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A Bacia do Alto Paraguai (BAP) inclui a planície inundável do Pantanal e as áreas altas no entorno que drenam para o rio Paraguai, uma região de transição entre quatro grandes domínios fitofisionômicos da América do Sul – Cerrado, Chaco, Amazônia e Mata Atlântica. A fauna da região é pobre em endemismos, porém as posições central e transicional no continente conferem enorme riqueza de animais vertebrados, oriundos de diferentes domínios, especialmente quanto à ordem Chiroptera. Aqui, revisamos as espécies de morcegos registradas na BAP e apresentamos mapas de distribuição e breve descrição de cada espécie, incluindo dados de tamanho, forma, cor, ambientes, abrigos, hábito alimentar e interações com predadores e parasitas. Encontramos registro de 93 espécies em sete famílias – Emballonuridae (3), Noctilionidae (2), Mormoopidae (2), Phyllostomidae (51), Natalidae (1), Molossidae (20) e Vespertilionidae (14). Foram registradas 66 espécies na planície e 84 no entorno; com 59 espécies reportadas na planície e no entorno. Quase dois terços das espécies (63%) foram registrados em cinco ou mais localidades na BAP, e um quinto (19%) em apenas uma localidade. A fauna de morcegos da BAP destaca a grande importância dessa região para a manutenção da diversidade de morcegos na América do Sul.

In climate change attribution, unprecedented magnitudes of extreme events can be defined on the basis of thresholds in the pre-industrial distributions of event magnitudes. This notion of unprecedented levels of climate change impacts has been extended toward the lifetime exposure to extreme events by evaluating exposure frequency. Unprecedented exposure to extreme events is assessed by comparing average lifetime exposure under different climate scenarios to an upper percentile of exposure in a pre-industrial climate. Here we combine simulations of climate change impacts under different climate forcing scenarios with country-level demography datasets to estimate the fraction of the global population experiencing unprecedented exposure to extreme events. This is done for 29 global mean temperature trajectories taken from the AR6 scenario explorer and multiple extreme event categories such as heatwaves, floods and droughts. Further, we assess the age of emergence at which birth cohorts reach unprecedented levels of exposure.

Recent unprecedented extremes such as the 2023 New Year’s winter warm spell in Europe, the 2022 summer heat and dryness in China and Europe, the 2021 Pacific Northwest heatwave, the 2021 floods in northwestern Germany and the 2020 Siberian heat anomaly broke previous observed record intensities by large margins. Based on the observations up to the year before the event, some of these record-shattering extremes were inconceivable. Could the scientific community have better quantified the potential for such unseen events based on the current generation of climate models?Here, we demonstrate how a new ensemble boosting approach can be used to generate physically coherent storylines of such unseen events. In ensemble boosting a fully-coupled free-running climate model (CESM2) is used to develop physical storylines of very rare extremes. To this end, the most extreme events in an initial-condition large ensemble for the near future are re-initialized with slightly perturbed atmospheric initial conditions to efficiently generate events with magnitudes unprecedented in the observational record.We demonstrate that, with this approach, CESM2 can simulate events that e.g. substantially exceed the magnitude of the 2021 Pacific Northwest (PNW) heatwave anomaly. Even though the most extreme ensemble members were only selected based on the local temperature anomaly over the PNW region, they show a very similar temporal evolution and spatial pattern as the observed event. The associated 500hPa geopotential height anomaly is remarkably similar to the observed event with a strong anticyclone over PNW that is part of a wave pattern extending over much of the Northern Hemisphere. We further show that in some of the storylines pre-conditioning dry soils strongly contributed to the heatwave intensity, whereas in others, heatwaves of similar magnitude occur even at average land surface conditions.We further develop storylines for heatwaves in the Greater Paris and Chicago regions of much greater intensities than ever observed. Particularly the US Midwest, where no intensification of heatwaves has been observed in recent decades, has not experienced anything close to the heatwave intensities possible in the coming years.Finally, we demonstrate examples how ensemble boosting can also be used to generate storylines for multi-year drought events and large-scale heavy precipitation extremes and compare the findings to other storyline approaches. Event storylines based on ensemble boosting can be used in impact studies that require physical consistency across variables, e.g., for the evaluation of humid heatwaves or compound events, for assessing wildfire risks or for ecosystem modelling.

Fuelled by our changing climate, the summer of 2022 was one of the warmest on record, with numerous heatwaves and other weather extremes occurring around the globe. However, there is limited quantitative evidence on the contribution of human-induced warming to the weather-related health impacts observed in recent extreme weather events. We present a health attribution analysis of heat-related mortality attributable to human-induced climate change in the past summer of 2022 in Switzerland. We combined state-of-the-art methods in climate science and epidemiology with high-resolution mortality and temperature data to estimate the number and fraction of all-cause deaths that could be attributed to heat between June and August 2022. We, thus, estimated that 623 [95% CI: 151 - 1,068] all-cause deaths can be attributed to heat between June and August 2022, representing 3.5% [95% CI: 0.9-6.1] of total all-cause mortality during the same period. In a second step, we modelled counterfactual daily temperatures representing summer 2022 in absence of anthropogenic climate change. Specifically, four counterfactual daily mean temperature series were derived by subtracting the anthropogenic signal from the observed series which ranged between 1.19 and 2.75 ºC. Then, we quantified the hypothetical heat mortality burden in absence of climate change, and finally, estimate the contribution of climate change by subtracting it from the observed heat mortality impacts. We estimated that, in absence of an anthropogenic signal, the heat-related burden would have amounted to 1.4% [95% CI: -0.2 - 3.4] of all-cause mortality, corresponding to 253 deaths [95% CI: -27;594]. Thus, 2.1% [95% CI: 0.8 - 3.7] of the all-cause mortality in summer 2022 would have been avoided in absence of anthropogenic warming. This corresponds to 370 [95% CI: 133-644] deaths and 60% of the observed heat-mortality burden between June-August 2022. Females and the oldest age group were the most affected. Specifically, 60% of heat-related deaths attributed to climate change were in females (220 [69 - 393] vs. 150 [62 - 250] in males), and 90% in older adults (330 [129-565] vs. 39 [-5 - 84]). Our findings confirm that climate change is already affecting the health of the population in Switzerland by amplifying the heat-related mortality burden, with a stronger impact on females and older adults.

Global mean surface air temperature (GSAT) is a key diagnostic for understanding and constraining historical climate variability and change, and for climate policy. Yet, global temperature estimates (1) are usually based on blending sea surface temperatures (SST) with near-surface air temperature over land (LSAT), and (2) contain many missing values due to incomplete coverage in the historical record. While these issues are usually accounted for in model-observation comparisons, elucidating the consistency of LSAT and SST records, and their contribution to GSAT variability and change, remains difficult.Here, we present a set of new GSAT reconstructions based separately on either the historical LSAT or SST record. The method is based on regularized linear regression models trained on climate model simulations to optimally predict GSAT from the climate model’s LSAT or SST patterns, respectively. We then predict GSAT from the HadSST4 and CRUTEM5 observational data, respectively, for each month from January 1850 up to December 2020.We demonstrate that the land- or ocean based GSAT estimates show very similar variability and long-term changes, both in the early (1850-1900) as well as in the late record (post-1950). For example, GSAT of the past decade (2011-2020) increased by 1.15°C (LSAT-based) and 1.17°C (SST-based) relative to an early reference period (1850-1900), which is both well within IPCC AR6 estimates.However, the GSAT estimates show pronounced disagreement in the early 20th century (1900 up to around 1930), when the SST-based GSAT estimates appear on average around 0.3°C colder than the LSAT-based estimates. Decadal changes in the LSAT-based estimates are well explained by the multi-model mean of CMIP6 simulations driven with historical forcings, thus implying only a small role of unforced decadal global variability. In contrast, the SST-based estimate highlights pronounced variability during the early 20th century cold anomaly, which may be related to concerns about instrumental cold biases in SST measurements, but overall reasons for the disagreement remain unclear. Further analysis based on physical reasoning, climate models, and proxy reconstructions, indicates that the ocean data may indeed be implausibly cold.In conclusion, our methodology and results may help to constrain the magnitude of early 20th century warming, and thus to better understand and attribute decadal climate variability.

The amplitude of precipitation extremes across Europe is expected to increase through the 21st century under most climate change scenarios. Current coarse-resolution global climate models broadly project increased flooding and drought extremes; however, they often rely on parametrizations schemes or downscaling methods when simulating potential future extreme events. These methods often introduce errors leading to high levels of uncertainty for policy makers and infrastructure planning. The need for accurate extreme event predictions became further evident after the July 2021 floods and summer 2022 record-breaking heatwaves/drought across Western Europe.The ongoing H2020 Next Generation Earth Modelling Systems (NextGEMS) project aims to address these issues with the development of storm-resolving Earth-system models. Using the latest Cycle 2 runs from the Integrated Forecast System (IFS) from ECMWF and ICON from MPI-M at 4 km and 5 km horizontal resolution respectively, we examine the dynamical representation of extreme precipitation events across Europe and compare against similar analogues in the Copernicus European Regional Reanalysis (CERRA) and observational datasets. The 5.5 km horizontal resolution within CERRA allows for an analysis of precipitation characteristics in complex terrain like the Alpes. This approach is applied to examining atmospheric river dynamics such as integrated moisture transport behavior, precipitation distribution, and vertical structure of the low-level jet., The 28th IUGG General Assembly (IUGG2023) (Berlin 2023)

Primer: The record-shattering Pacific Northwest heatwave in late June 2021 challenged a key element of extreme event attribution, namely, the statistical evaluation of the event likelihood given historical records up to the event. The respective model, a non-stationary generalised extreme value (GEV) distribution depending on a global mean temperature covariate, suggested an infinite return period, or zero probability of reaching the event intensity in the year in which it was observed, based on the historical record. The apparent shortcoming of the method triggered a widespread debate about the general suitability of this statistical approach and its ability to provide informative insight in the context of extreme event attribution.Research objective: The aim of this study is to first evaluate the quality of return period estimates for very rare heatwave events to determine whether or not the method can reliably characterise the event likelihood of rare extremes. We then assess the contributions of different factors to systematic deviations in tail estimates (such as high quantiles or return periods) relevant for rare event attribution statements. We consider both aspects associated with the statistical method, as well as such related to the attribution procedure.Data & Methods: A robust evaluation of tail estimates requires vast amounts of homogeneous data. Our analysis is based on two transient historical and future (RCP8.5 and SSP3.7) initial condition large ensembles (84 and 100 members) and an extensive bootstrap dataset of extreme values simulated from parametric GEV distributions.Results: We demonstrate that also in climate model experiments, events analogous to the 2021 heatwave are simulated, which, assessed with data up to the event, would have deemed to have zero occurrence probability. Thus, also within the climate model context, we find that the non-stationary GEV approach yields substantially biased exceedance probability estimates for low-likelihood events, thereby overestimating the respective return period or underestimating the likelihood of occurrence if the GEV distribution is based on a relatively short “historical” record. This systematic has become particularly pronounced in recent extreme events due to the emergence of a distinct climate change signal and high rate of warming.Especially maximum likelihood estimates of the non-stationary GEV distribution are prone to systematically underestimate the shape parameter, and in consequence overestimate the return periods. We demonstrate that the bias arises because the GEV fit is restricted to rather short time series, and it is partially alleviated if a Bayesian estimation approach is used. Furthermore, widely used symmetric, so-called Wald-type maximum likelihood confidence intervals are found to be a rather inadequate and misleading measure of the estimation uncertainty in GEV-parameters and tail quantities like return levels. For these reasons, Wald-type confidence intervals should thus not be used for model evaluation purposes in extreme event attribution studies.

Extreme precipitation events cause major ecological and societal hazards (e.g. river flooding and landslides). Aggravatingly, climate change will likely exacerbate extreme precipitation events on a global scale. Scientifically even more concerning, however, are remaining major uncertainties in projections of regional extreme precipitation events, which hamper and complicate local adaptation measurements around the globe. The arguably largest source of these remaining uncertainties stems from climate models feuding about the dynamic evolution of atmospheric circulation; atmospheric circulation governs moisture transport and profoundly affects the location of extreme precipitation events as well as their severity around the globe. We exploit this feud among climate models to narrow down the arguably largest source of uncertainttiesin projections of extreme precipitation events through a storyline approach. For a given location, the approach: (i) unravels the properties of driving weather systems that repeatedly gave rise to extreme precipitation events in the past, (ii) assesses the fidelity of a large suite of models, participating in the Coupled Model Intercomparison Project 6 (CMIP6), in representing these driving weather systems in historical simulations (1950-2015), and (iii) constrains regional projections of extreme precipitation events based on the ascertained fidelity of models.Here, we present such a storyline approach that dynamically constrains regional projections of extreme precipitation events. Accompanying the outline of the approach, we identify regions that are particularly plagued by uncertain projections of extreme precipitation events and showcase for a subset of these regions how to track down weather systems that repeatedly gave rise to extreme precipitation events in the past.

Despite the high confidence in the overall intensification of extreme precipitation at global scale in response to warming, uncertainties in regional intensity and spatial distribution remain large. Changes due to thermodynamical processes are largely consistent across global climate models and account for the globally homogeneous increase in extreme precipitation, whereas changes in dynamical processes modify the regional responses of extreme precipitation and are also mainly responsible for the projected uncertainties. We here aim at developing and characterizing different storylines for regional changes in extreme precipitation.The latest CMIP6 multi-model ensembles allow for disentangling the three potential sources of uncertainty across climate models. Specifically, we find that scenario uncertainty in annual maximum precipitation (Rx1day) per K global warming is relatively small except over tropical Pacific and subtropical Africa regions. In order to isolate the inter-model uncertainty, which primarily relates to different responses in atmospheric dynamics, we here average multiple members from initial condition ensembles within CMIP6. To further characterize the dynamical processes and their effects on extreme precipitation, clusters of different model responses to climate change are identified for Mediterranean and Asian monsoon regions. The clusters are defined based on metrics that characterize regional dynamics. Different atmospheric fields are evaluated to develop physical storylines of future changes in extreme precipitation with model clusters representing similar dynamical responses. Specifically, an overall anticyclonic change is found to be characteristic for projections indicating highest reduction in Rx1day over Mediterranean. Future extreme precipitation changes over South Asia are linked to the models' representation of western coastal precipitation and orographic rainfall over the Himalaya. Other potential factors characterizing dynamical responses will be further examined to help understand changes in regional projections of extreme precipitation.

Because long-term observations are sparse and heavily affected by inter-annual variability, the detection of forced trends in land carbon uptake has been remarkably difficult. While changes in aggregated monthly or yearly carbon uptake have been heavily studied, changes in the diurnal cycle of carbon uptake remain uninvestigated. Here, we evaluate long-term changes in three-hourly Net Ecosystem Production (NEP) and the contribution of different times of the day to long-term changes in the terrestrial carbon sink. We first show that between 1950 and 2014, five CMIP6 models show a significant increasing trend in the diurnal NEP amplitude (DNA). Second, we show that DNA trends have a much higher signal-to-noise ratio compared to trends in NEP itself, and linearly scale with long-term annual NEP. Evaluating these model-based results against observations, we show that positive DNA trends are also found at a majority of observational eddy-covariance sites between 1990 and 2022. The positive correlation between DNA and long-term NEP is also confirmed by these observations. Our results reveal a widespread but previously undocumented emergent climate signal in terrestrial carbon exchange at the diurnal scale with the potential to serve as a new observational constraint for Earth system models.

Heat extremes show the fastest warming trend over western Europe (WEU) and a weak cooling trend over the Midwest United States (MUS). We use observations and Earth System Model (ESM) large ensemble simulations to understand why the observed trends over these two regions are opposite. Based on the dynamical adjustment method we provide observational and model evidence that circulation changes greatly amplify the warming trends over WEU and weaken the trends over MUS in the last four decades. We find that circulation-driven changes in heat extremes cause ~0.2°C/decade cooling over MUS that reverses the weaker warming effects of all other forcings combined and thus leads to very small overall trends. In contrast, it causes an additional ~0.2°C/decade warming over WEU, which accounts for ~35% of the warming rate that is caused by forced thermodynamic changes. Although ESMs represent the forced thermodynamic warming well over WEU, they underestimate the circulation-induced warming that further reveals why ESMs underestimate the observed warming rate over WEU.Overall, these findings imply that if the circulation changes represent a forced response WEU continues to experience a severe intensification of heat extremes conditions in future as circulation-induced amplification of heat extremes may further intensify in the warmer world. Conversely, if the circulation-induced trend were due to internal variability, and thus would reverse in the coming decades, this would imply a somewhat slower rate of warming of heat extremes. Moreover, heat extremes over MUS continue to warm more slowly if circulation keeps dampening the warming effects in the coming decades, but potentially increase rapidly if the circulation trend reverses.

Parts of western North America experienced a heatwave in late June 2021that many would have conceived impossible based on observations so far. In Lytton, Canada, temperatures peaked at 49.6°C, and the area-average daily maximum temperature record across the Pacific Northwest was broken by nearly 5°C. Given the exceptional intensity of the eventsome media outlets and scientists raised the questions whether heat extremes intensify faster than previously projected based on climate models, or whether current generations of climate models miss crucial processes and are thus unable to even reproduce such an event.. Here I address these questions and highlight some of the challenges for widely methods in model evaluation and attribution.First, I review some of the recent literature detailing the key physical mechanisms driving the Pacific Northwest heatwave. I address some of the key scientific challenges regarding the quantification of return periods, event attribution, model evaluation and near-term projections. Widely used methods estimating stationary return periods based on the observational record up to the year before imply that such an event had an infinite return period, i.e., that it would never happen. Even when taking into account the non-stationarity of a warming climate, the exceedance probability would be zeroor nearly zero depending on the estimation of the confidence intervals, the duration of the event and whether the event itself is included in the fit. I discuss some potential ways forward in addressing the above challenges and in quantifying the potential intensity of record-shattering events in the near future.

Recent extreme temperature and precipitation events such as the dry and heat events in summer 2022 in Europe and China, the New Year’s warm spell 2022/23 across Europe, the 2021 heavy rainfall extremes in northwestern Germany, Belgium and the Netherlands and the 2021 Pacific Northwest heatwave broke previous observed record levels by large margins. The probability of such unprecedented record-shattering extremes increases with the rapid rate of warming. Thus, there is a crucial need for analyzing the underlying processes leading to these events and quantifying potential intensities of events possible in the coming decades.Here, we evaluate how ensemble boosting (Gessner et al. 2021 and Gessner et al. 2022) can help assess the tail of climate distributions and generate climate model-based storylines more resource-efficiently. In ensemble boosting the most extreme simulated events in an intermediate-size initial condition ensembles are re-initialized in targeted experiments in order to efficiently sample very extreme states of the model climatology. Here, we evaluate different ensemble design choices including lead time, ensemble size and potential iteration choices to most efficiently allocate computational resources to simulate events of very extreme intensity.The resulting boosted events are analyzed through a storyline approach, thus helping to interpret the underlying mechanisms of each physically consistent unfolding extreme event and its consequences. The Pacific Northwest heatwave in June 2021 will be used as a starting point; but ensemble boosting and storylines can be powerful tools for understanding extremes beyond heat. We further discuss how ensemble boosting can also be applied to compound extremes and future climate scenarios.

It comes as no surprise that the future holds record-breaking weather and climate events. As global warming continues, temperature records will continue to be broken. Also heavy precipitation records are likely to be broken due to the increased water holding capacity of the atmosphere, in combination with changing atmospheric stability and circulation patterns. Improved estimates on the range of possible record-breaking precipitation events – now and in the future – are a first step to inform adequate adaptation policies for heavy precipitation. Of particular interest are events that break records by large margins – record-shattering events –, since these are likely to incur most damage and losses. In order to improve estimates of record shattering precipitation events in the present and future climate we use initial condition large ensemble simulation data (CESM2, SSP370) and statistical models. We evaluate record-shattering events in Rx1d (day with most precipitation per chosen time period (year or season)). In a stationary climate, the probability of Rx1d record-breaking is known to decrease with the number of data points since the start of measurements (inversely proportional). We find, however, that in our nonstationary climate, the decay in Rx1d record breaking and shattering probability is slowed down and even reversed in most world regions. Regional changes in record shattering probability are attributable to a changing underlying probability distribution of Rx1d, which also is region specific. We elucidate the contributions of changes in mean (distribution shift), and in variability (distribution widening/narrowing) to increasing record shattering probability by using a statistical model to create counterfactual realities representative of the regions of interest.We focus on regions of a size relevant for national and cross-border policy that show differently driven changes in record shattering precipitation probabilities. For example, the annual probability of a record shattering precipitation event somewhere in the Benelux-Germany region which was hit by severe floods in summer 2021 increases from ~2% now to ~4.5% at the end of the century in CESM2. This increase results from a non-linear interaction between mean and variability increases, and is primarily driven by increasing variability. At lower latitudes, for example in Central America, the effect of variability is even stronger, where we find increasing record shattering probability despite a negative long-term trend in Rx1d levels.Very unlikely events are, paradoxically, arguably the most important to know about, since their unimaginability often means that critical infrastructure is not sized to withstand these events. Our results may thus prove invaluable for regional policy.

As in most countries in Europe, the 2022 summer in Switzerland was exceptionally hot, only comparable to the infamous 2003 summer, and particularly devastating in terms of excess health burden. Here we combine methods in climate epidemiology and attribution to quantify the mortality associated with heat and the contribution of human-induced climate change in the 2022 summer in Switzerland. We estimate 623 deaths [95% empirical confidence interval (95% eCI): 151 - 1,068] due to heat between June-August 2022, corresponding to 3.5% of all-cause mortality. More importantly, we find that 60% of this burden (370 deaths [95% eCI: 133-644]) could have been avoided in absence of human-induced climate change. Older women were affected the most, as well as populations in western and southern Switzerland and more urbanized areas. Our findings demonstrate that human-induced climate change was a relevant driver of the exceptional excess health burden observed in Switzerland.

Over the last 70 years, extreme heat has been increasing at global scale [1,2], with a rapid rate in several regions including Western Europe [3]. Climate models broadly capture heat trends globally [1], but exhibit systematically weaker extreme heat trends than observations in Western Europe [4-6], together with a weaker summer warming [7,8]. The causes of this mismatch, confirmed here by the analysis of 273 latest generation coupled climate simulations, among which only a handful of them overpass observed trends, are not well understood. Here we use a circulation analogue approach [9,10] to identify the dynamical contribution to temperature trends [11-12], and show that a substantial fraction (1.0°C [0.4°-1.6°C] of 3.4°C per global warming degree) of the trend is due to circulation changes, largely due to increases in southerly flows over Western Europe. Their rapid increase in frequency (+63% [20%-106%] since 1950) and persistence (+45% [12%-77%]) are not captured by any of the 32 climate model flow simulations analyzed. The few simulations reaching the observed warming trends in extreme heat have weak dynamical changes, with a decrease in occurrence of southerly flows, indicating that they capture the warming trend for the wrong reasons. These model biases in circulation trends can be due to a systematically underestimated or erroneous representation of the circulation response to external forcing, or to a systematic underestimation of interdecadal variability, or both. The former implies that projections are too conservative, the latter that we are left with deep uncertainty regarding the pace of future summer heat in Europe: the current strong recent trend could weaken or increase in future decades. This calls for caution when interpreting climate projections of heat extremes over Western Europe, in particular in view of adaptation to heat waves.

Although richness and distribution of woody species in the Cerrado physiognomies have been extensively studied, the shifts of woody species from savanna physiognomies to dry forests have not yet been addressed. Here, we investigate the effect of soil physical-chemical traits on the woody species turnover between adjacent cerrado stricto sensu and dry forest physiognomies. Woody species were surveyed, and soil and topographic variables measured, in 30 10×40 m plots systematically distributed, with 15 plots in each physiognomy. We found a spatially structured distribution of woody species, and differences of soil traits between cerrado stricto sensu and dry forest areas, mainly related to the aluminum saturation, base saturation, and available phosphorus. Aluminum saturation increased toward the savanna area, while base saturation increased toward the dry forest. Most woody species predominated in one physiognomy, such as Callisthene major in the cerrado stricto sensu and Anadenanthera colubrina in the dry forest. Only 20% of the species were widely distributed across both physiognomies or, not often, restricted to the intermediary values of the soil gradient. General results indicate that contrasting soil traits between cerrado stricto sensu and dry forest produce a strongly spatially organized and sharp transition in terms of species distribution between these physiognomies. Resumo Embora a distribuição e a riqueza em espécies arbóreas nas fitofisionomias do Cerrado venham sendo bastante estudadas, a transição entre savanas e florestas deciduais ainda não foi abordada. Investigamos o efeito de características físico-químicas do solo sobre a distribuição de espécies arbóreas em região de contato entre cerrado sentido restrito e floresta estacional decidual (FED). As espécies arbóreas foram amostradas sistematicamente, e variáveis de topografia e características do solo foram medidas em 30 parcelas de 10×40 m, sendo 15 parcelas em cada fisionomia. A distribuição das espécies arbóreas foi espacialmente estruturada, e as características do solo diferiram entre as áreas de cerrado sentido restrito e FED, principalmente relacionadas à saturação de alumínio, saturação de bases e teores de fósforo. A saturação de alumínio aumentou em direção ao cerrado sentido restrito, enquanto a saturação de bases aumentou em direção à FED. A maioria das espécies arbóreas predominou em uma das fisionomias, como Callisthene major em cerrado sentido restrito e Anadenanthera colubrina em FED. Apenas 20% das espécies foram amplamente distribuídas em ambas as fisionomias ou, em poucos casos, restritas aos valores intermediários do gradiente de solo. Os resultados indicam um forte contraste de características do solo entre o cerrado sentido restrito e a FED, assim como uma transição acentuada e espacialmente organizada quanto à distribuição de espécies arbóreas.

Roundtable on Compound Hazards and COVID-19 What: An online panel with leading experts in compound hazard research, preparedness, and response, attended by over 80 online participants, met to discuss hazard response in the context of COVID-19. When: 30 June 2021 Where: Online, convened by the World Meteorological Organization and hosted by the American Geophysical Union Peer Reviewed "Article signat per 12 autors/es: Benjamin F. Zaitchik, Judy Omumbo, Rachel Lowe, Maarten van Aalst, Liana O. Anderson, Erich Fischer, Charlotte Norman, Joanne Robbins, Rosa Barciela, Juli Trtanj, Rosa von Borries, and Jürg Luterbacher"

Made available in DSpace on 2022-04-29T08:34:59Z (GMT). No. of bitstreams: 0 Previous issue date: 2021-01-01 Mites of the family Melicharidae comprise 13 genera and 220 species often associated with edaphic environments and found in association with insects, birds and mammals. Two genera, Proctolaelaps and Tropicoseius, have been recorded in Brazil in association with birds and bromeliads. In this study, we add new locality and host-association records for Proctolaelaps superagui and Tropicoseius braziliensis in the country. Laboratório de Coleções Zoológicas Instituto Butantan Departamento de Patologia Reprodução e Saúde Única Faculdade de Ciências Agrárias e Veterinárias-UNESP Laboratório de Ecologia Instituto de Biologia Universidade Federal de Mato Grosso do Sul Museu Biológico Instituto Butantan Independent Researcher Departamento de Entomologia e Acarologia ESALQ-Universidade de São Paulo Department of Entomology University of Arkansas Departamento de Patologia Reprodução e Saúde Única Faculdade de Ciências Agrárias e Veterinárias-UNESP

The evolution of bat skull has been extensively studied at a broad scale. However, we know little about how phenotypes that evolved in response to selective pressure elsewhere interact with ecological pressures to determine species co-ocurrence in human-modified landscapes. Here, we test the influence of size, diet, and foraging strategy on the skull and jaw shape of 32 co-occurring bat species in an agroecological landscape from southwestern Brazil. We used 2D geometric morphometrics for skull and jaw and phylogenetic comparative methods to integrate morphology, ecology, and phylogeny data. Our results show that closely related species were clustered together in the morphospace. Likewise, ecology and phylogeny influenced the cranial and jaw shape. Groups with greater phenotypic disparity were animalivores and insectivores. The skull of frugivores and animalivores were larger than that of insectivores and had a common and positive allometric relationship, probably related to olfactory-visual senses. Apparently, coexistence in Stenodermatinae frugivores is possible because they have optimal phenotypes for exploration food resources related to the skull and jaw size. Conversely, insectivores that clustered together in the morphospace had differences in foraging strategy or size.

Heat-related mortality has been identified as one of the key climate extremes posing a risk to human health. Current research focuses largely on how heat mortality increases with mean global temperature rise, but it is unclear how much climate change will increase the frequency and severity of extreme summer seasons with high impact on human health. In this probabilistic analysis, we combined empirical heat-mortality relationships for 748 locations from 47 countries with climate model large ensemble data to identify probable past and future highly impactful summer seasons. Across most locations, heat mortality counts of a 1-in-100 year season in the climate of 2000 would be expected once every ten to twenty years in the climate of 2020. These return periods are projected to further shorten under warming levels of 1.5°C and 2°C, where heat-mortality extremes of the past climate will eventually become commonplace if no adaptation occurs. Our findings highlight the urgent need for strong mitigation and adaptation to reduce impacts on human lives.

Most Asteraceae species are pollinated by insects, mainly bees and butterflies, although pollination by birds has been documented and pollination by bats has been suggested for some species. Here, we investigated the pollination of Gongylolepis martiana, a species supposedly pollinated by bats. We assessed floral traits and visitors in a population of G. martiana in the Brazilian Amazon, measuring pollen removal from anthers and deposition on stigmas by diurnal and nocturnal visitors. Florets opened at dusk and lasted for 4 days, with the male phase starting on the first night and the female phase on the third night. Accumulated nectar per capitulum was 69.6 μl per night and sugar concentration was 15%. Nectar-feeding bats and hummingbirds contacted the sexual parts, but pollen removal and deposition were greater throughout the night than during the day, when Meliponini bees considerably reduced pollen availability. Other nocturnal visitors of G. martiana were rare, including nocturnal bees and moths that foraged for pollen and nectar, respectively. Our results support that nectarivorous bats are the main pollinators of G. martiana, confirming Vogel's hypothesis of bat pollination in Asteraceae, particularly in the genus Gongylolepis. Since anthesis and each sexual floral phase started in the evening, nectarivorous bats and diurnal bees caused additive priority effects, preventing hummingbirds from being efficient pollinators. The high density of flowering individuals of G. martiana in patches from white-sand forests likely increases bat attraction, while the small amount of nectar per plant favours cross-pollination.

Storylines provide a way to communicate climate change and especially changes in extreme events in a tangible way for the public and decision makers by putting the focus on associated risks and impacts. In this work, storylines are created for the 2003 summer heatwave in Switzerland. For this purpose, an analogous event is searched for in the already existing Climate Scenarios for Switzerland (CH2018). This event shall be as extreme in the future climate as 2003 was in its time. Building storylines for this future extreme event, we demonstrate how a 2003-like summer might be like by the end of the Century assuming no climate change mitigation. Specific indicators are chosen to develop four individual heat-related storylines and enabling a direct comparison between 2003 and the future hot summer. For the future summer, the number of hiking weather days in the Alps is projected to increase on the one hand, while on the other hand also fire danger increases massively. More and longer heatwaves may aggravate heat-related health issues and additional generations of agricultural pests could threaten agricultural yields.With these storylines of a similar future heatwave, we gain a better understanding of locally relevant processes, get insight into how extreme events quantitatively change with the warming climate, give examples of possible impacts, and finally try to stimulate public awareness for possible consequences of future climate change. We explore different ways of visualizing and communicating the results, which may find broader application to inform Swiss stakeholders about future climate change and its possible impacts.

Functional traits can determine pairwise species interactions, such as those between plants and pollinators. However, the effects of biogeography and evolutionary history on trait-matching and trait-mediated resource specialization remain poorly understood.We compiled a database of 93 mutualistic hummingbird-plant networks (including 181 hummingbird and 1,256 plant species), complemented by morphological measures of hummingbird bill and floral corolla length. We divided the hummingbirds into their principal clades and used knowledge on hummingbird biogeography to divide the networks into four biogeographical regions: Lowland South America, Andes, North & Central America, and the Caribbean islands. We then tested: (a) whether hummingbird clades and biogeographical regions differ in hummingbird bill length, corolla length of visited flowers and resource specialization, and (b) whether hummingbirds' bill length correlates with the corolla length of their food plants and with their level of resource specialization.Hummingbird clades dominated by long-billed species generally visited longer flowers and were the most exclusive in their resource use. Bill and corolla length and the degree of resource specialization were similar across mainland regions, but the Caribbean islands had shorter flowers and hummingbirds with more generalized interaction niches. Bill and corolla length correlated in all regions and most clades, that is, trait-matching was a recurrent phenomenon in hummingbird-plant associations. In contrast, bill length did not generally mediate resource specialization, as bill length was only weakly correlated with resource specialization within one hummingbird clade (Brilliants) and in the regions of Lowland South America and the Andes in which plants and hummingbirds have a long co-evolutionary history. Supplementary analyses including bill curvature confirmed that bill morphology (length and curvature) does not in general predict resource specialization.These results demonstrate how biogeographical and evolutionary histories can modulate the effects of functional traits on species interactions, and that traits better predict functional groups of interaction partners (i.e. trait-matching) than resource specialization. These findings reveal that functional traits have great potential, but also key limitations, as a tool for developing more mechanistic approaches in community ecology.A free Plain Language Summary can be found within the Supporting Information of this article.

Recent theories suggest that processes shaping species diversity are the same shaping genetic diversity, leading to a correlation between the two levels of diversity. Using Neotropical bat assemblages, and considering the genetic diversity of two co-distributed Chiroptera species with distinct life-history traits, Artibeus planirostris and Carollia perspicillata, we evaluated the correlation between metrics of taxonomic (species richness, evenness, and community divergence) and genetic diversity (number of haplotypes, haplotype diversity, allelic richness, expected heterozygosity and genetic divergence) in these two species. Landscape variables potentially affecting those correlations (distance to the nearest conservation unit, forest area, forest border length and number of forest fragments) were also analysed. For A. planirostris, we found a negative correlation between evenness and expected heterozygosity and also between mean presence-absence assemblage divergence and mean genetic divergence based on microsatellites. Our results indicate that the genetic diversity in A. planirostris is not explained by the landscape variables considered. For C. perspicillata, we found a positive correlation between species richness and haplotype richness and between evenness and expected heterozygosity. Genetic differentiation based on microsatellites in C. perspicillata was positively related to geographic distance and landscape differentiation. Allelic richness and expected heterozygosity in C. perspicillata were negatively related to distance from the conservation unit and forest area, but significance changed according to the spatial scale. We conclude that species-genetic diversity correlations may vary according to the species under study. Thus, A. planirostris appears to be ecologically different to other species in assemblages, while C. perspicillata seems to be ecologically similar.

We report on phyllostomid assemblages just after a wildfire and again three months later across burned and unburned forest patches. We recorded 10 species throughout the fire disturbance gradient, mainly determined by changes in the understory. Burned patches presented high abundance of predator bats right after fire, including gleaning insectivorous, carnivorous, and sanguivorous. Three months later, burned forests were empty of predators and dominated by large frugivores that occurred throughout the whole gradient in both periods. The fire appears to create ephemeral opportunity to predator phyllostomids right after its passage, but subsequent vegetation recovery seems to reduce diversity in burned forests.

Global-mean surface air temperature (GSAT) is a key diagnostic of climate change and a key metric for climate policies. Yet, global temperature datasets (1) are usually based on blending the sea surface temperature (SST) record with land surface air temperature (LSAT) data, and (2) contain a large number of missing values due to incomplete coverage, particularly in the early record. These issues are usually accounted for in model-observation comparisons via a similar processing of models and/or statistical infilling, but an apple-to-apple comparison between LSAT and SST records in their contribution to GSAT estimates, and also in their spatial consistency, remains difficult. Here, we present a set of novel GSAT estimates based separately on either the LSAT or SST record, and climate model information. The method is based on regularized linear regression models that are trained on climate model simulations to optimally predict GSAT from the climate model’s LSAT or SST predictors, respectively, which are masked to match the observational coverage at any given time step. In a second step, the derived statistical models are used to predict GSAT from the HadSST4 (SST) and CRUTEM5 (LSAT) observational data, respectively, and for any observational coverage from January 1850 up to December 2020. In addition, we generate a variant of these estimates that explicitly take into account the estimated errors as well as bias realizations for HadSST4 and CRUTEM5 data in the GSAT prediction. We show that the resulting independent, land- and ocean based GSAT estimates are remarkably consistent since around 1950: the squared correlation between the land- and ocean GSAT estimates is 0.98 for annual and 0.92 for monthly data, whereas it is only 0.94 (annual) and 0.77 (monthly), for the original CRUTEM5 and HadSST4 global land and ocean time series. In addition, the 1950-2020 long-term trends in GSAT estimates are virtually identical when inferred independently from land- or ocean data (1.14°C or 1.17°C warming per 71 years, respectively), and GSAT of the past decade (2011-2020) increased by 1.18°C (LSAT-based) and 1.15°C (SST-based) relative to an early period (1850-1880). However, the GSAT estimates show a pronounced period of disagreement in the early 20th century (1900 up to around 1920), when the SST-based GSAT estimates appear up to about 0.5°C (0.3°C on average) colder than the LSAT-based estimate, with important implications for the magnitude of the early 20th century warming. This finding is consistent with concerns about biased observed estimates raised in the literature, and is potentially related to instrumental cold biases in SST measurements, but overall reasons for the disagreement remain largely unclear. We show several lines of evidence, based on statistical analysis, physical reasoning and comparison with climate models, which indicate that the ocean data may indeed be implausibly cold. However, the early 20th century ocean cold anomaly, as well as the associated strong early 20th century ocean warming, require further study.

The Pacific Northwest heat wave is one of a series of record-shattering heat extremes that, based on the previous observational record, may have been deemed impossible. Here we address the question of whether the potential for such an extreme heat wave could have been foreseen using simulated physical climate storylines.We use a novel approach, called ensemble boosting, in which a fully-coupled free-running climate model (CESM2) is used to develop physical storylines of very rare heat extremes under present-day conditions. In ensemble boosting, the most extreme events in an initial-condition large ensemble for the near future are re-initialized with slightly perturbed atmospheric initial conditions to efficiently generate events that are even more extreme, with the goal of sampling events with magnitudes that have not been seen before.We demonstrate that, with this approach, CESM2 can efficiently simulate events that reach or even exceed the magnitude and duration of the 2021 Pacific Northwest heatwave anomaly. The atmospheric circulation anomalies associated with the most extreme simulated heat waves in the boosted ensemble are remarkably similar to the observed event. We further evaluate the anomalies in the surface energy and water budgets that contribute to the most intense simulated events. We conclude that based on this approach, heat waves unseen in the observational record can be simulated in models, at least in some regions. After probing this approach for the Pacific Northwest heatwave, we apply it to other mid-latitude regions where extreme heat events of much higher magnitude than has been observed are plausible in the near future.The ensemble boosting approach is computationally efficient, and it preserves physical consistency both in time, in space and across variables. This has the major advantages that the drivers can be directly evaluated against observed events and the generated storylines can be used in impact studies that require physical consistency, e.g. for the evaluation of humid heatwaves or compound events, for assessing wildfire risks or for ecosystem modelling.

Research on extreme weather typically investigated the physical and dynamical processes involved in the formation of specific meteorological events that occur on time scales of hours to a several days (e.g., heavy precipitation events, windstorms, heat waves). Such events can be extremely hazardous, but for certain socioeconomic sectors the seasonal aggregation of weather is particularly harmful. These sectors include, for instance, agriculture, forestry, energy, and reinsurance. This presentation introduces the concept of “extreme seasons” as an important and not yet thoroughly investigated research field at the interface of weather and climate science. Extreme seasons are defined as seasons during which a particular meteorological or impact-related parameter (or a combination thereof) strongly deviates from climatology. An important conclusion of the presentation will be that large ensemble climate simulations (here using an extended CESM1-LENS data set with 6-hourly output of 3D fields), with about 1000 simulated years per climate period, are an essential resource enabling novel quantitative insight into the processes leading to and characteristics of extreme seasons. The presentation provides examples for the identification of extreme seasons and emphasizes the importance of studying their substructure, including the occurrence of specific weather systems. A first approach to systematically study extreme seasons is to consider the top 10 seasons (for a given metric) in the large ensemble at every grid point, e.g., the 10 wettest winters or hottest summers, or the 10 summers with the largest vapour pressure deficit (as an example for a more impact-related metric). Alternatively, one can look at anomalies in a multi-dimensional parameter phase space, identifying extreme seasons that result from a highly unusual combination of, e.g., surface temperature, precipitation, and surface energy balance. Or, using a pragmatic method based on fitting a statistical model to seasonal mean values at each grid point, spatially coherent extreme season objects can be identified that exceed a local return period threshold of, e.g., 40 years. The same statistical approach can be applied to ERA5 reanalyses to compare characteristics of extreme season objects (e.g., their size and intensity) in climate models with observation-based data. With this approach we can meaningfully estimate how often, e.g., an observed extreme winter like the cold North American 2013/14 winter is expected anywhere in midlatitude regions. The last part of the presentation addresses the substructure and weather system characteristics of extreme seasons. Illustrative results are shown that address the questions: (i) Where are extremely hot summers the result of the warmest days being anomalously hot vs. the coldest days being anomalously mild? (ii) Where are wettest seasons the result of more frequent wet days vs. more intense precipitation on wet days? and (iii) How does the frequency of weather systems and their precipitation efficiency change during the wettest seasons? The answers to these questions reveal interesting and large regional differences.

Primer: The recent Pacific Northwest heatwave in June 2021 is widely considered a prime example of a record shattering low-likelihood extreme event, exceeding previous annual temperature maxima by large margins. The event intensity was generally perceived to be far beyond what was to be expected from historical data. It has been argued that the event would have been deemed essentially impossible, i.e. having an infinite return period, if estimated based on the historical record, even when taking the warming trend into account. This raises the question whether the non-stationary extreme value modelling approach, a widely used probabilistic framework applied to assess the likelihood of such extremes, yields systematically biased estimates determining the tail characteristics of the distribution.Research objective: We here aim at understanding why the intensity of the event exceeds the upper bound of the estimated distribution when only using data up to the year before the event. We quantify the contribution of a multitude of factors for a generalized extreme value distribution GEV with a non-stationary parametrization to be too conservative in the characterisation of tail events, especially in the context of heatwaves. We analyse how physical properties of heat extremes materialise in statistical effects contributing to potential biases in the GEV parameter estimation, as well as some inherent deficiencies of the GEV in its application to heat extremes with limited sample size due to asymptotic properties.Data & Methods: In order to test the respective hypotheses, we analyse climate model output of single model initial condition large ensembles (SMILEs), primarily an ensemble of 84 transient historical and RCP8.5 simulations performed with the Community Earth System Model CESM1.2. The results are further verified using additional CMIP6 models and ERA5 reanalysis.Preliminary results and outlook: We find that non-stationary return period estimates tend to be systematically biased high when estimated on the historical records up to a year before a record-shattering event, which is a standard practice in applications of this framework. We here disentangle the reason responsible for potential biases in the estiamtes. We find that even in case of stationary extremes, the asymptotic nature of the GEV distribution applied to finite data favours an underestimation of the shape parameter, which has substantial effects on the characterisation of the tail, inducing biases in estimates of widely used tail measures (exceedance probabilities, return periods), and derivatives thereof (risk ratios, fraction of attributable risk). The conditional effects of non-stationary components like global warming on heatwave intensity are potentially further underestimated due to internal variability and noise in the covariates. In the light of these shortcomings, we provide evidence for an improvement of the GEV framework by learning from climate model output about the effect of further process variables (high pressure patterns and soil moisture deficiencies).

Recent controversies about the climatic consequences of forestation and deforestation have centered on the carbon storage potential of forests and the local or global thermodynamic impacts due to biogeophysical effects. So far, not much attention has been given to the changes that biogeophysical effects of forestation and deforestation impose on the atmospheric and ocean circulation and consequently on remote weather and climate. Here we discuss how the changes in the Earth's energy balance following global-scale forestation and deforestation alter the global atmospheric circulation patterns and even have profound effects on the ocean circulation. We perform multicentury coupled climate model simulations in which preindustrial vegetation cover is either completely forested or deforested and carbon dioxide mixing ratio is kept constant. Forestation leads to global warming of +0.5 K, which is most pronounced over northern extratropical land. Consequently, the meridional heat transport in the Northern Hemisphere decreases in the forestation simulation. The reduction mainly occurs in the ocean as a result of a weakened Atlantic meridional overturning circulation (AMOC). Extratropical land-warming results further in weaker and poleward shifted weather systems, which, via momentum feedback to the mean flow leads to an attenuation and poleward displacement of the extratropical jet stream. Deforestation leads to global cooling of -1.6 K, a stronger AMOC and extratropical jet stream, a southward shift of the intertropical convergence zone and a stronger Hadley cell in boreal winter, and a weaker Hadley cell in boreal summer. In many aspects, deforestation causes the reverse patterns compared to forestation but with larger amplitudes. These larger amplitudes are mostly related to a strong snow-ice-albedo feedback in high latitudes. Both land surface changes substantially affect regional precipitation, temperature, and surface wind patterns across the globe. The design process of large-scale forestation projects thus needs to take into account global circulation adjustments and their influence on remote climate.

info:eu-repo/semantics/publishedVersion

Although critical in understanding human societies relying on natural game stocks, little attention has been paid to how socioeconomic traits can influence hunter behaviour. Our research focuses on whether village size, household size and age, and hunter age and monetary income affect hunting efficiency (catch-per-unit-effort) and catchment areas of traditional Amazonians. In collaboration with 13 volunteer hunters from six villages, we assessed social traits, and identified hunting areas with GPS and animal kills over six months. Contrary to expectations from central-place foraging, hunters in larger villages used smaller catchment areas, potentially because cassava-associated game species are more common near larger villages. Older hunters were more efficient, emphasizing the role of experience gained through time. Catchment areas increased with hunters’ income, but apparently the hunting efficiency did not. Overall results support the notion that a spatial arrangement of fewer large villages, rather than many small ones, maximizes hunting efficiency and minimizes catchment areas.

The consumption of fruits and floral resources, as core or complementary food items, occurs in 75% of phyllostomid species. If phylogenetically related bat species have similar plant species in their diets, then it is expected that vegetal resources composition influences the phylogenetic structure of phyllostomid assemblages. We ask here if the phylogenetic structure of phyllostomid assemblages is associated with vegetation structure and resources. Our results showed that proportions of plant sources consumed by phyllostomids in the Pantanal wetland have phylogenetic signal, and that variation of the available vegetal resources influences the phylogenetic structure of phyllostomid assemblages. Considering the availability of their principal food resources, the two major phyllostomid clades (Phyllostominae and Stenodermatinae) responded in opposite ways to the vegetal resources gradient, formed by plant species with distinct adaptations to inundation. Our results indicate that the use of different habitats based on variation of vegetal resources is the main driver of phylogenetic structure of phyllostomid assemblages in the Pantanal wetland. O consumo de frutos e de recursos florais como itens principais ou complementares da dieta ocorre em 75% das espécies de filostomídeos. Se morcegos filogeneticamente mais próximos apresentam maior similaridade de espécies de plantas em suas dietas, espera-se que a composição de recursos vegetais influencie a estrutura filogenética das comunidades de filostomídeos. Perguntamos neste estudo se as comunidades de filostomídeos são filogeneticamente estruturadas em resposta à variação da disponibilidade de recursos e da estrutura da vegetação. Os resultados mostraram que a porcentagem de recursos vegetais consumidos pelos filostomídeos no Pantanal apresenta sinal filogenético, e que a variação da composição dos recursos disponíveis influencia a estrutura filogenética das comunidades. Em resposta à disponibilidade de seus principais recursos alimentares, os dois principais clados de filostomídeos (Phyllostominae e Stenodermatinae) respondem de forma oposta ao gradiente de composição de recursos vegetais, formado por espécies de plantas com diferentes adaptações à inundação. Nossos resultados indicam que o uso de diferentes hábitats, baseado na variação dos recursos vegetais, é o principal direcionador da estrutura filogenética das comunidades de filostomídeos no Pantanal.