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Anthropogenic climate change is affecting rivers worldwide, threatening water availability and altering the risk of natural hazards. Understanding the pattern of regional streamflow trends can help to inform region-specific policies to mitigate and adapt to any negative impacts on society and the environment. We present a benchmark dataset of long, near-natural streamflow records across Aotearoa New Zealand (NZ) and the first nationwide analysis of observed spatiotemporal streamflow trends. Individual records rarely have significant trends, but when aggregated within homogenous hydrologic regions (determined through cluster analyses), significant regional trends emerge. A multitemporal approach that uses all available data for each region and considers trend significance over time reveals the influence of decadal variability in some seasons and regions, and consistent trends in others. Over the last 50+ years, winter streamflow has significantly increased in the west South Island and has significantly decreased in the north North Island; summer streamflow has significantly decreased for most of the North Island; autumn streamflow has generally dried nationwide; and spring streamflow has increased along the west coast and decreased along the east coast. Correlations between streamflow and dynamic and thermodynamic climate indices reveal the dominant drivers of hydrologic behavior across NZ. Consistencies between the observed near-natural streamflow trends and observed changes in circulation and thermodynamic processes suggest possible climate change impacts on NZ hydrology.

Understanding precipitation variability is challenging, particularly in regions such as New Zealand, where the local topography strongly controls precipitation. This study provides a comprehensive evaluation of mean and extreme precipitation features over Aotearoa, New Zealand, using six merged satellite-gauge, five reanalysis, and three in-situ products, using station data as a reference. We find that all products show similar features in depicting the mean precipitation pattern with a clear maximum over the Alpine regions and a strong west-east gradient across the South Island. However, there are differences in the magnitude of mean precipitation estimates amongst different products at various regions on a seasonal timescale. Investigating the frequency of wet days shows that GPCP has the lowest count of all the satellite-based products, likely due to its coarse grid size, while MSWEP depicts the highest frequencies over the wettest region. Similarly, for reanalysis, MERRA-TP likely overestimates the frequency of occurrence west of the Alps compared to MERRA-PCORR, although the former showed better similarity with other datasets in terms of mean precipitation pattern. Moreover, statistical tests such as the Pearson correlation coefficient of the spatiotemporal pattern revealed that amongst the satellite-based products, MSWEP and GPM-IMERGE outperform other products with values of 0.9 and 0.66 with a mean wet-bias of 0.37 and 0.89 mm/day over the entire country. At the same time, ERA-5 and BARRA-R perform better in the suite of reanalysis products with a mean correlation coefficient of 0.87 and 0.74 with a mean wet-bias of 0.43 and 0.76 mm/day, respectively. This presentation also incorporates a set of precipitation indices approved by the ETCCDI committee to facilitate an intercomparison of different products in capturing the extreme tail of the distribution. Substantial differences especially over the West Coast in the South Island were observed in the interannual variability of the indices among different products. A closer examination of the percentile-based indices such as R95P and R99P revealed a contrasting pattern between different products in geographical regions. For instance, all the satellite-based products consistently showed wet bias as compared to the reanalysis products that depicted dry bias in all seasons. The MSWEP and BARRA-R datasets had the smallest relative percentage difference compared with the station data for most of the indices, suggesting their potential use for capturing both the mean and extremes characteristics of precipitation quite well in this region.

Recent climate change is characterized by rapid global warming, but the goal of the Paris Agreement is to achieve a stable climate where global temperatures remain well below 2°C above pre‐industrial levels. Inferences about conditions at or below 2°C are usually made based on transient climate projections. To better understand climate change impacts on natural and human systems under the Paris Agreement, we must understand how a stable climate may differ from transient conditions at the same warming level. Here we examine differences between transient and quasi‐equilibrium climates using a statistical framework applied to greenhouse gas‐only model simulations. This allows us to infer climate change patterns at 1.5°C and 2°C global warming in both transient and quasi‐equilibrium climate states. We find substantial local differences between seasonal‐average temperatures dependent on the rate of global warming, with mid‐latitude land regions in boreal summer considerably warmer in a transient climate than a quasi‐equilibrium state at both 1.5°C and 2°C global warming. In a rapidly warming world, such locations may experience a temporary emergence of a local climate change signal that weakens if the global climate stabilizes and the Paris Agreement goals are met. Our research demonstrates that the rate of global warming must be considered in regional projections.

The issue of climate related loss and damage (L&D) has re-emerged and gained significant traction in international climate policy in recent years. However, many aspects remain unclear, including how aspects of liability and compensation in relation with L&D will be treated under the UNFCCC, human rights and environmental law. Furthermore, the type of scientific evidence required to link climate change impacts for each of these L&D mechanisms needs to be clarified. Here we analyze to which degree different types of scientific evidence can inform L&D discussions and policies. We distinguish between (i) L&D observation, (ii) understanding causation, and (iii) linking L&D to anthropogenic emissions through attribution studies. We draw on three case studies from Australia, Colombia and Alaska to demonstrate the relevance of the different types of evidence. We then discuss the potential and limitations of these types of scientific evidence, in particular attribution, for informing current L&D discussions and policies. Attribution (iii) sets the highest bar, but also provides the most complete set of information to support adaptation, risk reduction and L&D policies. However, rather than suggesting that attribution is a necessary requirement for L&D policies we want to highlight its potential for facilitating a more thematically structured, and thus hopefully a more constructive, policy and justice discussion.

This study classifies maximum air temperature patterns over West Africa into sixgroups and evaluates the capability of a global climate model (Community Atmospheric Model version 5.1; CAM) to simulate them. We analyzed 45-year (1961–2005) multi-ensemble (50 members) simulations from CAM and compared the results with those of the Climate Research Unit (CRU) and the twentieth Century Reanalysis data sets.Using Self Organizing Map algorithmto classify the spatial patterns of maximum air temperature during boreal spring, the study revealsthe temperature patterns that CAM can simulate welland those the modelstruggles to reproduce. The results show that the best agreements between the composites of observation and CAM occur in the first temperature patterngroup (which features positivetemperaturesanomalies over theSahel) and Node 2 (which featuresnear-normal temperature) pattern of the third group. CAM succeeded in reproducing some of the associated regional atmospheric dynamics and thermodynamic features in winds(horizontal and vertical), temperature fields, the cloud fractions, and the mean sea-level pressure. Although CAM struggles to capture the relationship between air temperaturepatterns and tele-connection indices during the boreal spring season over West Africa, it agrees with observations that temperature patterns over the sub-region cannot be associated with a single climate index. Anensemble member (SIM48) captures the inter-annual variation of the observed temperaure patterns with high sycronization (ɳ > 44%), much better than that ofensembles mean (ɳ < 30%). SIM48 also captures adequately four of the spatial patterns in comparison to three captured by the ensembles mean. This indicates that, for better seasonal forecasts and more reliable future climate projections, the practice whereby an ensemble mean is based on uniformly averaging the members rather than the performance of individual ensemble members needs to be reviewed. The results of the studymay be used to improve the perfomance of CAM over West Africa, therebystrengthening the on-going efforts to include CAM as part of multi-model forecasting system over West Africa.

This paper describes five sets of regions intended for use in summarizing extreme weather over Earth’s land areas from a climate perspective. The sets differ in terms of their target size: ∼10 Mm2, ∼5 Mm2, ∼2 Mm2, ∼0.5 Mm2, and ∼0.1 Mm2 (where 1 Mm2= 1 million km2). The regions are based on political/economic divisions, and hence are intended to be primarily aligned with geographical domains of decision-making and disaster response rather than other factors such as climatological homogeneity. This paper describes the method for defining these sets of regions; provides the final definitions of the regions; and performs some comparisons across the five sets and other available regional definitions with global land coverage, according to climatological and non-climatological properties.

Atmospheric rivers (ARs) are critical to the hydrological cycle of the western United States with both favorable and formidable impacts to society based on their landfalling characteristics. In this study, we provide a first-of-its-kind evaluation of how landfalling ARs may respond to several stabilized warming scenarios. To do this we combine a recently developed AR detection workflow with an ensemble of uniform high-resolution (0.25°) Community Earth System Model simulations designed to facilitate detection and attribution of extreme events with global warming. These simulations include a world that might have been in the absence of anthropogenic warming (+0◦C), a world that corresponds to present day warming (+0.85◦C), and several future worlds corresponding to +1.5◦C, +2◦C and +3◦C global warming. We show that warming increases the number of water management relevant landfalling ARs from 19.1 ARs per year at +0◦C to 23.6 ARs per year at +3◦C. Additionally, this warming intensifies the amount of water transported by landfalling ARs resulting in a decrease in the fraction of ARs that are “mostly to primarily beneficial” to water resource management (i.e., 91% of ARs at +0◦C to 78% at +3◦C) and an increase in the fraction of ARs that are “mostly or primarily hazardous” to water resource management (i.e., 2% of ARs at +0◦C to 8% at +3◦C). Shifts in AR character also have important ramifications on flood damages, whereby for every +1◦C of additional warming from present conditions annual average flood damages increase by ~$1 billion. These findings highlight the pragmatic implications of climate mitigation aimed at limiting global warming to under +2◦C.

Physical, biological, and human systems in mountain regions are highly sensitive to climate change due to strong feedbacks and low resilience. Detection of changes and attribution of them to climate and non-climate drivers provides ongoing monitoring of complex interactions of coupled natural and human systems and improving scientific assessments that inform mitigation and adaptation practices. In the IPCC 5th Assessment Report published in 2014, Central and South America was the region with the least evidence available for detection and attribution (D&A) of climate change impacts. Since then, much more evidence has accumulated due to an increasing number of studies detecting impacts in the Andean region. In this study, we therefore performed a systematic literature review of climate change impacts and made a local D&A expert impact assessment for a total of 12 natural and human systems in the Andes. We found the following confidence levels of detection and attribution of each impact for each system: medium and high, respectively, for energy; high and high, for snow and ice, tourism, and cultural values; high and medium for terrestrial and aquatic ecosystems, disasters, human health and migration; and medium and medium for agriculture and water systems. A total number of 65 sample impacts (in aggregate or case study form) could be attributed to climate change. Climate change was especially important in glacio-hydrological systems (49%) and terrestrial ecosystems (15%). Among the impacts that could be attributed to climate change with high confidence, snow and ice system dominated. About half of the total impact samples were attributed with medium confidence, of which 35% corresponded to water systems and 16% to agriculture. Finally, 14% of all impacts were assessed with low attribution confidence. Important results include: (1) glacier retreat leads to important cascading effects affecting most of the systems in the Andes; these impacts were primarily attributed to temperature increase caused by anthropogenic climate change; (2) numerous terrestrial and aquatic Andean ecosystems have been affected by climate change (e.g. upward plant colonization, changes in the abundance and distribution of species), and most of these impacts could be attributed to anthropogenic climate change; and (3) community changes and loss of cultural values are among the strongest impacts of human systems that were attributed to climate change; a broad set of studies detected that Andean communities perceived changes in their highly preserved long-standing cultural and spiritual rituals and cosmovision. These findings are key to understand current climate change impacts in the Andean region, and to advance our understanding of complex interactions of coupled natural and human systems in order to put particular attention on integrated scientific assessments and leverage local decision-making and management practices.

Mountains cover about a quarter of the Earth’s land surface and are home to or serve a substantial fraction of the global population with essential ecosystem services, in particular water, food, energy, and recreation. While mountain systems are expected to be highly exposed to climate change, we currently lack a comprehensive global picture of the extent to which environmental and human systems in mountain regions have been affected by recent anthropogenic climate change.Here we undertake an unprecedented effort to detect observed impacts of climate change in mountains regions across all continents. We follow the approach implemented in the IPCC 5th Assessment Report (AR5) and follow-up research where we consider whether a natural or human system has changed beyond its baseline behavior in the absence of climate change, and then attribute the observed change to different drivers, including anthropogenic climate change. We apply an extensive review of peer-reviewed and grey literature and identify more than 300 samples of impacts (aggregate and case studies). We show that a wide range of natural and human systems in mountains have been affected by climate change, including the cryosphere, the water cycle and water resources, terrestrial and aquatic ecosystems, energy production, infrastructure, agriculture, health, migration, tourism, community and cultural values and disasters. Our assessment documents that climate change impacts are observed in mountain regions on all continents. However, the explicit distinction of different drivers contributing to or determining an observed change is often highly challenging; particularly due to widespread data scarcity in mountain regions. In that context, we were also able to document a high amount of impacts in previously under-reported continents such as Africa and South America. In particular, we have been able to include a substantial number of place-based insights from local/indigenous communities representing important alternative worldviews.The role of human influence in observed climate changes is evaluated using data from multiple gridded observational climate products and global climate models. We find that anthropogenic climate change has a clear and discernable fingerprint in changing natural and human mountain systems across the globe. In the cryosphere, ecosystems, water resources and tourism the contribution of anthropogenic climate change to observed changes is significant, showing the sensitivity of these systems to current and future climate change. Furthermore, our analysis reveals the need to consider the plurality of knowledge systems through which climate change impacts are being understood in mountain regions. Such attempts at inclusivity, which addresses issues of representation and justice, should be deemed necessary in exploring climate change impacts.

Northern autumns and winters are getting warmer, and their weather is also getting blander. Observations and climate model simulations reveal that human activities have managed to make today’s weather measurably different than it was only a generation ago.

Weather and climate service providers around the world are looking to issue assessments of the human role in recent extreme weather events. For this attribution to be of value, it is important that vulnerability is acknowledged and questions are framed appropriately.

This study investigates the co-variability between measures of the spreads in the ensembles of seasonal climate simulations and large scale climate indices. Spreads in the ensembles of seasonal simulations (of rainfall and near surface air temperature) from an atmospheric model, over South African provinces, are quantified with the de-trended anomalies of standard deviation (StdDev) and the distance between the 90th and 10th percentiles (RoP) of the simulations. Results indicate that, on seasonal time scales, measures of spread significantly co-vary with observed global sea surface temperatures (SST) far and near. This suggests that the climate factors controlling the degree to which the seasonal climate may be precisely forecast over the South African provinces may both be locally and remotely based. Results also indicate that all significant predictors of spread—El Nino-Southern Oscillation (ENSO), Indian Ocean Dipole (IOD), Benguela Nino (BGN) and Southwest Indian Ocean Index (SWI), are of tropical origin; they co-vary significantly with measures of spread on seasonal time scales over a number of provinces and seasons, particularly during the rainfall onset and peak periods, as well as during the cold season. Nevertheless, responses of measures of spread to climate predictors are relatively small, either indicating that predictor-spread relationships are more complex in nature than can be represented by the traditionally simple climate indices, or indicating that controls on ensemble spread are weak. Therefore, there may be limits to the extent to which year-to-year variations in the predictability of seasonal climate over South Africa provinces might be understood.

An important and under-quantified facet of the risks associated with human-induced climate change emerges through extreme weather. In this paper, we present an initial attempt to quantify recent costs related to extreme weather due to human interference in the climate system, focusing on economic costs arising from droughts and floods in New Zealand during the decade 2007–2017. We calculate these using previously collected information about the damages and losses associated with past floods and droughts, and estimates of the “fraction of attributable risk” that characterizes each event. The estimates we obtain are not comprehensive, and almost certainly represent an underestimate of the full economic costs of climate change, notably chronic costs associated with long-term trends. However, the paper shows the potential for developing a new stream of information that is relevant to a range of stakeholders and research communities, especially those with an interest in the aggregation of the costs of climate change or the identification of specific costs associated with potential liability.

Author(s): Herger, N; Angelil, O; Abramowitz, G; Donat, M; Stone, D; Lehmann, K | Abstract: Climate models serve as indispensable tools to investigate the effect of anthropogenic emissions on current and future climate, including extremes. However, as low-dimensional approximations of the climate system, they will always exhibit biases. Several attempts have been made to correct for biases as they affect extremes prediction, predominantly focused on correcting model-simulated distribution shapes. In this study, the effectiveness of a recently published quantile-based bias correction scheme, as well as a new subset selection method introduced here, are tested out-of-sample using model-as-truth experiments. Results show that biases in the shape of distributions tend to persist through time, and therefore, correcting for shape bias is useful for past and future statements characterizing the probability of extremes. However, for statements characterized by a ratio of the probabilities of extremes between two periods, we find that correcting for shape bias often provides no skill improvement due to the dominating effect of bias in the long-term trend. Using a toy model experiment, we examine the relative importance of the shape of the distribution versus its position in response to long-term changes in radiative forcing. It confirms that the relative position of the two distributions, based on the trend, is at least as important as the shape. We encourage the community to consider all model biases relevant to their metric of interest when using a bias correction procedure and to construct out-of-sample tests that mirror the intended application.

India has witnessed some of the most devastating extreme precipitation events, which have affected urban transportation, agriculture, and infrastructure. Despite the profound implications and damage due to extreme precipitation events, the influence of anthropogenic warming on the intensity and frequency of extreme precipitation events over India remains poorly constrained. Here using the gridded observations and simulations from the Coupled model intercomparison project 5 (CMIP5) and Climate of 20th century plus (C20C+) detection and attribution (D&A) project, we show that the frequency and intensity of extreme precipitation events have increased in India during the last few decades. Along with the extreme precipitation, dew point temperature has also increased during 1979–2015. The scaling relationship between extreme precipitation and dew point temperature shows a super (more than 7% increase per unit rise in dew point temperature) Clausius-Clapeyron (C-C) relationship for the majority of south India. Moreover, southern and central India show a higher (10%/°C) scaling relationship than north India (3.5%/°C). Our analysis using the Hist (historic) and HistNat (historic natural) simulations from the CMIP5 and C20C+ projects confirms an increase in the frequency of extreme precipitation events under the anthropogenic warming. Moreover, we show that 1–5 day precipitation maxima at 5–500 year return period increases (10–30%) under the anthropogenic warming. The frequency of precipitation extremes is projected to rise more prominently in southern and central India in the mid and end of the 21st century under the representative concentration pathway (RCP) 8.5. Our results show a significant contribution of anthropogenic warming in the rise of the frequency of extreme precipitation, which has implications for infrastructure, agriculture, and water resources in India.

To assess the anthropogenic influence on the summer 2013 heat wave in Korea, this study employed a fraction of attributable risk (FAR) approach to three Atmospheric General Circulation Models (AGCMs) with a large ensemble simulation, participating in the C20C+ Detection and Attribution Project. Monthly and daily temperatures were compared between two experiments. The real world (ALL) experiments were simulated under the observed variations in sea surface temperature, sea ice, greenhouse gas, and aerosol concentrations, while the counterfactual world (NAT) experiments were performed under adjusted boundary conditions by removing anthropogenic warming and with preindustrial levels of greenhouse gases and aerosols. Results from the three AGCMs consistently show that anthropogenic influences had an important role in the extreme heat event over Korea, increasing the chance of the occurrence of extreme warming in summer mean temperature as observed in 2013 by at least 20 times, which supports results from the Coupled Model Intercomparison Project Phase 5 (CMIP5) coupled GCMs (CGCMs). A comparison of individual CMIP5 CGCMs suggests that inter-model difference in FAR values is highly correlated with the amplitude of surface warming centered over Korea, which is also supported by the three AGCMs. Further analysis of individual forcing experiments suggests that the inter-model difference in the regional surface warming is closely linked to the model's response to the aerosol forcing, with stronger influence than that of greenhouse gas forcing. Anthropogenic influences also result in a 5–6 times greater likelihood of extreme daily heat events as observed in 2013, which supports a robust mean-extreme relation in the attribution of extreme heat waves. Generally good agreement between AGCM and CGCM results increases the robustness of the conclusion of anthropogenic influences on the summer 2013 Korean heat wave.

Anthropogenic climate change likely influences the beginning of 2020 growing season's water deficit in parts of southern Africa, with severe consequences to food security.

© 2019 Elsevier Ltd Climate change is expected to alter European floods and associated economic losses in various ways. Here we investigate the impact of precipitation change on European average winter and summer financial losses due to flooding under a 1.5 °C warming scenario (reflecting a projected climate in the year 2115 according to RCP2.6)and for a counterfactual current-climate scenario where the climate has evolved without anthropogenic influence (reflecting a climate corresponding to pre-industrial conditions). Climate scenarios were generated with the Community Atmospheric Model (CAM)version 5. For each scenario, we derive a set of weights that when applied to the current climate's precipitation results in a climatology that approximates that of the scenario. We apply the weights to annual losses from a well-calibrated (to the current climate)flood loss model that spans 50,000 years and re-compute the average annual loss to assess the impact of precipitation changes induced by anthropogenic climate change. The method relies on a large stochastic set of physically based flood model simulations and allows quick assessment of potential loss changes due to change in precipitation based on two statistics, namely total precipitation, and total precipitation of very wet days (defined here as the total precipitation of days above the 95th percentile of daily precipitation). We compute the statistics with the raw CAM precipitation and bias-corrected precipitation. Our results show that for both raw and bias-corrected statistics i)average flood loss in Europe generally tend to increase in winter and decrease in summer for the future scenario, and consistent with that change we also show that ii)average flood losses have increased (decreased)for winter (summer)from pre-industrial conditions to the current day. The magnitude of the change varies among scenarios and statistics chosen.

A growing field of research aims to characterise the contribution of anthropogenic emissions to the likelihood of extreme weather and climate events. These analyses can be sensitive to the shapes of the tails of simulated distributions. If tails are found to be unrealistically short or long, the anthropogenic signal emerges more or less clearly, respectively, from the noise of possible weather. Here we compare the chance of daily land-surface precipitation and near-surface temperature extremes generated by three Atmospheric Global Climate Models typically used for event attribution, with distributions from six reanalysis products. The likelihoods of extremes are compared for area-averages over grid cell and regional sized spatial domains. Results suggest a bias favouring overly strong attribution estimates for hot and cold events over many regions of Africa and Australia, and a bias favouring overly weak attribution estimates over regions of North America and Asia. For rainfall, results are more sensitive to geographic location. Although the three models show similar results over many regions, they do disagree over others. Equally, results highlight the discrepancy amongst reanalyses products. This emphasises the importance of using multiple reanalysis and/or observation products, as well as multiple models in event attribution studies.

Heatwaves and extreme temperatures during summer (April–May) in India have profound implications on public health, mortality, water availability, and productivity of labourers. However, how the frequency of the hottest summers in observed record (1951–2015) will change under the warming climate in India is not well explored. Using observations from the India Meteorological Department, we show that mean maximum summer temperature has increased significantly in three (arid, monsoon, and savannah) out of five major climatic regions of India during 1951–2015. We identify the hottest summer in the observed record in the five climatic regions in India. The arid, cold, and temperate regions experienced the hottest summer in 2010 while monsoon and Savannah regions witnessed the hottest summer in 1979 and 1973, respectively. Based on simulations from the Climate of 20th Century Plus (C20C+) Detection and Attribution project, we show that the regional hottest summer of 2010 can be attributed to the anthropogenic warming. We then use simulations of a large (2000 year) ensemble of the EC-Earth model to estimate the exceedance probability of the observed hottest summer in the present climate, 2 °C and 3 °C warming worlds in India. The exceedance probability of the observed hottest summers shows a rise of more than seven and twenty-fold in the 2 °C and 3 °C warming world, respectively, compared to the present climate. The projected increases in the frequency of the hot summers and associated heatwave days will pose great societal challenges in the future in India.

Detection and attribution (D&A) simulations were important components of CMIP5 and underpinned the climate change detection and attribution assessments of the Fifth Assessment Report of the Intergovernmental Panel on Climate Change. The primary goals of the Detection and Attribution Model Intercomparison Project (DAMIP) are to facilitate improved estimation of the contributions of anthropogenic and natural forcing changes to observed global warming as well as to observed global and regional changes in other climate variables; to contribute to the estimation of how historical emissions have altered and are altering contemporary climate risk; and to facilitate improved observationally constrained projections of future climate change. D&A studies typically require unforced control simulations and historical simulations including all major anthropogenic and natural forcings. Such simulations will be carried out as part of the DECK and the CMIP6 historical simulation. In addition D&A studies require simulations covering the historical period driven by individual forcings or subsets of forcings only: such simulations are proposed here. Key novel features of the experimental design presented here include firstly new historical simulations with aerosols-only, stratospheric-ozone-only, CO2-only, solar-only, and volcanic-only forcing, facilitating an improved estimation of the climate response to individual forcing, secondly future single forcing experiments, allowing observationally constrained projections of future climate change, and thirdly an experimental design which allows models with and without coupled atmospheric chemistry to be compared on an equal footing., Geoscientific Model Development, 9 (10), ISSN:1991-9603, ISSN:1991-959X

© 2018 The Authors We examine the effect of the 20th and recent 21st century anthropogenic climate change on high temperature extremes as simulated by four global atmospheric general circulation models submitted to the Climate of the 20th Century Plus Detection and Attribution project. This coordinated experiment is based upon two large ensembles simulations for each participating model. The “world that was” simulations are externally forced as realistically as possible. The “world that might have been” is identical except that the influence of human forcing is removed but natural forcing agents and variations in ocean and sea ice are retained. We apply a stationary generalized extreme value analysis to the annual maxima of the three day average of the daily maximum surface air temperature, finding that long period return values have been increased by human activities between 1 and 3 °C over most land areas. Corresponding changes in the probability of achieving long period non-industrial return values in the industrialized world are also presented. We find that most regions experience increases in the frequency and intensity of extremely hot three day periods, but anthropogenic sulfate aerosol forcing changes locally can decrease these measures of heat waves in some models.

© 2017, Springer-Verlag GmbH Germany. In the context of ongoing climate change, extreme weather events are drawing increasing attention from the public and news media. A question often asked is how the likelihood of extremes might have changed by anthropogenic greenhouse-gas emissions. Answers to the question are strongly influenced by the model used, duration, spatial extent, and geographic location of the event—some of these factors often overlooked. Using output from four global climate models, we provide attribution statements characterised by a change in probability of occurrence due to anthropogenic greenhouse-gas emissions, for rainfall and temperature extremes occurring at seven discretised spatial scales and three temporal scales. An understanding of the sensitivity of attribution statements to a range of spatial and temporal scales of extremes allows for the scaling of attribution statements, rendering them relevant to other extremes having similar but non-identical characteristics. This is a procedure simple enough to approximate timely estimates of the anthropogenic contribution to the event probability. Furthermore, since real extremes do not have well-defined physical borders, scaling can help quantify uncertainty around attribution results due to uncertainty around the event definition. Results suggest that the sensitivity of attribution statements to spatial scale is similar across models and that the sensitivity of attribution statements to the model used is often greater than the sensitivity to a doubling or halving of the spatial scale of the event. The use of a range of spatial scales allows us to identify a nonlinear relationship between the spatial scale of the event studied and the attribution statement.

This paper presents two contributions for research into better understanding the role of anthropogenic warming in extreme weather. The first contribution is the generation of a large number of multi-decadal simulations using a medium-resolution atmospheric climate model, CAM5.1-1degree, under two scenarios of historical climate following the protocols of the C20C+ Detection and Attribution project: the one we have experienced (All-Hist), and one that might have been experienced in the absence of human interference with the climate system (Nat-Hist). These simulations are specifically designed for understanding extreme weather and atmospheric variability in the context of anthropogenic climate change.The second contribution takes advantage of the duration and size of these simulations in order to identify features of variability in the prescribed ocean conditions that may strongly influence calculated estimates of the role of anthropogenic emissions on extreme weather frequency (event attribution). There is a large amount of uncertainty in how much anthropogenic emissions should warm regional ocean surface temperatures, yet contributions to the C20C+ Detection and Attribution project and similar efforts so far use only one or a limited number of possible estimates of the ocean warming attributable to anthropogenic emissions when generating their Nat-Hist simulations. Thus, the importance of the uncertainty in regional attributable warming estimates to the results of event attribution studies is poorly understood. The identification of features of the anomalous ocean state that seem to strongly influence event attribution estimates should therefore be able to serve as a basis set for effective sampling of other plausible attributable warming patterns. The identification performed in this paper examines monthly temperature and precipitation output from the CAM5.1-1degree simulations averaged over 237 land regions, and compares interannual anomalous variations in the ratio between the frequencies of extremes in the All-Hist and Nat-Hist simulations against variations in ocean temperatures. Keywords: C20C+ D&A, CAM5.1, Extremes, Event attribution, Attributable warming

The four idealized configurations of the U.S. CLIVAR Hurricane Working Group are integrated using the global Community Atmospheric Model version 5.1 at two different horizontal resolutions, approximately 100 and 25 km. The publicly released 0.9° × 1.3° configuration is a poor predictor of the sign of the 0.23° × 0.31° model configuration’s change in the total number of tropical storms in a warmer climate. However, it does predict the sign of the higher-resolution configuration’s change in the number of intense tropical cyclones in a warmer climate. In the 0.23° × 0.31° model configuration, both increased CO2 concentrations and elevated sea surface temperature (SST) independently lower the number of weak tropical storms and shorten their average duration. Conversely, increased SST causes more intense tropical cyclones and lengthens their average duration, resulting in a greater number of intense tropical cyclone days globally. Increased SST also increased maximum tropical storm instantaneous precipitation rates across all storm intensities. It was found that while a measure of maximum potential intensity based on climatological mean quantities adequately predicts the 0.23° × 0.31° model’s forced response in its most intense simulated tropical cyclones, a related measure of cyclogenesis potential fails to predict the model’s actual cyclogenesis response to warmer SSTs. These analyses lead to two broader conclusions: 1) Projections of future tropical storm activity obtained by a direct tracking of tropical storms simulated by coarse-resolution climate models must be interpreted with caution. 2) Projections of future tropical cyclogenesis obtained from metrics of model behavior that are based solely on changes in long-term climatological fields and tuned to historical records must also be interpreted with caution.

The half a degree additional warming, prognosis and projected impacts (HAPPI) experimental protocol provides a multi-model database to compare the effects of stabilizing anthropogenic global warming of 1.5 °C over preindustrial levels to 2.0 °C over these levels. The HAPPI experiment is based upon large ensembles of global atmospheric models forced by sea surface temperature and sea ice concentrations plausible for these stabilization levels. This paper examines changes in extremes of high temperatures averaged over three consecutive days. Changes in this measure of extreme temperature are also compared to changes in hot season temperatures. We find that over land this measure of extreme high temperature increases from about 0.5 to 1.5 °C over present-day values in the 1.5 °C stabilization scenario, depending on location and model. We further find an additional 0.25 to 1.0 °C increase in extreme high temperatures over land in the 2.0 °C stabilization scenario. Results from the HAPPI models are consistent with similar results from the one available fully coupled climate model. However, a complicating factor in interpreting extreme temperature changes across the HAPPI models is their diversity of aerosol forcing changes., Earth System Dynamics, 9 (1), ISSN:2190-4987, ISSN:2190-4979

Author(s): Mishra, V; Mukherjee, S; Kumar, R; Stone, DA | Abstract: © 2017 The Author(s). Published by IOP Publishing Ltd. Heatwaves with large impacts have increased in the recent past and will continue to increase under future warming. However, the implication for population exposure to severe heatwaves remains unexplored. Here, we characterize maximum potential human exposure (without passive/active reduction measures) to severe heatwaves in India. We show that if the global mean temperature is limited to 2.0 °C above pre-industrial conditions, the frequency of severe heatwaves will rise by 30 times the current climate by the end-21st century. In contrast, the frequency is projected to be about 2.5 times more (than the low-warming scenario of 2 °C) under conditions expected if the RCP8.5 'business-as-usual' emissions scenario is followed. Under the 2.0 °C low-warming target, population exposure to severe heatwaves is projected to increase by about 15 and 92 times the current level by the mid and end-21st century respectively. Strategies to reduce population growth in India during the 21st century may provide only limited mitigation of heatwave exposure mostly late in the century. Limiting global temperatures to 1.5 °C above preindustrial would reduce the exposure by half relative to RCP8.5 by the mid-21st century. If global temperatures are to exceed 1.5 °C then substantial measures will be required to offset the large increase in exposure to severe heatwaves in India.

© 2017 Lawrence Berkeley National Laboratory, Christopher J. Paciorek The Colorado floods of September 2013 caused severe damage and fatalities, and resulted from prolonged heavy rainfall unusual for that time of year – both in its record-breaking amounts and associated weather systems. We investigate the possible role of anthropogenic climate change in this extreme event. The unusual hydrometeorology of the event, however, challenges standard frameworks for attributing extreme events to anthropogenic climate change, because they typically struggle to simulate and connect the large-scale meteorology associated with local weather processes. Therefore we instead employ a part dynamical modelling- part observational- based event attribution approach, which simulates regional Colorado rainfall conditional on boundary conditions prescribed from the observed synoptic-scale meteorology in September 2013 – and assumes these conditions would have been similar in the absence of anthropogenic forcing. Using this ‘conditional event attribution’ approach we find that our regional climate model simulations indicate that anthropogenic drivers increased the magnitude of heavy northeast Colorado rainfall for the wet week in September 2013 by 30%, with the occurrence probability of a week at least that wet increasing by at least a factor of 1.3. By comparing the convective and large-scale components of rainfall, we find that this increase resulted in part from the additional moisture-carrying capacity of a warmer atmosphere – allowing more intense local convective rainfall that induced a dynamical positive feedback in the existing larger scale moisture flow – and also in part from additional moisture transport associated with larger scale circulation change. Our approach precludes assessment of changes in the frequency of the observed synoptic meteorological conditions themselves, and thus does not assess the effect of anthropogenic climate drivers on the statistics of heavy Colorado rainfall events. However, tailoring analysis tools to diagnose particular aspects of localized extreme weather events, conditional on the observed large-scale meteorology, can prove useful for diagnosing the physical effects of anthropogenic climate change on severe weather events – especially given large uncertainties in assessments of anthropogenic driven changes in atmospheric circulation.

© 2017 IOP Publishing Ltd. In the midsummer of 2013, Central and Eastern China (CEC) was hit by an extraordinary heat event, with the region experiencing the warmest July-August on record. To explore how human-induced greenhouse gas emissions and natural internal variability contributed to this heat event, we compare observed July-August mean surface air temperature with that simulated by climate models. We find that both atmospheric natural variability and anthropogenic factors contributed to this heat event. This extreme warm midsummer was associated with a positive high-pressure anomaly that was closely related to the stochastic behavior of atmospheric circulation. Diagnosis of CMIP5 models and large ensembles of two atmospheric models indicates that human influence has substantially increased the chance of warm mid-summers such as 2013 in CEC, although the exact estimated increase depends on the selection of climate models.

Extremely high air temperatures are uncomfortable for everyone. For some segments of the population, they can be deadly. Both the physical and societal aspects of intense heat waves in a changing climate warrant close study. The large-scale meteorological patterns leading to such events lay the framework for understanding their underlying causal mechanisms, while several methods of quantifying the combination of heat and humidity can be used to determine when these patterns result in stressful conditions. We examine four historic heat waves as case studies to illustrate differences in the structure of heat waves and the variety of effects of extreme heat on humans, which are characterized in terms of demographic, geographic, and socioeconomic impacts, including mortality and economic ramifications. Weather station data and climate model projections for the future point to an increase in the frequency and intensity of extreme heat waves as the overall climate gets warmer. Changes in the radiative energy balance of the planet are the principal culprit behind this increase. Quantifying changes in the statistics of extreme heat waves allows for examination of changes in their potential contribution to human health risk. Large-scale mortality during heat waves always occurs within a context of other factors, including public health policy, rural and urban management and planning, and cultural practices. Consequently, the impacts of heat waves can be reduced, and may in many places be manageable into the future, through implementation of such measures as public health warning systems, effective land management, penetration of air conditioning, and increased monitoring of vulnerable or exposed individuals. Given the potential for severe impacts of the more intense heat waves that are virtually certain to occur in the warmer future, it is critical that both the physical and social sciences be considered together to enable society to adapt to these conditions.

The effect of forcing wind resolution on the extremes of global wind-wave climate are investigated in numerical simulations. Forcing winds from the Community Atmosphere Model at horizontal resolutions of ∼1.0° and ∼0.25° are used to drive Wavewatch III. Differences in extreme wave height are found to manifest most strongly in tropical cyclone (TC) regions, emphasizing the need for high-resolution forcing in those areas. Comparison with observations typically show improvement in performance with increased forcing resolution, with a strong influence in the tail of the distribution, although simulated extremes can exceed observations. A simulation for the end of the 21st century under a RCP 8.5 type emission scenario suggests further increases in extreme wave height in TC regions.

This study assesses the existence and importance of trends in the spread of South African climate simulations covering 50 years of a large initial-condition ensemble from a dynamical atmospheric model. It quantifies ensemble spread using two contrasting measures – standard deviation and 10–90th percentile range. The study then evaluates and examines the characteristics of long-term trends in the ensemble spread in relation to trends in the ensemble mean and in the observational record, by considering the skill of the monthly mean precipitation and near surface air temperature simulations. Results provide evidence that variations in ensemble spreads generated by the atmospheric model used in this study reflect fundamental properties of atmospheric variability in the real climate system. We find significant long-term trends in the measures of spread, with a general coastal–inland gradient, suggesting the possibility of existence of interannual variations in the potential range of seasonal climate simulations over South Africa. We also find robust relationships between trends in the observational record, in the simulated ensemble means and in measures of the simulated ensemble spread. Irrespective of the direction of trends, the correspondence of higher model skill when trends in the ensemble spread are larger suggests that the skill produced by a dynamical modelling system may not be independent of the model ability to capture the real atmospheric trends in whatever the model is simulating or forecasting. Therefore, based on historical data, further understanding of how potential predictability is changing has the prospect to improve the interpretation of current estimates of simulation skill.

There is a growing research interest in understanding extreme weather in the context of anthropogenic climate change, posing a requirement for new tailored climate data products. Here we introduce the Climate of the 20th Century Plus Detection and Attribution project (C20C + D&A), an international collaboration generating a product specifically intended for diagnosing causes of changes in extreme weather and for understanding uncertainties in that diagnosis. The project runs multiple dynamical models of the atmosphere-land system under observed historical conditions as well as under naturalised versions of those observed conditions, with the latter representing how the climate system might have evolved in the absence of anthropogenic interference. Each model generates large ensembles of simulations with different initial conditions for each historical scenario, providing a large sample size for understanding interannual variability, long-term trends, and the anthropogenic role in rare types of weather. This paper describes the C20C + D&A project design, implementation, strengths, and limitations, and also discusses various activities such as this special issue of Weather and Climate Extremes dedicated to “First results of the C20C + Detection and Attribution project”. Keywords: Detection and attribution, Event attribution, Climate models

© 2017 American Meteorological Society. The annual "State of the Climate" report, published in the Bulletin of the American Meteorological Society (BAMS), has included a supplement since 2011 composed of brief analyses of the human influence on recent major extreme weather events. There are now several dozen extreme weather events examined in these supplements, but these studies have all differed in their data sources as well as their approaches to defining the events, analyzing the events, and the consideration of the role of anthropogenic emissions. This study reexamines most of these events using a single analytical approach and a single set of climate model and observational data sources. In response to recent studies recommending the importance of using multiple methods for extreme weather event attribution, results are compared from these analyses to those reported in the BAMS supplements collectively, with the aim of characterizing the degree to which the lack of a common methodological framework may or may not influence overall conclusions. Results are broadly similar to those reported earlier for extreme temperature events but disagree for a number of extreme precipitation events. Based on this, it is advised that the lack of comprehensive uncertainty analysis in recent extreme weather attribution studies is important and should be considered when interpreting results, but as yet it has not introduced a systematic bias across these studies.

Changes in precipitation characteristics directly affect society through their impacts on drought and floods, hydro-dams, and urban drainage systems. Global warming increases the water holding capacity of the atmosphere and thus the risk of heavy precipitation. Here, daily precipitation records from over 700 Chinese stations from 1956 to 2005 are analyzed. The results show a significant shift from light to heavy precipitation over eastern China. An optimal fingerprinting analysis of simulations from 11 climate models driven by different combinations of historical anthropogenic (greenhouse gases, aerosols, land use, and ozone) and natural (volcanic and solar) forcings indicates that anthropogenic forcing on climate, including increases in greenhouse gases (GHGs), has had a detectable contribution to the observed shift toward heavy precipitation. Some evidence is found that anthropogenic aerosols (AAs) partially offset the effect of the GHG forcing, resulting in a weaker shift toward heavy precipitation in simulations that include the AA forcing than in simulations with only the GHG forcing. In addition to the thermodynamic mechanism, strengthened water vapor transport from the adjacent oceans and by midlatitude westerlies, resulting mainly from GHG-induced warming, also favors heavy precipitation over eastern China. Further GHG-induced warming is predicted to lead to an increasing shift toward heavy precipitation, leading to increased urban flooding and posing a significant challenge for mega-cities in China in the coming decades. Future reductions in AA emissions resulting from air pollution controls could exacerbate this tendency toward heavier precipitation.

16 páginas, 3 figuras., The next generation of climate-driven, disease prediction models will most likely require a mechanistically based, dynamical framework that parameterizes key processes at a variety of locations. Over the next two decades, consensus climate predictions make it possible to produce forecasts for a number of important infectious diseases that are largely independent of the uncertainty of longer-term emissions scenarios. In particular, the role of climate in the modulation of seasonal disease transmission needs to be unravelled from the complex dynamics resulting from the interaction of transmission with herd immunity and intervention measures that depend upon previous burdens of infection. Progress is also needed to solve the mismatch between climate projections and disease projections at the scale of public health interventions. In the time horizon of seasons to years, early warning systems should benefit from current developments on multi-model ensemble climate prediction systems, particularly in areas where high skill levels of climate models coincide with regions where large epidemics take place. A better understanding of the role of climate extremes on infectious diseases is urgently needed, FJDR received financial support from the ENSEMBLES project (GOCE-CT-2003-505539). Support for this work was also provided by CIRCE-EUFP6 to X.R., by NIH/NSF EID Grant 0430120 and a NOAA award to X.R., M.P., J.K. and A.J.D. J.G-S and X. Rodó wants to acknowledge support from the EU project QWeCI (Quantifying Weather and Climate Impacts on health in developing countries; funded by the European Commission’s Seventh Framework Research Programme under the grant agreement 243964). and the DENFREE: DENgue research Framework for Resisting Epidemics in Europe of the EUFP7 programme project. M. Pascual is an investigator of the Howard Hughes Medical Institute.

Author(s): Wehner, M; Stone, D; Krishnan, H; Achutarao, K; Castillo, F | Abstract: We find that the deadly heat waves in India and Pakistan in 2015 were exacerbated by anthropogenic climate change. Although the impacts of both events were severe, the events themselves were not connected to each other.

© 2016 IEEE. When working with large, multidimensional and multivariate data, science users are frequently interested in understanding variation in data, as opposed to the actual data values. Our work focuses on exploring how a simple statistical metric, the Coefficient of Variation (or Cv), can be used in several different ways to facilitate understanding variation in large data. As a statistical measure, it offers a key advantage over more widely accepted measures like standard deviation, namely to its ability to capture local variation properties. As a multidimensional projection operator, Cvis an effective way of reducing data size while preserving the key variational signal. Visualizations produced from Cvthat target conveying variation in data are highly informative, especially compared to those produced with more widely known methods. We demonstrate these ideas within the context of a two-part application case study focusing on understanding long-Term trends in the the changes in precipitation and winds in large-scale climate model ensemble output.

© 2016 Macmillan Publishers Limited, part of Springer Nature. The Paris Climate Agreement is an important step for international climate policy, but the compensation for negative effects of climate change based on clear assignment of responsibilities remains highly debated. From both a policy and a science perspective, it is unclear how responsibilities should be defined and on what evidence base. We explore different normative principles of justice relevant to climate change impacts, and ask how different forms of causal evidence of impacts drawn from detection and attribution research could inform policy approaches in accordance with justice considerations. We reveal a procedural injustice based on the imbalance of observations and knowledge of impacts between developed and developing countries. This type of injustice needs to be considered in policy negotiations and decisions, and efforts strengthened to reduce it.

© 2015, The Author(s). Despite being a well-established research field, the detection and attribution of observed climate change to anthropogenic forcing is not yet provided as a climate service. One reason for this is the lack of a methodology for performing tailored detection and attribution assessments on a rapid time scale. Here we develop such an approach, based on the translation of quantitative analysis into the “confidence” language employed in recent Assessment Reports of the Intergovernmental Panel on Climate Change. While its systematic nature necessarily ignores some nuances examined in detailed expert assessments, the approach nevertheless goes beyond most detection and attribution studies in considering contributors to building confidence such as errors in observational data products arising from sparse monitoring networks. When compared against recent expert assessments, the results of this approach closely match those of the existing assessments. Where there are small discrepancies, these variously reflect ambiguities in the details of what is being assessed, reveal nuances or limitations of the expert assessments, or indicate limitations of the accuracy of the sort of systematic approach employed here. Deployment of the method on 116 regional assessments of recent temperature and precipitation changes indicates that existing rules of thumb concerning the detectability of climate change ignore the full range of sources of uncertainty, most particularly the importance of adequate observational monitoring.

The Intergovernmental Panel on Climate Change (IPCC) has accepted the invitation from the UNFCCC to provide a special report on the impacts of global warming of 1.5 °C above pre-industrial levels and related global greenhouse gas emission pathways. Many current experiments in, for example, the Coupled Model Inter-comparison Project (CMIP), are not specifically designed for informing this report. Here, we document the design of the Half a degree Additional warming, Projections, Prognosis and Impacts (HAPPI) experiment. HAPPI provides a framework for the generation of climate data describing how the climate, and in particular extreme weather, might differ from the present day in worlds that are 1.5 °C and 2.0 °C warmer than pre-industrial conditions. Output from participating climate models includes variables frequently used by a range of integrated assessment models. The key challenge is to separate the impact of an additional approximately half degree of warming from uncertainty in climate model responses and internal climate variability that dominate CMIP-style experiments. Large ensembles of simulations (> 50 members) of atmosphere-only models for three time slice experiments are proposed, each a decade in length; the first being the most recent observed 10-year period (2006–2015), the second two being estimates of the a similar decade but under 1.5 and 2 °C conditions a century in the future. We use the Representative Concentration Pathways 2.6 (RCP2.6) to provide the model boundary conditions for the 1.5 °C scenario, and a weighted combination of RCP2.6 and RCP4.5 for the 2 °C scenario.

Impacts of recent regional changes in climate on natural and human systems are documented across the globe, yet studies explicitly linking these observations to anthropogenic forcing of the climate are scarce. Here we provide a systematic assessment of the role of anthropogenic climate change for the range of impacts of regional climate trends reported in the IPCC’s Fifth Assessment Report. We find that almost two-thirds of the impacts related to atmospheric and ocean temperature can be confidently attributed to anthropogenic forcing. In contrast, evidence connecting changes in precipitation and their respective impacts to human influence is still weak. Moreover, anthropogenic climate change has been a major influence for approximately three-quarters of the impacts observed on continental scales. Hence the effects of anthropogenic emissions can now be discerned not only globally, but also at more regional and local scales for a variety of natural and human systems. An assessment of links between anthropogenic climate change and the impacts of recent regional climate trends on human and natural systems shows that many of these impacts can now be attributed to the effects of global warming.

Changes in the hydrological cycle are among the aspects of climate change most relevant for human systems and ecosystems. Besides trends in overall wetting or drying, changes in temporal characteristics of wetting and drying are of crucial importance in determining the climate hazard posed by such changes. This is particularly the case for tropical regions, where most precipitation occurs during the rainy season and changes in rainy season onset and length have substantial consequences. Here we present projections for changes in tropical rainy season lengths for mean temperature increase of 1.5 °C and 2 °C above pre-industrial levels. Based on multi-ensemble quasi-stationary simulations at these warming levels, our analysis indicates robust changes in rainy season characteristics in large parts of the tropics despite substantial natural variability. Specifically, we report a robust shortening of the rainy season for all of tropical Africa as well as north-east Brazil. About 27% of West Africa is projected to experience robust changes in the rainy season length with a mean shortening of about 7 days under 1.5 °C. We find that changes in the temporal characteristics are largely unrelated to changes in overall precipitation, highlighting the importance of investigating both separately.