Complex Climate System Research Articles

Penerapan Analisis Data Panel Pada Suhu Udara Terhadap Perubahan Iklim Curah Hujan di Kota Padang

Climate is the average weather conditions that prevail over an extended period in a particular region. The complex climate system, comprising components such as the atmosphere, lithosphere, and biosphere, generates climate variations among different areas. Climate change has potential impacts such as changes in rainfall patterns, rising temperatures, and sea level rise. Extreme weather, prolonged droughts, and heatwaves can endanger both the environment and human beings.This article aims to assist in predicting future climate patterns to prepare for their impacts using panel data analysis. A panel data regression model is employed to assess the influence of rainfall and air temperature on climate change in Kota Padang from 2018 to 2022. The analysis reveals a significant influence of air temperature, estimated at 0.08, on climate change in Kota Padang during this period. These findings provide a basis for developing mitigation and adaptation strategies to address climate challenges in the region and offer valuable insights for government policies in facing future climate-related issues.

Escalating Wildfires in Siberia Driven by Climate Feedbacks Under a Warming Arctic in the 21st Century

AbstractSiberian wildfire is of paramount importance in the carbon cycle and climate change as it is a major disturbance in the pan‐Arctic ecosystems. In recent decades, the Siberian wildfire regime has been shifting; however, less is known about its process‐based feedback mechanisms. By integrating in‐situ and satellite observational data sets as well as chemistry‐climate coupled modeling, we find that central Siberia has featured the most prominent wildfire escalation during the past two decades, which is closely related to hydrological drought with decreasing rainfall and drying soil under a fast‐warming Arctic. Furthermore, fire‐emitted aerosols compound the increasing wildfires via serving as cloud condensation nuclei and suppressing precipitation, forming self‐amplifying feedback. As the Arctic warming is projected to continue, wildfires are estimated to more than double by the end of this century. This work highlights the great importance of fire risk management based on a fundamental scientific understanding of the complex climate system.

Characterizing climate pathways using feature importance on echo state networks

AbstractThe 2022 National Defense Strategy of the United States listed climate change as a serious threat to national security. Climate intervention methods, such as stratospheric aerosol injection, have been proposed as mitigation strategies, but the downstream effects of such actions on a complex climate system are not well understood. The development of algorithmic techniques for quantifying relationships between source and impact variables related to a climate event (i.e., a climate pathway) would help inform policy decisions. Data‐driven deep learning models have become powerful tools for modeling highly nonlinear relationships and may provide a route to characterize climate variable relationships. In this paper, we explore the use of an echo state network (ESN) for characterizing climate pathways. ESNs are a computationally efficient neural network variation designed for temporal data, and recent work proposes ESNs as a useful tool for forecasting spatiotemporal climate data. However, ESNs are noninterpretable black‐box models along with other neural networks. The lack of model transparency poses a hurdle for understanding variable relationships. We address this issue by developing feature importance methods for ESNs in the context of spatiotemporal data to quantify variable relationships captured by the model. We conduct a simulation study to assess and compare the feature importance techniques, and we demonstrate the approach on reanalysis climate data. In the climate application, we consider a time period that includes the 1991 volcanic eruption of Mount Pinatubo. This event was a significant stratospheric aerosol injection, which acts as a proxy for an anthropogenic stratospheric aerosol injection. We are able to use the proposed approach to characterize relationships between pathway variables associated with this event that agree with relationships previously identified by climate scientists.

Pliocene Model Intercomparison Project Phase 3 (PlioMIP3) – Science plan and experimental design

The Pliocene Model Intercomparison Project (PlioMIP) was initiated in 2008. Over two phases PlioMIP has helped co-ordinate the experimental design and publication strategy of the community, which has included an increasing number of climate models and modelling groups from around the world. It has engaged with palaeoenvironmental scientists to foster new data synthesis supporting the construction of new model boundary conditions, as well as to facilitate new data-model comparisons. The work has advanced our understanding of Pliocene climates and environments, enhanced our knowledge regarding the ability of complex climate and Earth System models to accurately simulate climate change, and helped to refine our estimates of how sensitive the climate system is to forcing conditions.In this community protocol paper, we outline the scientific plan for PlioMIP Phase 3 (PlioMIP3). This plan provides the required guidance to participating modelling groups from around the world to successfully set up and perform PlioMIP3 climate model experiments. The project is open to new participants from the scientific community (both from the climate modelling and geosciences communities).In PlioMIP3, we retain the PlioMIP2 Core experiments (Eoi400, E280) and extend the Core requirements to include either an experiment focussed on the Early Pliocene or an alternative Late Pliocene simulation (or both). These additions (a) allow a comparison of Early and Late Pliocene warm intervals and help build research connections and synergy with the MioMIP (Miocene Model Intercomparison Project - also known as DeepMIP-Miocene) and PlioMioVAR projects (Pliocene-Miocene Variability Working Group), and (b) create an alternative time slice simulation for 3.205 Ma (MIS KM5c) through removal of some of the largest palaeogeographic differences introduced between PlioMIP1 and 2 resulting in minimal land-sea mask variations from the modern. In addition, we present ten optional experiments designed to enhance our assessment of climate sensitivity and to explore the uncertainty in greenhouse gas-related forcing. For the first time, we introduce orbital sensitivity experiments into the science plan, as well as simulations incorporating dynamic vegetation-climate feedbacks and an experiment designed to examine the potential significance of East Antarctic Ice Sheet boundary condition uncertainty. These changes enhance palaeo-to-future scientific connections and enable an exploration of the significance of palaeogeographic uncertainties on climate simulations.

Spatio-Temporal Drought Assessment Using Standardized Precipitation Evapotranspiration Index (SPEI) over Mantaro Valley, Peru

Peru has several studies based on the Peruvian Tropical Andes (PTA) and its effects due to climate change. It has been shown that due to orography characteristics, different kinds of climate conditions are seen. In that sense, the PTA has a very complex climate system, which causes significant variability mostly related to an increase in temperature and a decrease in precipitation patterns. The Mantaro Valley is located in the PTA, Junin region. Where vulnerable farmers are already affected by poverty, practice agriculture based on rain-fed cropping systems. Hence, climate variability causes agricultural vulnerability and water resource scarcity. This study aims to elaborate the Spatio-temporal drought assessment over Mantaro Valley to provide information on local climate change events. In order to do so, the Standardized Precipitation Evapotranspiration Index (SPEI), recognized by the World Meteorological Organization (WMO), was used. The results have shown that almost every station has clear evidence of warming over the years and only a precipitation decrease in two out of six stations. Furthermore, the Spatio-temporal analysis shows around 30% of drought events from the total data, considering the frequency, severity, and duration. In conclusion, it has been demonstrated that all analyzed stations within Mantaro Valley have the same deportment regarding drought characteristics with different highest frequency per time scale. Finally, it is recommended to keep tracking stations with the lowest available data, realize comparison analysis with different drought assessment methods, and study the correlation between drought and ENSO events and other climate events such as floods.

Radiative controls by clouds and thermodynamics shape surface temperatures and turbulent fluxes over land

Land surface temperatures (LSTs) are strongly shaped by radiation but are modulated by turbulent fluxes and hydrologic cycling as the presence of water vapor in the atmosphere (clouds) and at the surface (evaporation) affects temperatures across regions. Here, we used a thermodynamic systems framework forced with independent observations to show that the climatological variations in LSTs across dry and humid regions are mainly mediated through radiative effects. We first show that the turbulent fluxes of sensible and latent heat are constrained by thermodynamics and the local radiative conditions. This constraint arises from the ability of radiative heating at the surface to perform work to maintain turbulent fluxes and sustain vertical mixing within the convective boundary layer. This implies that reduced evaporative cooling in dry regions is then compensated for by an increased sensible heat flux and buoyancy, which is consistent with observations. We show that the mean temperature variation across dry and humid regions is mainly controlled by clouds that reduce surface heating by solar radiation. Using satellite observations for cloudy and clear-sky conditions, we show that clouds cool the land surface over humid regions by up to 7 K, while in arid regions, this effect is absent due to the lack of clouds. We conclude that radiation and thermodynamic limits are the primary controls on LSTs and turbulent flux exchange which leads to an emergent simplicity in the observed climatological patterns within the complex climate system.

Climate Change: Localizing a Complex Global Issue

Climate ChangeLocalizing a Complex Global Issue Martha E. Durr (bio) Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press, 2022. https://www.ipcc.ch/report/ar6/wg2/. Climate Change 2022: Impacts, Adaptation, and Vulnerability. Contribution of Working Group II to the Sixth Assessment Report of the Intergovernmental Panel on Climate Change. New York: Cambridge University Press, 2022. https://www.ipcc.ch/report/ar6/wg2/. If only science were enough, the climate crisis would be solved by now. This is a statement I remind myself of and think about daily. Serving as a State Climatologist in the Great Plains, I have the privilege of providing a science-based voice on local-to-global changing climate. Global climate change is inherently complex, and often a translator is needed to understand and navigate resources to extract relevant information. Reports such as the Intergovernmental Panel on Climate Change provide timely and valuable information. Implications and solutions to climate change have been requested by hundreds of organizations with information broadcasted to thousands of individuals. In doing so, insights into concerns for what climate change means and how action can and should be realized provide a wealth of knowledge for informing solutions. These solutions lie within us—how we perceive our risk, our will to implement lasting behavior change, and the ability and capacity to do so. This essay begins with an introduction of frequently asked questions. Then it summarizes our climate past in order to place future change into historical context; synthesizes local implications of global climate change; offers considerations; and provides suggestions for an equitable path forward. Background A statement I have heard on numerous occasions is "Our climate is always changing; why is this so different?" Our climate has and will continue to change due to natural causes. Shifts in our complex climate system (which is composed of the landscape, the dynamic atmosphere, the vast heat-storing and heat-redistributing world ocean, the frozen world, energy from the sun, and us humans) are common. Significant volcanic eruptions, cycles of solar energy output, and changes in Earth's orbit about the sun all influence our regional and global climate. Internal variability in the ocean and atmosphere also plays a role in our climate. Sometimes the surface waters of the Pacific Ocean off the coast of South America cool, influencing the jet stream and weather patterns globally, even here in the Great Plains. When [End Page 97] these surface waters warm, the predominant weather patterns change, bringing us a different set of conditions. Since the global ocean and atmosphere are interactively connected, a change or shift in one part of the globe can and often does produce a snowball effect of sorts and influences weather conditions far away. What happens in the Pacific doesn't stay in the Pacific, one might say. We will come back to this later in the essay when it deals with the rapid rate of Arctic climate change and what it means for the Great Plains. While it is true that our climate does change naturally, it also changes because of what humans have been doing and continue to do, most notably changing the composition of our atmosphere. The primary way in which humans alter our climate is through an enhancement of the naturally occurring greenhouse effect. That's right, I said "naturally occurring." The greenhouse effect is often not well understood by the public. Ask anyone on the street what it means and you will get just as many answers as the people you asked. The science of the greenhouse effect has been known and documented for more than a century. Gasses in our atmosphere such as carbon dioxide, methane, nitrous oxide, and even water vapor have properties making them essentially invisible to incoming sunlight. However, these same gasses absorb Earth's outgoing longwave radiation (thermal energy) and reemit that energy back to earth. We require the greenhouse effect for a comfortable life on Earth. Without it, our average temperature would be 60°F colder. What we do not need, however, is its enhancement. The higher the concentration...

Insights into moisture sources and evolution from groundwater isotopes (2H, 18O, and 14C) in Northeastern Qaidam Basin, Northeast Tibetan Plateau, China

Knowledge of moisture sources is of great significance for the understanding of groundwater recharge and hydrological cycle. However, it is often difficult to identify the moisture sources and evolution especially in the areas with complex climate system. Isotopes in groundwater that acts as a climate archive provide a unique perspective on the moisture sources and evolution. In this study, the stable isotopes (2H, 18O) of precipitation and groundwater, radioactive isotope (14C) of groundwater, water vapor trajectory modeling (HYSPLIT models) and d-excess based on mass balance model were employed to reveal the groundwater origin, moisture source and evolution in the northeastern Qaidam Basin, northeast Tibetan Plateau, China. The stable isotopic compositions indicate that the precipitation in the mountainous areas is the main origin of groundwater. The spatiotemporal variation of groundwater d-excess together with HYSPLIT modeling suggest that the moisture sources in the northeastern Qaidam Basin have been controlled by the Westerlies and did not alter obviously with time, whereas Delingha with relatively low elevation is influenced by both the Westerlies and local recycled moisture. More than 80 % water vapor derives from the northwest of study area for the plain and mountainous area, except for the mountainous area of Delingha, where approximately 23 % water vapor originates from the surface water evaporation in the plain area. The water vapor with high d-excess formed in the plain area is transported to the mountainous area and mixed with advected water vapor, resulting in the large d-excess of groundwater in Delingha. The moisture recycling fraction in precipitation for the mountainous area of Delingha is estimated to be about 2.0 % by using d-excess-based mass balance model. The results of the study could be helpful to the understanding of hydrological cycle of the area and elsewhere.

The temporal correlation of fluctuation–variation in the non-stationary complex climate system

In this article we mainly focus on the temporal correlation between the past dynamic fluctuations and the future motion of the dynamic variables in the climate system by means of a new temporal correlation function. When the complex climate system is assumed to be in the stationary states, that is, the dynamic effects of the non-stationary states are not considered, the above correlation is weak or even non-existent. Unexpectedly, a good correlation is shown when the dynamic effects of the non-stationary states are taken into account. Subsequently, we analyze the main factors contributing to these results and conclude that the small fluctuations make a major contribution to this. At the same time, we also find that this correlation function is somewhat stable over time scales. Finally, we extend our analysis by using the persistence probabilities. Knowing these is of key importance for the study of climate analysis and prediction.

On the Effects of Mixed and Deep Ocean Layers on Climate Change and Variability

The ocean, one of the five major components of the Earth’s climate system, plays a key role in climate-forming processes, affecting its change and variability. The ocean influences climate over a wide range of time–space scales. To explore the climate, its components, interactions between them and, in particular, the effect of the ocean on weather and climate, researchers commonly use extremely complex mathematical models of the climate system that describe the atmospheric and ocean general circulations. However, this class of climate models requires enormous human and computing resources to simulate the climate system itself and to analyze the output results. For simple climate models, such as energy balance and similar models, the computational cost is insignificant, which is why these models represent a test tool to mimic a complex climate system and obtaining preliminary estimates of the influence of various internal and external factors on climate, its change and variability. The global mean surface temperature (GMST) and its fluctuations in time serve as critical indicators of changes in the climate system state. We apply a simple two-box ocean model to explore the effect of mixed and deep ocean layers on climate-forming processes and especially on climate change and variability. The effect of mixed and deep ocean layers on GMST is parameterized via the layers’ effective heat capacities and heat exchange between layers. For the listed parameters, the sensitivity functions were derived numerically and analytically, allowing one to obtain an idea of how the mixed and deep ocean layers affect climate change and variability. To study climate change, a deterministic version of the model was used with radiative forcing parameterized by both stepwise and linear functions. In climate variability experiments, a stochastic version of the model was applied in which the radiative forcing is considered as a delta-correlated random process.

Solar Activities and their Relationship to the Maximum Temperatures of the City of Baghdad for the Solar Cycles 23 and 24

The interaction of solar activities with the layers of the Earth's atmosphere determines the pattern of the complex climate system. It is clear that any change in incoming solar radiation has the potential to affect the climate. In this research, the effect of the energies of coronal mass ejections and solar flares on the maximum temperatures of the summer seasons over the city of Baghdad for the time period (1996-2019) representing the 23 and 24 solar cycles was studied. CME data were collected from the SOHO/LASCO CME Catalog. Solar flare data were taken from the National Oceanic and Atmospheric Center (NOAA) database. As for the geoclimatic data for the study for the summer seasons, it was relied on through the meteorological ground station in Baghdad affiliated with the Iraqi General Authority for Meteorology and Seismic Monitoring. The statistical program Minitab 19.0 was used, and the results showed a direct relationship between the energies of coronal mass ejections and solar flares with maximum temperatures during the solar cycles 23 and 24. Except for the descending phase of the solar cycle 24, the results showed an inverse relationship.

Multi-proxy indications of depositional evolution and paleo-natural disasters (flooding and fire) in the southern part of the Korean Peninsula during the Holocene

Understanding paleoenvironmental changes and corresponding driving forces can provide valuable information for predicting and preparing for future climate change. However, under the complex climate system, the driving mechanisms behind hydroclimatic variability in terms of timescales and magnitude are still unclear, and the various ecological responses are not clear. Here, we reconstruct the history of swamp evolution and climate changes in the southern part of the Korean Peninsula during the Holocene based on multi-proxy data (palynomorphs, microscopic charcoal, diatoms, grain size, total organic carbon, C/N ratio, and δ13CTOC data). The climatic conditions in the study area gradually became cold and dry due to the weakening East Asian summer monsoon (EASM) after ca. 5400 cal yr BP, as evidenced by the shift in vegetation communities from temperate broad-leaved forests dominated by Quercus to mixed conifer and deciduous broad-leaved forests with increased cold/dry-tolerant taxa. After ca. 4700 cal yr BP, microscopic charcoal concentration to terrestrial pollen concentration values are relatively high together with a sharp decrease in palynomorph concentrations and the proportions of arboreal pollen, representing open forest with fewer plants due to frequent wildfires. In addition, the quasi-millennial frequencies of seasonal fires (dry spring) and flooding (summer rainfall) were identified in our study area during the Late Holocene, which correspond to lower sea surface temperatures of the western tropical Pacific and stronger El Niño Southern Oscillation (ENSO) activity periods. After ca. 8200 cal yr BP, the depositional environment underwent gradual backswamp expansion due to high precipitation, strengthened EASM, and relative sea-level rise during the Early to Middle Holocene. During the Late Holocene, ENSO-driven extreme hydroclimatic variability, such as frequent flooding and fire, had significant impacts on not only swamp development with large fluctuations in water level but also long-term vegetation and aquatic ecosystem dynamics.

Effects of Sunshine Hours and Daily Maximum Temperature Declines and Cultivar Replacements on Maize Growth and Yields

In this study, the crop environment resource synthesis maize (CERES-Maize) model was used to explore the effects of declining sunshine hours (SSH), decreasing daily maximum temperature (Tmax), and cultivar replacements on growth processes and yields of maize in Northern China, a principal region of maize production. SSH were found to decrease at 189 of 246 meteorological stations in the northern provinces of China over the period of 1994–2012, and a decrease in Tmax was also seen at many of these stations. The most significant decrease in these two climate variables occurred during June to September, a period for summer maize growth. For this study, seven crop field stations in the ShaanXi province, in the Guanzhong Plain, were selected, all of which showed a downward trend in SSH and Tmax over the period of 1994–2012. The CERES-Maize model was first calibrated and validated against yield observations for these stations over the same period, and the yield simulations matched very well with observations. The model was then driven by the detrended SSH and Tmax data, and the simulations were compared with those with a trend in these two input variables. The decline in SSH was found to reduce the maize yield by 8% on average over these stations due mostly to limited root growth, and the decline for shorter SSH reduced the yield more than that for longer SSH. Meanwhile, the decrease in higher Tmax increased the yield by extending the growth period, while the decrease in lower Tmax reduced the yield by lowering the thermal time. In addition, the observed yield showed a significant upward trend, and our modeling results indicate that this increase can be attributed mainly to the frequent cultivar replacements over our study period. The replaced cultivars usually had a longer growth period than the prior ones, which compensated for the yield loss due to fewer SSH. Net maize production decreased with the combined effects of the declines in SSH and Tmax on yields. This study quantifies the contribution of changes in climate and cultivars to maize growth processes and yields and provides strong insights into maize production under a complex dynamic climate system.

A causal flow approach for the evaluation of global climate models

AbstractGlobal climate models (GCMs) are generally used to forecast weather, understand the present climate, and project climate change. Their reliability usually rests on their capability to represent climatic processes, and most evaluations directly measure the spatiotemporal agreement of scalar climate variables. However, climate naturally involves complex interactions that are hard to infer and, therefore, difficult to evaluate. Climate networks (CNs) have been used to infer flows of mass and energy in the complex climate system. Here, an Evaluation of Models by Causal Flows (EMCaF) is proposed. EMCaF focuses on the assessment of properties about mass and energy flows in the CNs derived from GCMs. First, causal CNs are inferred from GCMs, and then the capabilities to reproduce characteristic transfer flows are assessed with reference models. A more in‐depth feature is the possibility to assess how climate change disturbs CNs properties. In addition to the quantitative difference between modelled and observed values taken into account in standard evaluations, the EMCaF approach aims to assess the weaknesses and strengths of GCMs to represent climate mechanisms and processes that couple different components of the climate system. The comparison of models through this approach allows having complimentary feedback on model evaluations to understand possible causes of errors and enable a judgement based on processes. The approach is illustrated by evaluating one GCM and subsequently assessing changes of its CNs under future climate projections. Results show that known climatic patterns are assimilated and that causal strength patterns are likely to agree with the wind magnitude as a transfer factor. Significative issues are then explored, showing the capabilities of the approach and allowing understand fundamental structures in transport flows, compare their properties, and assess changes in the future. Different alternatives and considerations in each step of the approach are discussed to expand its applicability.

Directional information coupling dynamics in complex climate system

Climate change has been significantly disturbing the dynamics of different earth system components, such as land surface and ocean, as well as the interactive relationship between different components. Here we aim to investigate the sea surface temperature dynamics and its remote connection to the precipitation patterns. Previous efforts on the remote ocean-land remote coupling are mostly relied on linear based statistical inference framework, disregarding the nonlinearity of the earth system dynamics. Here we apply a new inference framework that fully adapts to nonlinear system to quantify the coupling strength between Atlantic Oceanic temperature signals (AMO index) and US precipitation patterns. We found that the linear based coupling patterns are significantly different from the nonlinear based coupling patterns, which provides important insights into the system nonlinearity. We also conduct uncertainty analysis to quantify the estimated coupling strength uncertainty and discuss the robustness of the climate coupling between AMO and US precipitation.

A simple earth system model for C3IAM: based on BCC_CSM1.1 and CMIP5 simulations

Traditional complex climate system models used in natural sciences cannot match the integrated assessment models well due to differences in temporal and spatial resolutions and especially computational complexities. In this study, we develop a simple earth system model called Beijing Climate Center Simple Earth System Model (BCC_SESM), which is based on the complex BCC_CSM1.1 model and its CMIP5 simulation results as inputs. The basic and complete processes are considered in the BCC_SESM initiating from the carbon emission, including the carbon cycle processes in atmosphere–land–ocean, the radiation forcing and temperature changes. The emission-driven simulation results for historical and RCP8.5 scenarios are selected as the default datasets, and econometric regressions are applied to calibrate the parameters. Projection analysis and sensitive experiments with the BCC_SESM show that this model is parsimonious and robust enough to reproduce results of original complex climate system model. We also compare results of parameters and projection efficacy of different mid-lower emission scenarios simulations by applying this BCC_SESM. The results show that BCC_SESM is suitable for climate projections in the integrated assessment modeling, i.e., the C3IAM.

Spatiotemporal analysis of nonlinear trends in precipitation over Germany during 1951–2013 from multiple observation‐based gridded products

Spatial and temporal patterns of trends in annual and seasonal precipitation over Germany during 1951–2013 were analysed using the ensemble empirical mode decomposition (EEMD) method. Three widely used and recognized high‐resolution observation‐based gridded precipitation products, the Climatic Research Unit (CRU) time‐series data, Global Precipitation Climatology Centre (GPCC) Full Data Reanalysis data and the EU‐FP6 project ENSEMBLES derived data set (EOBS), were used and compared. Comparison among different products showed that the CRU used a considerably lower number of gauge stations (maximum 36) in the construction of gridded product over Germany and should be used with great caution for other applications, and overall CRU presented less precipitation and smaller inter‐annual variability; GPCC and EOBS agreed well in the spatial and temporal distribution of seasonal and annual precipitation, as well as the identified trends. The spatiotemporal analysis of trends showed that trends in precipitation during 1951–2013 were, in most cases, highly nonlinear and varying over time. In particular, the spring, summer and autumn precipitation showed large variations in trends. Therefore, the assumption of linear trends on which previous studies were based is invalid. Annual precipitation showed increasing trends in over 66% of Germany (particularly northern and eastern Germany), and the magnitude of increasing trends were generally enhanced over time, e.g. GPCC showed that the average magnitude of increasing trends rose from 6.3% for the 1951–1980 period to 12% for the 1951–2013 period. There were considerable spatial and temporal variabilities in trends in seasonal precipitation totals over Germany. This study is among one of the first studies applying the EEMD method to a comprehensive analysis of time‐varying trends in precipitation over Germany, which is expected to improve our understanding of the complex and nonlinear regional climate system.

Precipitation forecasting using classification and regression trees (CART) model: a comparative study of different approaches

Interest in semiarid climate forecasting has prominently grown due to risks associated with above average levels of precipitation amount. Longer-lead forecasts in semiarid watersheds are difficult to make due to short-term extremes and data scarcity. The current research is a new application of classification and regression trees (CART) model, which is rule-based algorithm, for prediction of the precipitation over a highly complex semiarid climate system using climate signals. We also aimed to compare the accuracy of the CART model with two most commonly applied models including time series modeling (ARIMA), and adaptive neuro-fuzzy inference system (ANFIS) for prediction of the precipitation. Various combinations of large-scale climate signals were considered as inputs. The results indicated that the CART model had a better results (with Nash–Sutcliffe efficiency, NSE > 0.75) compared to the ANFIS and ARIMA in forecasting precipitation. Also, the results demonstrated that the ANFIS method can predict the precipitation values more accurately than the time series model based on various performance criteria. Further, fall forecasts ranked “very good” for the CART method, while the ANFIS and the time series model approximately indicated “satisfactory” and “unsatisfactory” performances for all stations, respectively. The forecasts from the CART approach can be helpful and critical for decision makers when precipitation forecast heralds a prolonged drought or flash flood.

The Perceptions of Pre-Service Science Teachers and Science Teachers about Climate Change

<p>The global climate phenomenon in the context of climate change is the impact of both the dynamic complex climate system and human behaviors that affect environmental sustainability. Human is an important component that should be considered in science teaching that is believed to improve human attitudes towards the environmental sustainability. The research aims to investigate the perceptions of pre-service science teachers and science teachers in South Sumatra who teach climate change and global warming. The data were collected from 17 science teachers and 53 pre-service science teachers from April to August 2016. The instruments were 17 modified questions which were developed from Pruneau’s framework. There are three linear perceptions regarding climate change. First, greenhouse effect causes global warming and global warming causes climate change. Second, ozone leakage causes global warming and global warming causes acid rain. Third, greenhouse effect causes ozone leakage and ozone leakage causes global warming; then it causes climate change and other climatic phenomena. Both pre-service science teachers and science teachers argue that climate change is caused by global warming. Actually, climate change is not only global warming but also global cooling. Those phenomena occur because of interactions among climate system components. They do not believe that education is able to change human attitudes in saving environmental sustainability from global climate change disasters. They believe that media give stronger effects than teachers in shaping those perceptions. Factually, most of wrong perceptions come from media.<br /><br /><br /><br /></p>

Polar Cyclone Identification from 4D Climate Data in a Knowledge-Driven Visualization System

Arctic cyclone activity has a significant association with Arctic warming and Arctic ice decline. Cyclones in the North Pole are more complex and less developed than those in tropical regions. Identifying polar cyclones proves to be a task of greater complexity. To tackle this challenge, a new method which utilizes pressure level data and velocity field is proposed to improve the identification accuracy. In addition, the dynamic, simulative cyclone visualized with a 4D (four-dimensional) wind field further validated the identification result. A knowledge-driven system is eventually constructed for visualizing and analyzing an atmospheric phenomenon (cyclone) in the North Pole. The cyclone is simulated with WebGL on in a web environment using particle tracing. To achieve interactive frame rates, the graphics processing unit (GPU) is used to accelerate the process of particle advection. It is concluded with the experimental results that: (1) the cyclone identification accuracy of the proposed method is 95.6% when compared with the NCEP/NCAR (National Centers for Environmental Prediction/National Center for Atmospheric Research) reanalysis data; (2) the integrated knowledge-driven visualization system allows for streaming and rendering of millions of particles with an interactive frame rate to support knowledge discovery in the complex climate system of the Arctic region.