Terms Of Extreme Events Research Articles

Drought versus flood: What matters more to the performance of Sahel farming systems?

AbstractRecent climate change has brought new patterns of extreme events in terms of both drought and heavy rainfall to the drought‐prone African Sahel. The effects of these recent extreme events on the performance of the Sahel farming systems are still weakly investigated. This study aims at assessing effects of droughts versus floods on crop yield levels and losses, focusing on the so‐called recovery period, particularly 2001–2020. A newly developed productivity‐drought condition index (PDCI) is utilized to assess agricultural productivity as related to drought or flood in a highly vulnerable region, that is, the Sudanese Sahel. Four farming systems, namely traditional rainfed, mechanized rainfed, gravity irrigated and spate irrigated systems, with sorghum and millet as staple food crops, are considered. The PDCI is defined as a function of the integrated normalized difference vegetation index (iNDVI) over the growing season. To address temporal and spatial variabilities, scaling of the PDCI is done in two dimensions: space and time. Crop statistics are used to derive yield losses. Our results show that both drought and flood episodes (seven and six episodes, respectively) can be captured using the PDCI. Drought remains the most relevant risk to Sahel's crop productivity. Some recent large‐scale floods led to yield loss. However, floods cause smaller risks to agricultural productivity compared to droughts. Floods may even result in enhanced crop yields. Based upon scaling in the time or space domain, ranking the severity of drought impacts on crop yield for individual years from 2001 to 2020 reveals least to slightly different results. Vulnerability to drought depends on the crop type and farming system. Drought effect on crop yield from the irrigated sector is clear on individual years but not as a general statistical relationship. The parameter ‘percentage area under drought’ explains around one‐third of the variation in the rainfed crop yield. The spate irrigation scheme, the gravity irrigated system and the rainfed farmlands experienced respectively 87%, 57% and 46% of area under drought on average. Irrigated systems produce much higher crop yields than rainfed systems. The mechanized system is more drought‐vulnerable than the traditional system. These results call for identifying agricultural management pathways that recognize the combined implications of both hydrological extremes for the region's food security.

Study of Extreme Magnetopause Distortions Under Varying Solar Wind Conditions

AbstractTo first order, the magnetopause (MP) is defined by a pressure balance between the solar wind and the magnetosphere. The boundary moves under the influence of varying solar wind conditions and transient foreshock phenomena, reaching unusually large and small distances from the Earth. We investigate under which solar wind conditions such extreme MP distortions occur. Therefore, we construct a database of magnetopause crossings (MPCs) observed by the THEMIS spacecraft in the years 2007 to mid‐2022 using a simple Random Forest Classifier. Roughly 7% of the found crossing events deviate beyond reported errors in the stand‐off distance from the Shue et al. (1998, https://doi.org/10.1029/98JA01103) MP model and thus are termed extreme distortions. We find the occurrence of these extreme events in terms of expansion or compression of the MP to be linked to different solar wind parameters, most notably to the IMF magnitude, cone angle, velocity, Alfvén Mach number and temperature. Foreshock transients like hot‐flow anomalies and foreshock bubbles could be responsible for extreme magnetospheric expansions. The results should be incorporated into future magnetopause models and may be helpful for the reconstruction of the MP locations out of soft x‐ray images, relevant for the upcoming SMILE mission.

Forecasting seed production in perennial plants: identifying challenges and charting a path forward.

Interannual variability of seed production, known as masting, has far-reaching ecological impacts including effects on forest regeneration and the population dynamics of seed consumers. Because the relative timing of management and conservation efforts in ecosystems dominated by masting species often determines theirsuccess, there is a need to study masting mechanisms and develop forecasting tools for seed production. Here, we aim to establish seed production forecasting as a new branch of the discipline. We evaluate the predictive capabilities of three models - foreMast, ΔT, and a sequential model - designed to predict seed production in trees using a pan-European dataset of Fagus sylvatica seed production. The models are moderately successful in recreating seed production dynamics. The availability of high-quality data on prior seed production improved the sequential model's predictive power, suggesting that effective seed production monitoring methods are crucial for creating forecasting tools. In terms of extreme events, the models are better at predicting crop failures than bumper crops, likely because the factors preventing seed production are better understood than the processes leading to large reproductive events. We summarize the current challenges and provide a roadmap to help advance the discipline and encourage the further development of mast forecasting.

«Я з вогненнай вёскі» как книга о травматическом опыте жертв Второй мировой войны

The article is devoted to the non-fiction book Out of the Fire. The authors had an extraordinary task to record the memory of civilians about the Second World War in Belarus. The methods of work of the authors with the respondents (search for witnesses, ways to establish contact, important points of interviewing witnesses) are analyzed. At the beginning of the article, the difficulties that the authors encountered in the process of working with people who survived mass executions are described. The style of the work is considered, attention is paid to journalistic and documentary elements. The structure of the book and the ways of its organization are also studied. The article explores how the experience of war victims and the concept of a fiery village are reflected in the work. A significant part of the article is devoted to the psychological reactions of civilians who survived extreme events in terms of cruelty. Out of the Fire is a topical and remarkable book about the suffering of civilians in war with a strong anti-war message. The experience of the authors in collecting materials, methods of working with respondents can be useful to modern documentary authors.

The impact of social and environmental extremes on cholera time varying reproduction number in Nigeria.

Nigeria currently reports the second highest number of cholera cases in Africa, with numerous socioeconomic and environmental risk factors. Less investigated are the role of extreme events, despite recent work showing their potential importance. To address this gap, we used a machine learning approach to understand the risks and thresholds for cholera outbreaks and extreme events, taking into consideration pre-existing vulnerabilities. We estimated time varying reproductive number (R) from cholera incidence in Nigeria and used a machine learning approach to evaluate its association with extreme events (conflict, flood, drought) and pre-existing vulnerabilities (poverty, sanitation, healthcare). We then created a traffic-light system for cholera outbreak risk, using three hypothetical traffic-light scenarios (Red, Amber and Green) and used this to predict R. The system highlighted potential extreme events and socioeconomic thresholds for outbreaks to occur. We found that reducing poverty and increasing access to sanitation lessened vulnerability to increased cholera risk caused by extreme events (monthly conflicts and the Palmers Drought Severity Index). The main limitation is the underreporting of cholera globally and the potential number of cholera cases missed in the data used here. Increasing access to sanitation and decreasing poverty reduced the impact of extreme events in terms of cholera outbreak risk. The results here therefore add further evidence of the need for sustainable development for disaster prevention and mitigation and to improve health and quality of life.

Extreme events in biological, societal, and earth sciences: A systematic review of the literature

The term “extreme event” is commonly used to describe high-impact, unanticipated natural events, like floods, tsunamis, earthquakes, and volcanic eruptions. It first appeared in the scientific literature in the 1950s and has since spread to disciplines as diverse as economics, psychology, medicine, and engineering. The term is increasingly being applied to the study of historical, prehistorical, and deep-time events across a broad range of scales, and it is widely acknowledged that such events have had profound impacts on the Earth’s biodiversity and cultures. Understandably, then, how people think about, define, and study extreme events varies considerably. With extreme events expected to become more frequent, longer lasting, and more intense in the coming decades as a result of global warming, the differing extreme event definitions—both across and within disciplines—is likely to lead to confusion among researchers and pose significant challenges for predicting and preparing for extreme events and their impacts on natural and social systems. With this in mind, we conducted a systematic quantitative review of 200 randomly selected, peer-reviewed “extreme event” research papers (sourced from Web of Science, accessed January 2020) from the biological, societal, and earth sciences literature with the aim of quantifying several pertinent features of the research sample. On the one hand, our analysis found a great deal of variability among extreme event papers with respect to research interests, themes, concepts, and definitions. On the other hand, we found a number of key similarities in how researchers think about and study extreme events. One similarity we encountered was that researchers tend to view extreme events within a particular temporal context and quite often in terms of rates of change. Another similarity we encountered was that researchers often think of and study extreme events in terms of risks, vulnerabilities, and impacts. The similarities identified here may be useful in developing a common and comprehensive definition of what constitutes an extreme event, and should allow for more comparative research into extreme events at all spatio-temporal scales which, we predict, will provide important new insights into the nature of extreme events.

Combining multi-spectral and thermal remote sensing to predict forest fire characteristics

Forest fires preparedness strategies require the assessment of spatial and temporal variability of fire danger. While several tools have been developed to predict fire occurrence and behaviour from weather data, it is acknowledged that fire danger models may benefit from direct assessment of live fuel condition, as allowed by Earth Observation technologies. In this study, the performance of pre-fire observations of land surface temperature (LST) anomaly and of the Perpendicular Moisture Index (PMI) in predicting fire characteristics was evaluated against the Canadian Forest Fire Weather Index (FWI) System, a fire danger model adopted in several areas worldwide. To this purpose, a database of forest fires recorded in Campania (13,595 km2), Italy, was combined with MODIS retrievals of LST anomaly and PMI, and with FWI maps from NASA’s Global Fire Weather Database. Fires were grouped in decile bins of LST anomaly, PMI and FWI System components, and probability distribution functions of burned area, fire duration and rate of spread were fitted in each bin. The dependence of probability model parameters on LST anomaly, PMI and FWI System components was assessed by means of trend analysis (coefficient of determination and p-value of the linear fit, Sen’s slope and Mann-Kendall test) and likelihood ratio test versus the corresponding unconditional probability model. Finally, the probability of an extreme event, conditional to ignition, was modelled as a function of LST anomaly and PMI. Results show that the probability distribution function of burned area has a strong dependence on both LST anomaly and PMI, that the probability distribution function of fire duration has a strong dependence on LST anomaly but not on PMI, and that the probability distribution function of rate of spread has a weak dependence on LST anomaly and a strong dependence on PMI. These results are in line with expectations from models of the combustion and flames propagation processes. Trend analyses and likelihood ratio tests showed that the FWI System components are good predictors of burned area and fire duration, but not of rate of spread. They also confirmed that, where LST anomaly and PMI are covariates of the considered fire characteristic, their performance is similar or better than the FWI System components. Finally, the probability of an extreme event in terms of burned area as a joint function of LST anomaly and PMI shows a wider dynamic range than the same probability modelled as a function of these remote sensing variables individually.

Different Impact of Heart Rate Variability in the Deep Cerebral and Central Hemodynamics at Rest: An in silico Investigation.

Background: Heart rate variability (HRV), defined as the variability between consecutive heartbeats, is a surrogate measure of cardiac vagal tone. It is widely accepted that a decreased HRV is associated to several risk factors and cardiovascular diseases. However, a possible association between HRV and altered cerebral hemodynamics is still debated, suffering from HRV short-term measures and the paucity of high-resolution deep cerebral data. We propose a computational approach to evaluate the deep cerebral and central hemodynamics subject to physiological alterations of HRV in an ideal young healthy patient at rest.Methods: The cardiovascular-cerebral model is composed by electrical components able to reproduce the response of the different cardiovascular regions and their features. The model was validated over more than thirty studies and recently exploited to understand the hemodynamic mechanisms between cardiac arrythmia and cognitive deficit. Three configurations (baseline, increased HRV, and decreased HRV) are built based on the standard deviation (SDNN) of RR beats. For each configuration, 5,000 RR beats are simulated to investigate the occurrence of extreme values, alteration of the regular hemodynamics pattern, and variation of mean perfusion/pressure levels.Results: In the cerebral circulation, our results show that HRV has overall a stronger impact on pressure than flow rate mean values but similarly alters pressure and flow rate in terms of extreme events. By comparing reduced and increased HRV, this latter induces a higher probability of altered mean and extreme values, and is therefore more detrimental at distal cerebral level. On the contrary, at central level a decreased HRV induces a higher cardiac effort without improving the mechano-contractile performance, thus overall reducing the heart efficiency.Conclusions: Present results suggest that: (i) the increase of HRV per se does not seem to be sufficient to trigger a better cerebral hemodynamic response; (ii) by accounting for both central and cerebral circulations, the optimal HRV configuration is found at baseline. Given the relation inversely linking HRV and HR, the presence of this optimal condition can contribute to explain why the mean HR of the general population settles around the baseline value (70 bpm).

ENSO diversity driving low-frequency change in mesoscale activity off Peru and Chile

Transient mesoscale oceanic eddies in Eastern Boundary Upwelling Systems are thought to strongly affect key regional scale processes such as ocean heat transport, coastal upwelling and productivity. Understanding how these can be modulated at low-frequency is thus critical to infer their role in the climate system. Here we use 26 years of satellite altimeter data and regional oceanic modeling to investigate the modulation of eddy kinetic energy (EKE) off Peru and Chile by ENSO, the main mode of natural variability in the tropical Pacific. We show that EKE tends to increase during strong Eastern Pacific (EP) El Niño events along the Peruvian coast up to northern Chile and decreases off central Chile, while it is hardly changed during Central Pacific El Niño and La Niña events. However the magnitude of the EKE changes during strong EP El Niño events is not proportional to their strength, with in particular the 1972/1973 El Niño event standing out as an extreme event in terms of EKE increase off Peru reaching an amplitude three times as large as that during the 1997/1998 El Niño event, and the 2015/2016 El Niño having instead a weak impact on EKE. This produces decadal changes in EKE, with a similar pattern than that of strong EP El Niño events, resulting in a significant negative (positive) long-term trend off Peru (central Chile).

Future climatic changes, extreme events, related uncertainties, and policy recommendations in the Hindu Kush sub-regions of Pakistan

This study explores the relative changes and future projections of temperature and precipitation for baseline (1976–2005) and future (2006–2040, 2041–2070, and 2071–2100) periods for the Bajaur, Mohmand, and Khyber districts of Pakistan situated in the Hindu Kush region. Data from 14 GCMs (out of which five GCMs were selected based on evaluation and validation) and three RCMs were downscaled and bias corrected using the quantile delta mapping (QDM) method for GCMs and the best easy systemic (BES) method for RCMs. The future extremes were projected by using the standard indices from the Expert Team on Climate Change Detection and Indices (ETCCDI). Uncertainty in the data was analysed by a probabilistic distribution function (PDF), boxplots, and standard deviation. For RCP4.5, the GCM and RCM projections show an increase in the average maximum temperature of 0.98 °C for 2006–2040, 1.89 °C and 2.04 °C for 2041–2070, and 2.25 °C and 2.56 °C for 2071–2100, respectively, while the increase is almost double for RCP8.5 during the last period (2071–2099) over the whole study area. The percent increase in precipitation over the whole study area from GCMs and RCMs for RCP4.5 is 10.00–17.00% and 21.14–34.47%, while for RCP8.5, it is 11.73–22.12% and 16.17–31.50%, respectively. In RCM projections for RCP4.5, the Khyber district will experience drier conditions in mid-century, while for GCMs (RCP4.5), the same conditions are projected through the end of the century. In terms of extreme events, warm temperature extremes and extreme precipitation events show an increasing trend accompanied by a decrease in cold extremes over all regions. Hence, it is concluded that warm and wet conditions are projected to prevail in all regions. However, GCM data depict less uncertainty compared with RCM data, and while models show less error for the baseline period, the error increases slightly for the future periods. Thus, this study provides research findings along with policy recommendations essential to combat the potential impacts of projected changes in the regional climate.

Interconnectedness in the Australian National Electricity Market: A Higher Moment Analysis

We examine the risk transmission mechanisms in the interconnected Australian National Electricity Market (NEM). We illustrate that the transmission of extreme events in terms of their magnitude (via skewness) and the likelihood of their occurrence (via kurtosis) should be considered when promoting NEM interconnectedness. Our empirical findings suggest that interconnectedness costs can be limited by providing sufficient transmission capacities as it can expand generation capacity. Our results suggest that a one percent increase in NEM generation capacity can decrease the transmission of these risks by between 0.9 percent and 1.7 percent, depending on the moment of the electricity return distribution.

Evaluation of Daily Precipitation Product in China from the CMA Global Atmospheric Interim Reanalysis

The China Meteorological Administration (CMA) recently produced a CMA Global Atmospheric Interim Reanalysis (CRAI) dataset for the years 2007–2016. A comprehensive evaluation of the ability of CRAI to capture the spatiotemporal variability of observed precipitation, in terms of both mean states and extreme indicators over China, is performed. Comparisons are made with other current reanalysis datasets, namely, the ECMWF interim reanalysis (ERAI), Japanese 55-yr reanalysis (JRA55), NCEP Climate Forecast System Reanalysis (CFSR), and NASA Modern-Era Retrospective analysis for Research and Applications version 2 (MERRA2), as well as NCEP Climate Prediction Center (CPC) observations. The results show that, for daily variations of rainfall during warm seasons in eastern China, CRAI and CFSR overestimate the precipitation of the main rain belt, while the overestimation is confined to the area south of 25°N in JRA55 but north of 24°N in MERRA2; whereas ERAI tends to underestimate the precipitation in most regions of eastern China. Two extreme metrics, the total amount of precipitation on days where daily precipitation exceeds the 95th percentile (R95pTOT) and the number of consecutive dry days (CDDs) in one month, are examined to assess the performance of reanalysis datasets. In terms of extreme events, CRAI, ERAI, and JRA55 tend to underestimate the R95pTOT in most of eastern China, whereas more frequent extreme rainfall can be found in most regions of China in both CFSR and MERRA2; and all of the reanalyses underestimate the CDDs. Among the reanalysis products, CRAI and JRA55 show better agreement with the observed R95pTOT than the other datasets, with fewer biases, higher correlation coefficients, and much more similar linear trend patterns, while ERAI stands out in better capturing the amount and temporal variations of the observed CDDs.

Grass community responses to drought in an African savanna

Drought is often discussed as a major perturbation in savanna ecosystems, associated with lasting shifts in savanna structure and functioning. However, historically, droughts have occurred frequently in southern African savannas, suggesting that plants might benefit from being resilient to drought. Here, we evaluate whether the 2014–2016 drought was an extreme event in terms of effects on the herbaceous vegetation and specifically on grasses. We monitored herbaceous responses, pre-, during and post-drought in the Kruger National Park and compared responses with a previous extreme drought (1991–1992). Grass biomass decreased during both droughts, but recovered quickly once rains returned, regardless of whether areas burned in the years preceding drought. However, species functional composition was slower to recover, with shifts in dominance that closely matched grass photosynthetic machinery. Species drought tolerances were best predicted by their bundle sheath morphology: species characterised by XyMS− bundle sheaths (often associated with NADP-ME photosythesis) declined in abundance consistently during drought, whereas those characterised by XyMS+ bundle sheaths (associated with PEP-ck and NAD-ME photosynthesis) either increased or were unaffected by drought. The response of grass to drought, at least in terms of productivity and functional composition, is relatively predictable.

Experimental investigation of vertical turbulent transport of a passive scalar in a boundary layer: Statistics and visibility graph analysis

The dynamics of a passive scalar plume in a turbulent boundary layer is experimentally investigated via vertical turbulent transport time-series. Data are acquired in a rough-wall turbulent boundary layer that develops in a recirculating wind tunnel set-up. Two source sizes in an elevated position are considered in order to investigate the influence of the emission conditions on the plume dynamics. The analysis is focused on the effects of the meandering motion and the relative dispersion. First, classical statistics are investigated. We found that (in accordance with previous studies) the meandering motion is the main responsible for differences in the variance and intermittency, as well as the kurtosis and power spectral density, between the two source sizes. On the contrary, the mean and the skewness are slightly affected by the emission conditions. To characterize the temporal structure of the turbulent transport series, the visibility algorithm is exploited to carry out a complex network-based analysis. Two network metrics -- the average peak occurrence and the assortativity coefficient -- are analysed, as they can capture the temporal occurrence of extreme events and their relative intensity in the series. The effects of the meandering motion and the relative dispersion of the plume are discussed in the view of the network metrics, revealing that a stronger meandering motion is associated with higher values of both the average peak occurrence and the assortativity coefficient. The network-based analysis advances the level of information of classical statistics, by characterizing the impact of the emission conditions on the temporal structure of the signals in terms of extreme events and their relative intensity. In this way, complex networks provide -- through the evaluation of network metrics -- an effective tool for time-series analysis of experimental data.

Total OH Reactivity Changes Over the Amazon Rainforest During an El Niño Event

The 2015/16 El Niño event caused unprecedented drought and warming in the Amazon basin. How tropical forests react to such extreme events in terms of volatile organic compound (VOC) emissions is of interest as the frequency of these events is predicted to increase through climate change. The diverse VOCs emitted can be significant for plants' carbon budgets, influence ozone and particle production, and through their reactivity impact OH concentrations. Total OH reactivity is a directly measureable quantity that gives the reaction frequency of OH radicals with all reactive species in the atmosphere in s−1. Here we present a comparison of the OH reactivity diel cycle from November 2015, i.e., extreme drought and elevated temperatures associated with strong El Niño conditions, with November 2012, a “normal” El Niño Southern Oscillation (ENSO)-neutral period. Interestingly, the diel maximum of OH reactivity during the El Niño event occurred at sunset instead of, under normal conditions, early afternoon. The absolute total diel OH reactivity, however, did not change significantly. Daytime OH reactivity averages were 24.3 ± 14.5 s−1 in 2012 and 24.6 ± 11.9 s−1 in 2015, respectively. Our findings suggest that a combination of stronger turbulent transport above the canopy with stress-related monoterpene and, possibly, other biogenic volatile organic compound (BVOC) emissions were responsible for the increased reactivity at sunset.

Ultra-Reliable and Low-Latency Vehicular Transmission: An Extreme Value Theory Approach

Considering a Manhattan mobility model in vehicle-to-vehicle networks, this work studies a power minimization problem subject to second-order statistical constraints on latency and reliability, captured by a network-wide maximal data queue length. We invoke results in extreme value theory to characterize statistics of extreme events in terms of the maximal queue length. Subsequently, leveraging Lyapunov stochastic optimization to deal with network dynamics, we propose two queue-aware power allocation solutions. In contrast with the baseline, our approaches achieve lower mean and variance of the maximal queue length.

Characterization of ten extreme disturbance events in the context of social and ecological systems

An extreme disturbance event is one in which any of its component disturbance forces and their interactions with affected systems have dimensions and responses that exceed the known range of variation expected of those parameters. If the exposed system does not respond or exhibits a low level response to an event, the event was not extreme to the exposed system, regardless of the dimensions of its disturbance forces. Extreme disturbance events are complex and require disaggregation to improve understanding of their effects. The areas affected by extreme events and the duration of the events are related but involve many orders of magnitude in terms of area affected and duration. One way to compare events is through a common and objective unit of measure such as energy. A comparison of ten extreme events in terms of their power and total energy delivered per unit area revealed a broad range of values among them. The power of events ranged 8 orders of magnitude and the total load per unit area ranged 14 orders of magnitude. Each event had different points of interaction with exposed ecosystems. When exposed to the same extreme event, the response of social systems is different from the response of the ecological systems. Also, social systems recovered quicker to a category 3 hurricane than did ecological systems. Both social and ecological systems have the capacity to evolve, adapt, innovate, and develop novelty in response to the selective pressure of extreme events.

The Benefits of Reduced Anthropogenic Climate changE (BRACE): a synthesis

Understanding how impacts may differ across alternative levels of future climate change is necessary to inform mitigation and adaptation measures. The Benefits of Reduced Anthropogenic Climate changE (BRACE) project assesses the differences in impacts between two specific climate futures: a higher emissions future with global average temperature increasing about 3.7 °C above pre-industrial levels toward the end of the century and a moderate emissions future with global average warming of about 2.5 °C. BRACE studies in this special issue quantify avoided impacts on physical, managed, and societal systems in terms of extreme events, health, agriculture, and tropical cyclones. Here we describe the conceptual framework and design of BRACE and synthesize its results. Methodologically, the project combines climate modeling, statistical analysis, and impact assessment and draws heavily on large ensembles using the Community Earth System Model. It addresses uncertainty in future societal change by employing two pathways for future socioeconomic development. Results show that the benefits of reduced climate change within this framework vary substantially across types of impacts. In many cases, especially related to extreme heat events, there are substantial benefits to mitigation. The benefits for some heat extremes are statistically significant in some regions as early as the 2020s and are widespread by mid-century. Benefits are more modest for agriculture and exposure to some health risks. Benefits are negative for agriculture when CO2 fertilization is incorporated. For several societal impacts, the effect on outcomes of alternative future societal development pathways is substantially larger than the effect of the two climate scenarios.

Photosynthetic productivity and its efficiencies in ISIMIP2a biome models: benchmarking for impact assessment studies

Simulating vegetation photosynthetic productivity (or gross primary production, GPP) is a critical feature of the biome models used for impact assessments of climate change. We conducted a benchmarking of global GPP simulated by eight biome models participating in the second phase of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP2a) with four meteorological forcing datasets (30 simulations), using independent GPP estimates and recent satellite data of solar-induced chlorophyll fluorescence as a proxy of GPP. The simulated global terrestrial GPP ranged from 98 to 141 Pg C yr−1 (1981–2000 mean); considerable inter-model and inter-data differences were found. Major features of spatial distribution and seasonal change of GPP were captured by each model, showing good agreement with the benchmarking data. All simulations showed incremental trends of annual GPP, seasonal-cycle amplitude, radiation-use efficiency, and water-use efficiency, mainly caused by the CO2 fertilization effect. The incremental slopes were higher than those obtained by remote sensing studies, but comparable with those by recent atmospheric observation. Apparent differences were found in the relationship between GPP and incoming solar radiation, for which forcing data differed considerably. The simulated GPP trends co-varied with a vegetation structural parameter, leaf area index, at model-dependent strengths, implying the importance of constraining canopy properties. In terms of extreme events, GPP anomalies associated with a historical El Niño event and large volcanic eruption were not consistently simulated in the model experiments due to deficiencies in both forcing data and parameterized environmental responsiveness. Although the benchmarking demonstrated the overall advancement of contemporary biome models, further refinements are required, for example, for solar radiation data and vegetation canopy schemes.

THE 2012 JULY 23 BACKSIDE ERUPTION: AN EXTREME ENERGETIC PARTICLE EVENT?

ABSTRACT The backside coronal mass ejection (CME) of 2012 July 23 had a short Sun-to-Earth shock transit time (18.5 hr). The associated solar energetic particle (SEP) event had a >10 MeV proton flux peaking at ∼5000 pfu, and the energetic storm particle event was an order of magnitude larger, making it the most intense event in the space era at these energies. By a detailed analysis of the CME, shock, and SEP characteristics, we find that the July 23 event is consistent with a high-energy SEP event (accelerating particles to gigaelectronvolt energies). The times of maximum and fluence spectra in the range 10–100 MeV were very hard, similar to those of ground-level enhancement (GLE) events. We found a hierarchical relationship between the CME initial speeds and the fluence spectral indices: CMEs with low initial speeds had SEP events with the softest spectra, while those with the highest initial speeds had SEP events with the hardest spectra. CMEs attaining intermediate speeds result in moderately hard spectra. The July 23 event was in the group of hard-spectrum events. During the July 23 event, the shock speed (>2000 km s−1), the initial acceleration (∼1.70 km s−2), and the shock-formation height (∼1.5 solar radii) were all typical of GLE events. The associated type II burst had emission components from meter to kilometer wavelengths, suggesting a strong shock. These observations confirm that the 2012 July 23 event is likely to be an extreme event in terms of the energetic particles it accelerated.