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Most attribution studies tend to focus on the impact of anthropogenic forcing on individual variables. However, studies have already established that many climate variables are interrelated, and therefore, multidimensional changes can occur in response to climate change. Here, we propose a multivariate method which uses copula theory to account for underlying climate conditions while attributing the impact of anthropogenic forcing on a given climate variable. This method can be applied to any relevant pair of climate variables; here we apply the methodology to study high temperature exceedances given specified precipitation conditions (e.g., hot droughts). With this method, we introduce a new conditional probability ratio indicator, which communicates the impact of anthropogenic forcing on the likelihood of conditional exceedances. Since changes in temperatures under droughts have already accelerated faster than average climate conditions in many regions, quantifying anthropogenic impacts on conditional climate behavior is important to better understand climate change. Plain Language Summary: Most studies investigating human impacts on climate conditions focus on characterizing changes in individual variables such as precipitation or temperature. However, since many climate conditions are interconnected, these individual variables do not comprehensively represent the many changes that can occur in response to human activity. Here, we introduce a method that takes into account underlying climate conditions while quantifying the impact of human activity on a given climate variable. This method can be used to study pairs of climate variables and here we provide an example application to examine high temperature occurrences during dry precipitation conditions using climate models. For example, we show that regions such as the Amazon have a 4.1 times higher likelihood of experiencing high temperatures under dry climate conditions as a result of human activity. Given our knowledge of future climate change, we anticipate that the relationships between key climate variables may continue to change, which makes the study of human impacts on conditional climate behavior important for a more complete understanding of climate change. Key Points: We propose a multivariate method to conduct an attribution analysis of one variable conditioned on another related climate variableWe show how this method can be applied to high temperature exceedances given specified precipitation conditionsLand regions between 60°N and 60°S show increased risk of conditional temperature extremes under dry conditions due to anthropogenic forcing [ABSTRACT FROM AUTHOR]

Changes in regional water availability belong to the most crucial potential impacts of anthropogenic climate change, but are highly uncertain. It is thus of key importance for stakeholders to assess the possible implications of different global temperature thresholds on these quantities. Using a subset of climate model simulations from the fifth phase of the Coupled Model Intercomparison Project (CMIP5), we derive here the sensitivity of regional changes in precipitation and in precipitation minus evapotranspiration to global temperature changes. The simulations span the full range of available emission scenarios, and the sensitivities are derived using a modified pattern scaling approach. The applied approach assumes linear relationships on global temperature changes while thoroughly addressing associated uncertainties via resampling methods. This allows us to assess the full distribution of the simulations in a probabilistic sense. Northern high-latitude regions display robust responses towards wetting, while subtropical regions display a tendency towards drying but with a large range of responses. Even though both internal variability and the scenario choice play an important role in the overall spread of the simulations, the uncertainty stemming from the climate model choice usually accounts for about half of the total uncertainty in most regions. We additionally assess the implications of limiting global mean temperature warming to values below (i) 2K or (ii) 1.5K (as stated within the 2015 Paris Agreement). We show that opting for the 1.5K target might just slightly influence the mean response, but could substantially reduce the risk of experiencing extreme changes in regional water availability. [ABSTRACT FROM AUTHOR]

Although prediction plays a prominent role in mental processing, we have only limited understanding of how the brain generates and employs predictions. This paper develops a theoretical framework in three steps. First I propose a process model that describes how predictions are produced and are linked to behavior. Subsequently I describe a generative mechanism, consisting of the selective amplification of neural dynamics in the context of boundary conditions. I hypothesize that this mechanism is active as a process engine in every mental process, and that therefore each mental process proceeds in two stages: (i) the formation of process boundary conditions; (ii) the bringing about of the process function by the operation – within the boundary conditions – of a relatively ‘blind’ generative process. Thirdly, from this hypothesis I derive a strategy for describing processes formally. The result is a multilevel framework that may also be useful for studying mental processes in general. [ABSTRACT FROM AUTHOR]

Changes in the hydrological conditions of the land surface have substantial impacts on society. Yet assessments of observed continental dryness trends yield contradicting results. The concept that dry regions dry out further, whereas wet regions become wetter as the climate warms has been proposed as a simplified summary of expected as well as observed changes over land, although this concept is mostly based on oceanic data. Here we present an analysis of more than 300 combinations of various hydrological data sets of historical land dryness changes covering the period from 1948 to 2005. Each combination of data sets is benchmarked against an empirical relationship between evaporation, precipitation and aridity. Those combinations that perform well are used for trend analysis. We find that over about three-quarters of the global land area, robust dryness changes cannot be detected. Only 10.8% of the global land area shows a robust 'dry gets drier, wet gets wetter' pattern, compared to 9.5% of global land area with the opposite pattern, that is, dry gets wetter, and wet gets drier. We conclude that aridity changes over land, where the potential for direct socio-economic consequences is highest, have not followed a simple intensification of existing patterns. [ABSTRACT FROM AUTHOR]

Soil water content (SWC) depends on and affects the energy flux partitioning at the land-atmosphere interface. Above all, the latent heat flux is limited by the SWC of the root zone on one hand and radiation on the other. Therefore, SWC is a key variable in the climate system. In this study, the performance of the Weather Research and Forecasting model coupled with the Noah land surface model (WRF-Noah) system in a climate hindcast simulation from 1990 to 2008 is evaluated with respect to SWC versus two reanalysis datasets for Europe during 2007 and 2008 with in situ soil moisture observations from southern France. When compared with the in situ observations, WRF-Noah generally reproduces the SWC annual cycle while the reanalysis SWCs do not. The biases in areal mean WRF-Noah SWCs relate to biases in precipitation and evapotranspiration in a cropland environment. The spatial patterns and temporal variability of the seasonal mean SWCs from the WRF-Noah simulations and from the two reanalyses correspond well, while absolute values differ significantly, especially at the regional scale. [ABSTRACT FROM AUTHOR]

Large herbivores and termites are important functional groups in African savannahs. Both groups affect small mammals, which are also important determinants for savannah structure and function. Because vegetation on Macrotermes mounds are preferentially grazed by large herbivores, and mounds represent resource-rich distinct habitat patches for small mammals in relatively resource-poor savannahs, termite mounds are ideal sites for studies of how grazing by large mammals and productivity affect communities of small mammals. We conducted an experiment in Lake Mburo National Park, Uganda, with four treatments: large vegetated Macrotermes mounds (with and without large herbivores) and adjacent savannah areas (with and without large herbivores). We replicated the treatment blocks nine times and trapped small mammals regularly over a period of almost 2 years. Small mammal species assemblages differed considerably between mounds and savannah areas. Grazing had a substantial effect on small mammal species assemblages in the resource-poor savannah, but not in the relatively resource-rich termitaria. Small mammal species abundance, biomass, and richness were higher on termite mounds than adjacent savannah areas. Excluding large herbivores caused a major increase in species abundance, biomass, and richness both on savannah and termitaria. Herbaceous plant species evenness was an important determinant of the small mammal community. Small mammal biomass increased with high plant dominance, indicating that a few dominant plant species are important for biomass production of small mammals. Small mammal diversity was not related to any of the treatments, but increased with plant species evenness as well as richness. Fencing increased species dominance in the small mammal community on both savannah and termitaria, probably because competitive patterns shift from inter-guild (that is, between large and small mammals) to intra-guild (that is, between small mammals) when large mammals are excluded. The study highlights the complex interactions among large herbivores, termites, herbaceous plants, and small mammals in African savannahs. When studying the structure and function of small mammal communities it is therefore important to consider several coexisting functional groups. [ABSTRACT FROM AUTHOR]

Anticipated increases in water scarcity will pose substantial challenges to households, farmers, and industries in order to secure a sufficient supply of water in the coming decades. To facilitate an adequate policy-making that addresses the mounting water scarcity problems, a thorough assessment of uncertainties and characteristics of projected changes in water scarcity are of great interest to decision-makers and stakeholders in the water sector. Here we assess global water scarcity projections following feasible combinations of Shared Socioeconomic Pathways (SSPs) and Representative Concentration Pathways (RCPs) for the first half of the 21st century. We identify - alongside trends in average water scarcity - changes in the uncertainty range and drivers of anticipated water scarcity conditions. Our results show that average water scarcity and the associated range of uncertainty are generally on the increase worldwide. Based on this results we, develop a general decision-making framework by identifying four representative clusters of specific water-policy challenges and needs that could help to enhance policymaking in impacted regions by explicitly taking into account the associated uncertainties.We further show that in ca. 4/5 of the global land area that is prone to water scarce conditions, these increases are dominated by climate change and associated trends in the water supply. In other regions, and especially those regions projected to show large relative increases in water scarcity, water scarcity changes are more likely driven by anticipated changes in the water demand. These results and the assessment of the relative importance of sector-specific changes in water demand (domestic, industrial, agricultural sector), could in particular help to facilitate targeted adaptation strategies. [ABSTRACT FROM AUTHOR]

Aridity is a complex concept, aiming to indicate and measure the perception of climatologicaldryness on the earth surface. It is generally defined as the 'degree to which a climate lacksmoisture to sustain life in terrestrial ecosystems’. A widely used (offline) impact model toassess projected changes in aridity is the Aridity index (defined as the ratio of potentialevaporation to precipitation or vice versa). Several studies using climate model output asinput to the Aridity index, have concluded that aridity will generally increase underconditions of increased CO2 and associated global warming. However, a growingnumber of studies, assessing the same climate model output directly, suggests a morenuanced response of aridity to global warming, raising the question if the Aridityindex provides a good representation of the complex nature of anticipated ariditychanges. Based on the general definition of aridity, we select here several hydroclimatological andecohydrological variables from the climate model output and systematically compareprojected changes (1980-99 vs. 2080-99) in these variables against projected changes in theAridity index. Our results show that the Aridity index generally provides a poor proxy foranticipated changes in most hydroclimatological variables. The Aridity index further fails torepresent projected changes in vegetation. Our results e.g., show that in about 3∕4 of all landarea experiencing increases in aridity projected by the Aridity index, plant growth is insteadsimulated to increase. We further identify a substantial mean bias between projected changesin hydroclimatological variables and the Aridity index, which is related to the parametrizationof potential evaporation. We show that this mean bias can be largely reduced by choosingparameterizations that account for the vegetation response to increased atmosphericCO2. Even though we recognize that the aridity index has been used as a proxy for historicalgeographic patterns of some of the aforementioned quantities, we call here for a more directapproach to assess projected trends of aridity. The central issue is that the offline Aridity indexmodel does not directly translate to plant productivity, runoff and many other variables thatare related to our common understanding of aridity. Instead, the most direct approach forassessing aridity projections is to use climate model output for these impact-relevantvariables. [ABSTRACT FROM AUTHOR]

A correction to the article "Global assessment of trends in wetting and drying over land" that was published online in the September 14, 2014 issue is presented.

Deficiencies of micronutrients, including essential trace elements, affect up to 3 billion people worldwide. The dietary availability of trace elements is determined largely by their soil concentrations. Until now, the mechanisms governing soil concentrations have been evaluated in small-scale studies, which identify soil physicochemical properties as governing variables. However, global concentrations of trace elements and the factors controlling their distributions are virtually unknown. We used 33,241 soil data points to model recent (1980-1999) global distributions of Selenium (Se), an essential trace element that is required for humans.Worldwide, up to one in seven people have been estimated to have low dietary Se intake. Contrary to small-scale studies, soil Se concentrations were dominated by climate-soil interactions. Using moderate climate-change scenarios for 2080-2099, we predicted that changes in climate and soil organic carbon content will lead to overall decreased soil Se concentrations, particularly in agricultural areas; these decreases could increase the prevalence of Se deficiency. The importance of climate-soil interactions to Se distributions suggests that other trace elements with similar retentionmechanismswill be similarly affected by climate change. [ABSTRACT FROM AUTHOR]

In the context of changing climate and increasing water demand, large-scale hydrological models are helpful for understanding and projecting future water resources across scales. Groundwater is a critical freshwater resource and strongly controls river flow throughout the year. It is also essential for ecosystems and contributes to evapotranspiration, resulting in climate feedback. However, groundwater systems worldwide are quite diverse, including thick multilayer aquifers and thin heterogeneous aquifers. Recently, efforts have been made to improve the representation of groundwater systems in large-scale hydrological models. The evaluation of the accuracy of these model outputs is challenging because (1) they are applied at much coarser resolutions than hillslope scale, (2) they simplify geological structures generally known at local scale, and (3) they do not adequately include local water management practices (mainly groundwater pumping). Here, we apply a large-scale hydrological model (CWatM), coupled with the groundwater flow model MODFLOW, in two different climatic, geological, and socioeconomic regions: the Seewinkel area (Austria) and the Bhima basin (India). The coupled model enables simulation of the impact of the water table on groundwater–soil and groundwater–river exchanges, groundwater recharge through leaking canals, and groundwater pumping. This regional-scale analysis enables assessment of the model's ability to simulate water tables at fine spatial resolutions (1 km for CWatM, 100–250 m for MODFLOW) and when groundwater pumping is well estimated. Evaluating large-scale models remains challenging, but the results show that the reproduction of (1) average water table fluctuations and (2) water table depths without bias can be a benchmark objective of such models. We found that grid resolution is the main factor that affects water table depth bias because it smooths river incision, while pumping affects time fluctuations. Finally, we use the model to assess the impact of groundwater-based irrigation pumping on evapotranspiration, groundwater recharge, and water table observations from boreholes. [ABSTRACT FROM AUTHOR]

In the context of changing climate and increasing water demand, large-scale hydrological models are helpful for understanding and projecting future water resources across scales. Groundwater is a critical freshwater resource and strongly controls river flow throughout the year. It is also essential for ecosystems and contributes to evapotranspiration, resulting in climate feedback. However, groundwater systems worldwide are quite diverse, including thick multi-layer aquifers and thin heterogeneous aquifers. Recently, efforts have been made to improve the representation of groundwater systems in largescale hydrological models. The evaluation of the accuracy of these model outputs is challenging because: (1) they are applied at much coarser resolutions than hillslope scale, (2) they simplify geological structures generally known at local scale, and (3) they do not adequately include local water management practices (mainly groundwater pumping). Here, we apply a large-scale hydrological model (CWatM), coupled with the groundwater flow model MODFLOW, in two different climatic, geological, and socioeconomic regions, the Seewinkel area (Austria) and the Bhima basin (India). The coupled model enables simulation of the impact of the water table on groundwater-soils and groundwater-rivers exchanges, groundwater recharge through leaking canals, and groundwater pumping. This regional scale analysis enables assessment of the model's ability to simulate water tables at fine spatial resolutions (1 km for CWatM, 100-250 m for MODFLOW) and when groundwater pumping is well estimated. Evaluating large-scale models remains challenging, but the results show that the reproduction of (1) average water table fluctuations and (2) water table depths without bias can be a benchmark objective of such models. We found that subgrid resolution (river incision) is the main factor that affects water table depth bias, while pumping affects time fluctuations. Finally, we use the model to assess the impact of groundwater-based irrigation pumping on evapotranspiration, groundwater recharge, and water table observations from boreholes. [ABSTRACT FROM AUTHOR]

We develop a new large-scale hydrological and water resources model, the Community Water Model (CWatM), which can simulate hydrology both globally and regionally at different resolutions from 30 arcmin to 30 arcsec at daily time steps. CWatM is open source in the Python programming environment and has a modular structure. It uses global, freely available data in the netCDF4 file format for reading, storage, and production of data in a compact way. CWatM includes general surface and groundwater hydrological processes but also takes into account human activities, such as water use and reservoir regulation, by calculating water demands, water use, and return flows. Reservoirs and lakes are included in the model scheme. CWatM is used in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which compares global model outputs. The flexible model structure allows for dynamic interaction with hydro-economic and water quality models for the assessment and evaluation of water management options. Furthermore, the novelty of CWatM is its combination of state-of-the-art hydrological modeling, modular programming, an online user manual and automatic source code documentation, global and regional assessments at different spatial resolutions, and a potential community to add to, change, and expand the open-source project. CWatM also strives to build a community learning environment which is able to freely use an open-source hydrological model and flexible coupling possibilities to other sectoral models, such as energy and agriculture. [ABSTRACT FROM AUTHOR]

We develop a new large-scale hydrological and water resources model, the Community Water Model (CWatM), which can simulate hydrology both globally and regionally at different resolutions from 30 arc min to 30 arc sec at daily time steps. CWatM is open-source in the Python programming environment and has a modular structure. It uses global, freely available data in the netCDF4 file format for reading, storage, and production of data in a compact way. CWatM includes general surface and groundwater hydrological processes, but also takes into account human activities, such as water use and reservoir regulation, by calculating water demands, water use, and return flows. Reservoirs and lakes are included in the model scheme. CWatM is used in the framework of the Inter-Sectoral Impact Model Intercomparison Project (ISIMIP), which compares global model outputs. The flexible model structure allows dynamic interaction with hydro-economic and water quality models for the assessment and evaluation of water management options. Furthermore, the novelty of CWatM is its combination of state-of the-art hydrological modeling, modular programming, an online user manual and automatic source code documentation, global and regional assessments at different spatial resolutions, and a potential community to add to, change, and expand the open-source project. CWatM also strives to build a community learning environment which is able to freely use an open-source hydrological model and flexible coupling possibilities to other sectoral models, such as energy and agriculture. [ABSTRACT FROM AUTHOR]

This article investigates projected changes in temperature and water cycle extremes at 1.5°C of global warming, and highlights the role of land processes and land-use changes (LUCs) for these projections. We provide new comparisons of changes in climate at 1.5°C versus 2°C based on empirical sampling analyses of transient simulations versus simulations from the 'Half a degree Additional warming, Prognosis and Projected Impacts' (HAPPI) multimodel experiment. The two approaches yield similar overall results regarding changes in climate extremes on land, and reveal a substantial difference in the occurrence of regional extremes at 1.5°C versus 2°C. Land processes mediated through soil moisture feedbacks and land-use forcing play a major role for projected changes in extremes at 1.5°C in most midlatitude regions, including densely populated areas in North America, Europe and Asia. This has important implications for low-emissions scenarios derived from integrated assessment models (IAMs), which include major LUCs in ambitious mitigation pathways (e.g. associated with increased bioenergy use), but are also shown to differ in the simulated LUC patterns. Biogeophysical effects from LUCs are not considered in the development of IAM scenarios, but play an important role for projected regional changes in climate extremes, and are thus of high relevance for sustainable development pathways. [ABSTRACT FROM AUTHOR]

Droughts can have strong environmental and socio-economic impacts in the Mediterranean region, in particular for countries relying on rain-fed agricultural production, but also in areas in which irrigation plays an important role and in which natural vegetation has been modified or is subject to water stress. The purpose of this review is to provide an assessment of the complexity of the drought phenomenon in the Mediterranean region and present various perspectives on drought in the present and under future climate change scenarios. The projections of various model experiments on future climate change scenarios strongly agree on an increased frequency and severity of droughts in the Mediterranean basin. Nevertheless, given the complexity of the phenomenon, with different types of droughts and complex interrelated impacts, significant future uncertainties remain. For example, uncertainties are stronger for hydrological droughts than meteorological droughts due to human influences and water withdrawal. Significant drought impacts are expected in the future, in particular for developing countries in the southern and eastern parts of the Mediterranean basin. To improve the resilience and adaptive capacities of societies and environments faced with drought, we aim to provide an overview of the key issues in research on climate change impacts on droughts, with a specific focus on the Mediterranean region, in order to: i) redefine more meaningful drought metrics tailored to the Mediterranean context, ii) better take into account vegetation and its feedback on droughts, iii) improve the modelling and forecasting of drought events through remote sensing and land surface models, and iv) promote a more integrated vision of droughts taking into account both water availability and water use. This overview reflects the complexity of the problem and the need to combine scientific research with adaptation solutions to deal with drought in the future. [ABSTRACT FROM AUTHOR]

This presentation will provide an overview on changes in climate extremes at 1.5°C vs 2°C global warming, based on chapter 3 of the IPCC Special Report on 1.5°C global warming (Hoegh-Guldberg et al. 2019) as well as on several recent publications (Seneviratne et al. 2016; Wartenburger et al. 2017; Seneviratne et al. 2018a,b). There are substantial differences in regional climate means and extremes at 1.5°C vs 2°C global warming, with up to 2-3 times larger increases in hot extremes over land compared to the global mean temperature. There are also detectable differences in heavy precipitation and droughts in some regions. Limiting global warming to 1.5°C avoids substantial risks compared to higher levels of warming, including irreversible impacts. However, it does not completely remove the risk of some regional extremes reaching dangerous levels for ecosystems and societies in the coming decades.References: Hoegh-Guldberg, O., D. Jacob, M. Taylor, M. Bindi, S. Brown, I. Camilloni, A. Diedhiou, R. Djalante, K. Ebi, F. Engelbrecht, J. Guiot, Y. Hijioka, S. Mehrotra, A. Payne, S. I. Seneviratne, A. Thomas, R. Warren, G. Zhou, 2019, Impacts of 1.5ºC global warming on natural and human systems. In: Global warming of 1.5°C. An IPCC special report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [V. Masson-Delmotte, et al. (eds.)]. In Press.Seneviratne, S.I., M. Donat, A.J. Pitman, R. Knutti, and R.L. Wilby, 2016: Allowable CO2 emissions based on regional and impact-related climate targets. Nature, 529, 477-483, doi:10.1038/nature16542.Seneviratne, S.I., J. Rogelj, R. Séférian, R. Wartenburger, M.R. Allen, M. Cain, R.J. Millar, K.L. Ebi, N. Ellis, O. Hoegh-Guldberg, A.J. Payne, C.-F. Schleussner, P. Tschakert, R.F. Warren, 2018a: The many possible climates from the Paris Agreement's aim of 1.5°C warming. Nature. 558, 41-49. Seneviratne, S.I., R. Wartenburger, B.P. Guillod, A.L. Hirsch, M.M. Vogel, V. Brovkin, D.P. van Vuuren, N. Schaller, L. Boysen, K.V. Calvin, J. Doelman, P. Greve, P. Havlik, F. Humpenöder, T. Krisztin, D. Mitchell, A. Popp, K. Riahi, J. Rogelj, C.-F. Schleussner, J. Sillmann, E. Stehfest, 2018b: Climate extremes, land-climate feedbacks and land-use forcing at 1.5°C. Phil. Trans. R. Soc. A. 376.Wartenburger, H., H. Hirschi, M.G. Donat, P. Greve, A.J. Pitman, S.I. Seneviratne, 2017: Changes in regional climate extremes as a function of global mean temperature: an interactive plotting framework. Geoscientific Model Development, 10, 3609-3634. [ABSTRACT FROM AUTHOR]