and retain useful information, and SVR does not depend on the dimension of input space, a CNN-SVR hybrid prediction model is proposed. The average percentage error (MAPE) of the CNN-SVR model is 9.14%. Compared with the separately constructed CNN, SVR and backpropagation models, the MAPE of CNN-SVR is reduced by 8.22%, 6.00% and 32.28% respectively. Therefore, CNN-SVR can effectively predict tropospheric NO2 profiles by using MAX-DOAS observation.
Estimation of 30 m land surface temperatures over the entire Tibetan Plateau based on Landsat-7 ETM+ data and machine learning methods
Authors: Wang Xian , Zhong Lei , Ma Yaoming
Published: February 2023

Land surface temperature (LST) is an important parameter in land surface processes. Improving the accuracy of LST retrieval over the entire Tibetan Plateau (TP) using satellite images with high spatial resolution is an important and essential issue for studies of climate change on the TP. In this study, a random forest regression (RFR) model based on different land cover types and an improved generalized single-channel (SC) algorithm based on linear regression (LR) were proposed. Plateau-scale LST products with a 30 m spatial resolution from 2006 to 2017 were derived by 109,978 Landsat 7 Enhanced Thematic Mapper Plus images and the application of the Google Earth Engine. Validation between LST results obtained from different algorithms and in situ measurements from the Tibetan observation and research platform showed that the root mean square errors of the LST results retrieved by the RFR and LR models were 1.890 K and 2.767 K, respectively, which were smaller than those of the MODIS product (3.625 K) and the original SC method (5.836 K).
Responses of westerly jet over Asia versus North America in Last Glacial Maximum and their attributions
Authors: Lei Jing , Shi Zhengguo , Sha Yingying , Li Xinzhou , Xie Xiaoning
Published: February 2023

The Northern Hemisphere westerly jet (WJ) during the Last Glacial Maximum (LGM) exhibits significant anomalies from present day. However, the total responses of Northern Hemisphere WJ, as well as the relative influences of different individual LGM forcings are still unclear. Therefore, this study analyzed the seasonal variation of Northern Hemisphere WJ during LGM through a series of sensitivity experiments, and evaluated the relative contributions and dynamic mechanisms of different forcing factors, with a special focus on ice sheet topography and albedo. Our results show that the WJ is intensified and shifts southward in North America, while generally weakens and displaces northward in central Asia. Over Japan, the WJ is enhanced and moves southward in summer but attenuated and shifts northward in winter. The change of WJ has a close relationship with the mid troposphere temperature anomalies which are resulted from different external forcings. In summer, the ice sheet albedo plays a leading role in wind fields at mid-to-high latitudes of Northern Hemisphere by causing a zonal wave-like response of temperature. While in winter, the ice sheet topography dominates the wind fields across North America to North Atlantic by inducing a northwest-southeast orientated tripole temperature response in this region. The effects of orbital parameters and greenhouse gases largely contribute to the alteration of upper wind fields over Asia and North Pacific by motivating a dipole response of temperature.
Deforestation and changes in rainfall across the Amazon – reducing uncertainty using a continental scale convection permitting domain
Authors: Bassett Richard , Garcia-Carreras Luis , Lowe Douglas , Alves Lincoln , Fisch Gilberto , Halladay Kate , Kahana Ron , Veiga José
Published: February 2023

The Amazon rainforest holds more than 40% of all remaining tropical rainforest and is a key component of the climate system. The scale of deforestation in the Amazon significantly impacts both local and global climates. Under a business-as-usual scenario as much as 40% of the Brazilian Amazon rainforest will be lost by 2050. Despite the magnitude of these changes and its importance, the overall effects of deforestation on rainfall remain uncertain. Land-use change influences rainfall through a variety of mechanisms acting at local to continental scales. As such, previous research indicates conflicting responses to rainfall depending on the scales studied. In reality, rainfall processes interact across these scales, but until recently have been impossible to capture within a single model due to computational expense. Consequently we are unable to rely on these simulations as future estimations of rainfall for such a sensitive and anthropogenically impacted region as the Amazon. In this study, we overcome these limitations by running convection permitting simulations (horizontal resolution 4.5km) over a large domain (6000km covering the majority of South America) using a Tropical configuration of the UK Met Office Unified Model. The high-resolution and continental-scale of these simulations present an opportunity to reduce the uncertainty in Amazonian rainfall estimates within a single model and ensures rainfall processes and interactions across scales are captured. To investigate the impacts of deforestation we will include a series of land-use sensitivity runs making use of a range of socioeconomic scenarios to 2050. Here we present initial results from our simulations, indicating how localised storms, mesoscale convective systems and large-scale circulations respond to land-use change.
Bayesian neural network-based satellite fog detection
Authors: Deshpande Prasad , Tripathi Shivam , Bhattacharya Arnab
Published: February 2023

Fog, an essential component of the hydrological cycle, is frequently experienced in North India during winter. The reduced visibility due to fog causes many accidents and delays in trains and flights, leading to loss of health and economy. Hence real-time detection and forecast of fog are crucial for mitigating these losses. The study proposes an algorithm to detect fog using satellite observations. The algorithm consists of Bayesian Neural Networks containing weights as probability distributions, unlike ordinary neural networks that treat weights as deterministic parameters. This algorithm provides prediction uncertainty. Both epistemic (data-dependent) and aleatoric (model-dependent) uncertainties are modelled. The final output is the percentage chances of fog which can be suitably thresholded into fog/no-fog. In this study, in situ airport weather records (METAR) are used as reference observations, whereas satellite observations are obtained from the 6 bands of the INSAT-3D geostationary satellite (with a spatial resolution of 4 km). Sub-hourly data of wintertime observations from 2016 to 2020 for seven cities spread across North India are used to train and validate the proposed methodology. The model performs better than the INSAT-3D fog product developed by ISRO. The critical success index of INSAT-3D fog product and the proposed method are 0.17 and 0.44, respectively, whereas Cohen’s Kappa values are 0.22 and 0.50, respectively. The uncertainty analysis shows that aleatoric uncertainty is generally higher than epistemic uncertainty. Moreover, for observations having higher aleatoric uncertainty, the epistemic uncertainty is also high, showing a positive correlation. The real-time predictions are disseminated on the website (www.fog.iitk.ac.in) for the public and scientists. This work is a part of the Fog Prediction using Data Science project.
Construction and management of sustainable vegetation system in Loess Plateau
Authors: wen zhongmin
Published: February 2023

In recent years, the vegetation coverage of the Loess Plateau has increased rapidly and the ecological benefits have been improved obviously, yet problems such as excessive water resources consumption and poor vegetation sustainability have occurred in this region. This paper summarized the studies over the past years, and 1) the basic process and characteristics of natural vegetation restoration were clarified, the potential distribution pattern of natural vegetation was predicted, the contribution of  environmental factors at different scale to the formation of vegetation pattern was evaluated, and the importance of habitat heterogeneity to the formation of vegetation pattern was revealed.  2) Based on the distribution pattern of potential natural vegetation, the distribution and biomass carrying capacity of existing artificial vegetation were evaluated, which provided support for site environment selection of vegetation restoration. 3) The soil water status of natural vegetation and artificial vegetation were compared, and the differences of functional traits between natural plant community and artificial robinia pseudoacacia were analyzed, and the reasons that cause soil drying and water resource loss by robinia pseudoacacia plantation were explained. 4) The runoff and sediment reduction of different vegetation restoration methods was analyzed, indicating that natural vegetation restoration had higher runoff and sediment reduction efficiency, which provides an important basis for adjusting vegetation restoration planning to save water resources. 5) The environmental adaptation strategies of 149 native plants in the Loess hilly region were analyzed, and the ecological suitability of 38 main afforestation tree species was evaluated, and a decision support system was established to provide a basis for the selection of vegetation restoration species. 6) The concept of structured vegetation index was proposed for the first time, and a structured vegetation index model was established, which provide a basis for the construction of a reasonably structured vegetation system for soil and water conservation. We concluded that vegetation restoration and reconstruction with stable natural vegetation as reference or through natural restoration will not cause soil drying, but also obtain sediment reduction benefits similar to artificial vegetation with less water reduction, thus reducing the consumption of water resources. In addition, the restored vegetation has community structure characteristics adapted to the site, which is of great importance to sustainable vegetation system.
Human exposure to size-segregated particulate polycyclic aromatic hydrocarbons during residential cooking in northeastern India
Authors: Sharma Bijay , Sarkar Sayantan
Published: February 2023

More than 50% of the population in the Indian subcontinent still depends on solid fuel for cooking purposes, and around 0.5 million yearly deaths are attributed to household air pollution. Despite the poor indoor air quality, especially in the rural spheres of the country, there is a lack of comprehensive field-based understanding of exposure to toxic chemical components such as polycyclic aromatic hydrocarbons (PAHs) associated with particulate matter (PM). This study attempts to quantify and assess the exposure to and inhalation health risk from 16 US EPA priority PAHs in size-segregated PM collected from relatively unexplored rural kitchens of northeastern India. A total of 44 sets of samples (5 from kitchens using liquefied petroleum gas (LPG), 18 using firewood (FW), 18 using a mixture of biomass (MB), and 3 non-cooking blanks) were collected and characterized from 6 locations. The sum of PM10-associated 16 priority PAHs (∑16 PAHs) was observed to be 258, 745, and 2554 ng m-3 for LPG, MB, and FW using kitchens, respectively. Size fraction-wise, the highest concentration of ∑16 PAHs was observed in PM0.25 and the least in PM1-10 across kitchen categories, with PM1 associated ∑16 PAHs comprising 80-92% of the total. ∑16 PAHs in kitchen settings were enriched by factors of 1.7-16.5 in comparison to the non-cooking background. Fuel-wise, stark differences were observed between kitchen categories. Within biomass using kitchens, the type of biomass and combustion (flaming vs smoldering) influenced the PAHs concentration and composition, e.g., ∑16 PAHs were enriched by factors of 3.4 for smoldering combustion of FW compared to flaming combustion of MB. Composition-wise, 2-3 ring PAHs dominated the total PAHs concentration in LPG kitchens (82%), while it constituted 43 and 25% in MB and FW using kitchens, respectively. In contrast, 5-6 ring PAHs showed dominance in FW using kitchens (52%), followed by MB (40%). This suggested a greater release of high molecular weight PAHs during biomass combustion with an increased contribution during the smoldering phase. Estimation of the total BaP equivalent concentration (BaPeq) revealed a similar profile as ∑16 PAHs with the lowest values in LPG using kitchens (29 ng m-3) followed by MB (235 ng m-3), and the highest in FW (856 ng m-3). Incremental lifetime cancer risk (ILCR) estimation via the inhalation pathway showed values above the acceptable risk for LPG (3.2×10-5), and much above tolerance levels for biomass using kitchens (MB: 2.6×10-4; FW: 9.4×10-4). Overall, these findings warrant immediate intervention into the cooking practices prevalent in northeastern India, with an emphasis on biomass-dependent households, in order to alleviate health risks.
The Dynamics and Structure of the Baroclinic Annular Mode
Authors: Gerber Edwin , Youngs Madeleine , Pauluis Olivier
Published: February 2023

As first explored by Thompson and Barnes (2014), hemispheric mean storm activity (or related quantities, such as the meridional eddy heat flux) exhibits periodicity on 20-30 day time scales.  They characterized this variability with the so-called Baroclinic Annular Mode, or BAM, a ring of enhanced eddy activity which is present in both hemispheres, but most pronounced in the south, which is less perturbed by zonal asymmetries relative to the north.   The mechanism behind this internally generated periodicity, however, has remained elusive.  We probe the dynamics and structure of the BAM on two fronts.  To understand the mechanism, we develop a minimal model that captures the essential dynamics: 2 layer quasi-geostrophic flow in a channel. By varying the geometry of the channel and the thermal and frictional forcing, we tease out the parameters that control the period and amplitude of the BAM.  The resulting changes in the BAM support the general framework of the charge-discharge mechanism proposed by Thompson and Barnes, but demand a more detailed explanation for the coupling between eddies and the mean baroclinicity that generates enhanced variability on subseasonal time scales.  On a second front, we apply dynamical mode decomposition (DMD) to atmospheric reanalyses of the Southern Hemisphere to quantify the structure of the southern BAM.  DMD captures BAM variability, providing additional information on relationships between the eddy kinetic energy and other mean and eddy quantities.  It suggests that moisture plays a fundamental role in the relationship between the eddy activity and baroclinicity, and that changes in stratification are more important than horizontal temperature gradients in the dynamics.    In this sense, the underlying BAM dynamics of vacillation between eddy and potential energy are remarkably robust, active in our moist atmosphere and in dry quasi-geostrophic systems where only the meridional temperature gradient can capture the available energy.
Carbon fluxes from different fire types in the Amazon and Cerrado biomes quantified using Earth-observation based modelling
Authors: Fawcett Dominic , Ng Leo , Tai Amos , Yan Xiaoyu , Rosan Thais , Silva Junior Celso , Bastos Ana , Ciais Philippe , Albergel Clément , Aragão Luiz , Sitch Stephen
Published: February 2023

Fires in the tropics are linked to both climate and land-use change. While in the Amazon, deforestation-related fires decreased following a substantial reduction in deforestation activities until 2012, there have been recent upturns in deforestation and forest fires. Furthermore, earth system models predict a further increase in the intensity of dry seasons in this region in the 21st century. Therefore, carbon emissions from drought-induced forest fires can counteract further pledged deforestation reductions in the following decades, yet they are only partially accounted for in national carbon emission estimates. Improved assessments of fire impacts, including the carbon fluxes arising from post-fire mortality and regrowth, are therefore highly important. Combining a range of available satellite products enables spatially specific fire emissions estimations. We developed a remote sensing based approach where biomass maps, observed forest loss, burned area and active fire data are combined to generate updated fuel load and emission estimates. In addition, space-for-time methods are employed to derive estimates of post-fire mortality as a function of pre-fire biomass. This high-resolution model guarantees an improved separation of fire types, and we can report emissions associated with deforestation, forest degradation and savanna fires for the entire Amazon basin and the Brazilian Cerrado at monthly intervals. Results show that over 2015-2020 fires cause annual gross emissions of ~300 Tg C over the Amazon and Cerrado. While instantaneous emissions from forest fires are small, the fire-induced mortality and subsequent decomposition cause legacy fluxes which are closer to deforestation fire emissions in magnitude, highlighting their importance. Recent upturns in deforestation fire emissions were observed, including in conservation areas established before 2004. There is overall good agreement with previous instantaneous fire emission estimates from other approaches (GFED4s, GFED 500 m) while remaining disagreements highlight areas of uncertainties, such as combustion completeness values, which could benefit from additional field measurements and spatial modelling supported by EO products.Outputs from this work can further be used to improve regional greenhouse gas budgets and inform emission reduction and mitigation efforts.