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The impact of climate on topography, which is a theme in landscape evolution studies, has been demonstrated, mostly, at mountain range scales and across climate zones. However, in drylands, spatiotemporal discontinuities of rainfall and the crucial role of extreme rainstorms raise questions and challenges in identifying climate properties that govern surface processes. Here, we combine methods to examine hyperarid escarpment sensitivity to storm‐scale forcing. Using a high‐resolution DEM and field measurements, we analyzed the topography of a 40‐km‐long escarpment in the Negev desert (Israel). We also used rainfall intensity data from a convection‐permitting numerical weather model for storm‐scale statistical analysis. We conducted hydrological simulations of synthetic rainstorms, revealing the frequency of sediment mobilization along the sub‐cliff slopes. Results show that cliff gradients along the hyperarid escarpment increase systematically from the wetter (90 mm yr−1) southwestern to the drier (45 mm yr−1) northeastern sides. Also, sub‐cliff slopes at the southwestern study site are longer and associated with milder gradients and coarser sediments. Storm‐scale statistical analysis reveals a trend of increasing extreme (>10 years return‐period) intensities toward the northeast site, opposite to the trend in mean annual rainfall. Hydrological simulations based on these statistics indicate a higher frequency of sediment mobilization in the northeast, which can explain the pronounced topographic differences between the sites. The variations in landscape and rainstorm properties across a relatively short distance highlight the sensitivity of arid landforms to extreme events. Plain Language Summary: Identifying the link between climatic properties and topography helps to elucidate key controlling processes affecting Earth's landscape evolution. In drylands, however, it is harder to associate topography with average climate as surface processes are significantly affected by spatially disconnected, discrete and short‐duration rainstorms. In this study, we investigate whether variations in rainstorm properties can account for topographic differences along a 40‐km‐long hyperarid escarpment. Results indicate that in the drier parts of the escarpment, cliffs are steeper, and slopes are covered by finer sediment. Rainstorm statistical analysis indicates that the drier part of the escarpment is associated with extreme rainstorms characterized by higher rainfall intensities. We demonstrate that these storms mobilize sediment more frequently in the drier part of the escarpment, which can explain the pronounced topographic differences along the escarpment. This study presents a rarely‐recognized link between small‐scale variations in arid rainstorm properties and topography and highlights the susceptibility of arid landscapes to extreme rainstorms. Key Points: The morphology of cliffs and sub‐cliff slopes varies significantly with rainfall along a 40‐km hyperarid escarpmentThe observed morphologic variability is triggered primarily by differences in sediment mobilization frequencies on sub‐cliff slopesRainstorm properties in arid landscapes could change over relatively short distances and leave a significant geomorphic imprint [ABSTRACT FROM AUTHOR]

End of century projections from Coupled Model Intercomparison Project (CMIP) models show a decrease in precipitation over subtropical oceans that often extends into surrounding land areas, but with substantial intermodel spread. Changes in precipitation are controlled by both thermodynamical and dynamical processes, though the importance of these processes for regional scales and for intermodel spread is not well understood. The contribution of dynamic and thermodynamic processes to the model spread in regional precipitation minus evaporation (P − E) is computed for 48 CMIP models. The intermodel spread is dominated essentially everywhere by the change of the dynamic term, including in most regions where thermodynamic changes drive the multi‐model mean response. The dominant role of dynamic changes is insensitive to zonal averaging which removes any influence of stationary wave changes, and is also evident in subtropical oceanic regions. Relatedly, intermodel spread in P − E is generally unrelated to climate sensitivity. Plain Language Summary: Earth's hydroclimate will change in response to increased greenhouse gas concentrations. However, the physical process(es) whereby climate change leads to these hydroclimate change is still uncertain, especially on regional scales. Furthermore, models disagree as to the magnitude of these hydroclimate changes, and the causes of this intermodel spread are also not well understood. We demonstrate that uncertainty in future changes is driven almost everywhere by changes in the large scale winds, while the precise amount of warming simulated by a given model is largely irrelevant. This highlights that reducing uncertainty in future hydroclimate changes requires primarily narrowing uncertainties in the circulation response. Key Points: Even in regions where thermodynamic changes drive the multi‐model mean hydrologic changes, dynamic changes drive uncertaintyDynamic changes more important for intermodel spread even after zonal averaging and even over subtropical oceansNarrowing climate sensitivity will not help constrain future hydroclimate changes in most regions [ABSTRACT FROM AUTHOR]

Annual and decadal-scale hydroclimatic variability describes key characteristics that are embedded into climate in situ and is of prime importance in subtropical regions. The study of hydroclimatic variability is therefore crucial to understand its manifestation and implications for climate derivatives such as hydrological phenomena and water availability. However, the study of this variability from modern records is limited due to their relatively short span, whereas model simulations relying on modern dynamics could misrepresent some of its aspects. Here we study annual to decadal hydroclimatic variability in the Levant using two sedimentary sections covering ∼ 700 years each, from the depocenter of the Dead Sea, which has been continuously recording environmental conditions since the Pleistocene. We focus on two series of annually deposited laminated intervals (i.e., varves) that represent two episodes of opposing mean climates, deposited during MIS2 lake-level rise and fall at ∼ 27 and 18 ka, respectively. These two series comprise alternations of authigenic aragonite that precipitated during summer and flood-borne detrital laminae deposited by winter floods. Within this record, aragonite laminae form a proxy of annual inflow and the extent of epilimnion dilution, whereas detrital laminae are comprised of sub-laminae deposited by individual flooding events. The two series depict distinct characteristics with increased mean and variance of annual inflow and flood frequency during "wetter", with respect to the relatively "dryer", conditions, reflected by opposite lake-level changes. In addition, decades of intense flood frequency (clusters) are identified, reflecting the in situ impact of shifting centennial-scale climate regimes, which are particularly pronounced during wetter conditions. The combined application of multiple time series analyses suggests that the studied episodes are characterized by weak and non-significant cyclical components of sub-decadal frequencies. The interpretation of these observations using modern synoptic-scale hydroclimatology suggests that Pleistocene climate changes resulted in shifts in the dominance of the key synoptic systems that govern rainfall, annual inflow and flood frequency in the eastern Mediterranean Sea over centennial timescales. [ABSTRACT FROM AUTHOR]

Projections of extreme precipitation based on modern climate models suffer from large uncertainties. Specifically, unresolved physics and natural variability limit the ability of climate models to provide actionable information on impacts and risks at the regional, watershed and city scales relevant for practical applications. Here, we show that the interaction of precipitating systems with local features can constrain the statistical description of extreme precipitation. These observational constraints can be used to project local extremes of low yearly exceedance probability (e.g., 100‐year events) using synoptic‐scale information from climate models, which is generally represented more accurately than the local scales, and without requiring climate models to explicitly resolve extremes. The novel approach, demonstrated here over the south‐eastern Mediterranean, offers a path for improving the predictability of local statistics of extremes in a changing climate, independent of pending improvements in climate models at regional and local scales. Plain Language Summary: Climate change impact studies are currently restrained by the limited accuracy of climate models in resolving precipitation extremes, by the computational power required to run high‐resolution models, and by the uncertainties characterizing their analysis. Here, we present a novel approach which permits to project extreme precipitation for future climatic scenarios based on the combination of coarse‐scale information from climate models with local observations. Focusing on the south‐eastern Mediterranean, we provide projections of precipitation extremes which could not yet be derived using traditional methods, such as the events occurring on average once in 100 years. The combined effect of changes in intensity and occurrence of two dominant synoptic systems is projected to increase the intensity of the 100‐year events in the coast and in the desert areas of the region and to decrease it elsewhere. The novel approach offers a path for improving the predictability of extremes in a changing climate, independent of pending improvements in climate models. Key Points: Local observations can be used to constrain the intensity distribution of precipitation events associated with different synoptic systemsThe constraints allow projecting extreme return levels at scales relevant for impact studies from synoptic information from climate modelsThe approach improves the predictability of local extremes, independent of improvements in climate models at regional and local scales [ABSTRACT FROM AUTHOR]

While CMIP5 models robustly project drying of the subtropics and more precipitation in the tropics and subpolar latitudes by the end of the century, the magnitude of these changes in precipitation varies widely across models: for example, some models simulate no drying in the eastern Mediterranean while others simulate more than a 50% reduction in precipitation relative to the model-simulated present-day value. Furthermore, the factors leading to changes in local subtropical precipitation remain unclear. The importance of zonal-mean changes in atmospheric structure for local precipitation changes is explored in 42 CMIP5 models. It is found that up to half of the local intermodel spread over the Mediterranean, northern Mexico, East Asia, southern Africa, southern Australia, and southern South America is related to the intermodel spread in large-scale processes such as the magnitude of globally averaged surface temperature increases, Hadley cell widening, polar amplification, stabilization of the tropical upper troposphere, or changes in the polar stratosphere.Globally averaged surface temperature increases account for intermodel spread in land subtropical drying in the Southern Hemisphere but are not important for land drying adjacent to the Mediterranean. The factors associated with drying over the eastern Mediterranean and westernMediterranean differ, with stabilization of the tropical upper troposphere being a crucial factor for the former only. Differences in precipitation between the western and eastern Mediterranean are also evident on interannual time scales. In contrast, the global factors examined here are unimportant over most of the United States, and more generally over the interior of continents. Much of the rest of the spread can be explained by variations in local relative humidity, a proxy also for zonally asymmetric circulation and thermodynamic changes. [ABSTRACT FROM AUTHOR]

In this study we investigate the scaling of precipitation extremes with temperature in the Mediterranean region by assessing against observations the present day and future regional climate simulations performed in the frame of the HyMeX and MED-CORDEX programs. Over the 1979-2008 period, despite differences in quantitative precipitation simulation across the various models, the change in precipitation extremes with respect to temperature is robust and consistent. The spatial variability of the temperature-precipitation extremes relationship displays a hook shape across the Mediterranean, with negative slope at high temperatures and a slope following Clausius-Clapeyron (CC)-scaling at low temperatures. The temperature at which the slope of the temperature-precipitation extreme relation sharply changes (or temperature break), ranges from about 20 °C in the western Mediterranean to <10 °C in Greece. In addition, this slope is always negative in the arid regions of the Mediterranean. The scaling of the simulated precipitation extremes is insensitive to ocean-atmosphere coupling, while it depends very weakly on the resolution at high temperatures for short precipitation accumulation times. In future climate scenario simulations covering the 2070-2100 period, the temperature break shifts to higher temperatures by a value which is on average the mean regional temperature change due to global warming. The slope of the simulated future temperature-precipitation extremes relationship is close to CC-scaling at temperatures below the temperature break, while at high temperatures, the negative slope is close, but somewhat flatter or steeper, than in the current climate depending on the model. Overall, models predict more intense precipitation extremes in the future. Adjusting the temperature-precipitation extremes relationship in the present climate using the CC law and the temperature shift in the future allows the recovery of the temperature-precipitation extremes relationship in the future climate. This implies negligible regional changes of relative humidity in the future despite the large warming and drying over the Mediterranean. This suggests that the Mediterranean Sea is the primary source of moisture which counteracts the drying and warming impacts on relative humidity in parts of the Mediterranean region. [ABSTRACT FROM AUTHOR]

Intensity-duration-frequency (IDF) curves are widely used to quantify the probability of occurrence of rainfall extremes. The usual rain gauge-based approach provides accurate curves for a specific location, but uncertainties arise when ungauged regions are examined or catchment-scale information is required. Remote sensing rainfall records, e.g. from weather radars and satellites, are recently becoming available, providing high-resolution estimates at regional or even global scales; their uncertainty and implications on water resources applications urge to be investigated. This study compares IDF curves from radar and satellite (CMORPH) estimates over the eastern Mediterranean (covering Mediterranean, semiarid, and arid climates) and quantifies the uncertainty related to their limited record on varying climates. We show that radar identifies thicker-tailed distributions than satellite, in particular for short durations, and that the tail of the distributions depends on the spatial and temporal aggregation scales. The spatial correlation between radar IDF and satellite IDF is as high as 0.7 for 2-5-year return period and decreases with longer return periods, especially for short durations. The uncertainty related to the use of short records is important when the record length is comparable to the return period ( ~50, ~100, and ~150% for Mediterranean, semiarid, and arid climates, respectively). The agreement between IDF curves derived from different sensors on Mediterranean and, to a good extent, semiarid climates, demonstrates the potential of remote sensing datasets and instils confidence on their quantitative use for ungauged areas of the Earth. [ABSTRACT FROM AUTHOR]

The typical short generation length of insects makes their population dynamics highly sensitive not only to mean annual temperatures but also to their intra-annual variations. To consider the combined effect of both thermal factors under global warming, we propose a modeling framework that links general circulation models (GCMs) with a stochastic weather generator and population dynamics models to predict species population responses to inter- and intra-annual temperature changes. This framework was utilized to explore future changes in populations of Bemisia tabaci, an invasive insect pest-species that affects multiple agricultural systems in the Mediterranean region. We considered three locations representing different pest status and climatic conditions: Montpellier (France), Seville (Spain), and Beit-Jamal (Israel). We produced ensembles of local daily temperature realizations representing current and future (mid-21st century) climatic conditions under two emission scenarios for the three locations. Our simulations predicted a significant increase in the average number of annual generations and in population size, and a significant lengthening of the growing season in all three locations. A negative effect was found only in Seville for the summer season, where future temperatures lead to a reduction in population size. High variability in population size was observed between years with similar annual mean temperatures, suggesting a strong effect of intra-annual temperature variation. Critical periods were from late spring to late summer in Montpellier and from late winter to early summer in Seville and Beit-Jamal. Although our analysis suggested that earlier seasonal activity does not necessarily lead to increased populations load unless an additional generation is produced, it is highly likely that the insect will become a significant pest of open-fields at Mediterranean latitudes above 40° during the next 50 years. Our simulations also implied that current predictions based on mean temperature anomalies are relatively conservative and it is better to apply stochastic tools to resolve complex responses to climate change while taking natural variability into account. In summary, we propose a modeling framework capable of determining distinct intra-annual temperature patterns leading to large or small population sizes, for pest risk assessment and management planning of both natural and agricultural ecosystems. [ABSTRACT FROM AUTHOR]

Summary Intensity–Duration–Frequency (IDF) curves are widely used in flood risk management because they provide an easy link between the characteristics of a rainfall event and the probability of its occurrence. Weather radars provide distributed rainfall estimates with high spatial and temporal resolutions and overcome the scarce representativeness of point-based rainfall for regions characterized by large gradients in rainfall climatology. This work explores the use of radar quantitative precipitation estimation (QPE) for the identification of IDF curves over a region with steep climatic transitions (Israel) using a unique radar data record (23 yr) and combined physical and empirical adjustment of the radar data. IDF relationships were derived by fitting a generalized extreme value distribution to the annual maximum series for durations of 20 min, 1 h and 4 h. Arid, semi-arid and Mediterranean climates were explored using 14 study cases. IDF curves derived from the study rain gauges were compared to those derived from radar and from nearby rain gauges characterized by similar climatology, taking into account the uncertainty linked with the fitting technique. Radar annual maxima and IDF curves were generally overestimated but in 70% of the cases (60% for a 100 yr return period), they lay within the rain gauge IDF confidence intervals. Overestimation tended to increase with return period, and this effect was enhanced in arid climates. This was mainly associated with radar estimation uncertainty, even if other effects, such as rain gauge temporal resolution, cannot be neglected. Climatological classification remained meaningful for the analysis of rainfall extremes and radar was able to discern climatology from rainfall frequency analysis. [ABSTRACT FROM AUTHOR]

ABSTRACT The effect of climate change on the Eastern Mediterranean (EM) region, a region that reflects a transition between Mediterranean and semi-arid climates, was examined. This transition region is affected by global changes such as the expansion of the Hadley cell, which leads to a poleward shift of the subtropical dry zone. The Hadley cell expansion forces the migration of jet streams and storm tracks poleward from their standard course, potentially increasing regional desertification. This article focuses on the northern coastline of Israel along the EM region where most wet synoptic systems (i.e. systems that may lead to precipitation) are generated. The current climate was compared to the predicted mid-21st century climate based on Intergovernmental Panel on Climate Change (IPCC) Representative Concentration Pathway (RCP) RCP4.5 and RCP8.5 scenarios using four Coupled Model Intercomparison Project Phase 5 (CMIP5) models. A warming of 1.1-2.6 °C was predicted for this region. The models predicted that rain in the region will become less frequent, with a reduction of 1.2-3.4% in 6-h intervals classified as wet synoptic systems and a 10-22% reduction in wet events. They further predicted that the maximum wet event duration in the mid-21st century would become shorter relative to the current climate, implying that extremely long wet systems will become less frequent. Three of the models predicted shrinking of the wet season length by up to 15%. All models predicted an increasing occurrence frequency of Active Red Sea Troughs (ARSTs) for the RCP8.5 scenario by up to 11% by the mid-21st century. For the RCP4.5 scenario, a similar increase of up to 6% was predicted by two of the models. [ABSTRACT FROM AUTHOR]

Fresh water resources, human societies, and ecosystems are expected to be strongly impacted by climate change, with precipitation trends being one of the most important elements that will be closely monitored. However, the natural variability of precipitation data can often mask existing trends such that the results appear as statistically insignificant. Information on the limitations of trend detection is important for risk assessment and for decision making related to adaption strategies under inherent uncertainties. This paper reports on an effort to quantify and map minimal detectable absolute trends in annual precipitation data series on a global scale. Monte Carlo simulations were conducted to generate realizations of trended precipitation data for different precipitation means and coefficients of variance, and the Mann-Kendall method was applied for detecting the trend significance. Global Precipitation Climatology Centre (GPCC) VASClimO data was used to compute the mean and coefficient of variance of annual precipitation over land and to map minimal detectable absolute trends. It was found that relatively high magnitude trends (positive or negative) have a low chance of being detected as a result of high natural variance of the precipitation data. The largest undetectable trends were found for the tropics. Arid and semiarid regions also present high relative values in terms of percent change from the mean annual precipitation. Although the present analysis is based on several simplified assumptions, the goal was to point out an inherent problem of potentially undetectable high absolute trends that must be considered in analyzing precipitation data series and assessing risks in adaption strategies to climate change. [ABSTRACT FROM AUTHOR]

Highlights • Simplified formulation of the metastatistical extreme value is proposed. • Parsimonious handling of extremes emerging from multiple underlying processes. • Computationally efficient sensitivity analyses to changes in the underlying processes. • Robust framework for explanatory models, nonstationary analyses, climate projections. Abstract This paper presents a Simplified Metastatistical Extreme Value formulation (SMEV) able to model hydro-meteorological extremes emerging from multiple underlying processes. The formulation explicitly includes the average intensity and probability of occurrence of the processes allowing to parsimoniously model changes in these quantities to quantify changes in the probability of occurrence of extremes. SMEV allows (a) frequency analyses of extremes emerging from multiple underlying processes and (b) computationally efficient analyses of the sensitivity of extreme quantiles to changes in the characteristics of the underlying processes; moreover, (c) it provides a robust framework for explanatory models, nonstationary frequency analyses, and climate projections. The methodology is applied to daily precipitation data from long recording stations in the eastern Mediterranean, using Weibull distributions to model daily precipitation amounts generated by two classes of synoptic systems. At-site application of SMEV provides spatially consistent estimates of extreme quantiles, in line with regional GEV estimates and generally characterized by reduced uncertainties. The sensitivity of extreme quantiles to changes and uncertainty in the intensity and yearly occurrences of events generated by different synoptic classes is examined, and an application of SMEV for the projection of future extremes is provided. [ABSTRACT FROM AUTHOR]

Highlights • A 50-m spatial resolution wheat production model for semiarid regions is developed. • Model integrates 3 sub-models: crop growth, soil hydrology and runoff models on GIS. • Model was evaluated vs SWC, biomass (AGB) and grain yield (GY) in 22 fields in Israel. • Model shows sensitivity to spatial variations in soils (SWC) and rain (AGB and GY). • Significant AGB and GY decreases were predicted for projected warming and drought in the study area. Abstract Current literature suggests that wheat production models are limited either to wide-scale or plot-based predictions ignoring pattern of habitat conditions and surficial hydrological processes. We present here a high-spatial resolution (50 m) non-calibrated GIS-based wheat production model for predictions of aboveground wheat biomass (AGB) and grain yield (GY). The model is an integration of three sub-models, each simulating elemental processes relevant for wheat growth dynamics in water-limited environments: (1) HYDRUS-1D, a finite element model that simulates one-dimensional movement of water in the soil profile; (2) a two-dimensional GIS-based surface runoff model; and (3) a one-dimensional process-driven mechanistic wheat growth model. By integrating the three sub-models, we aimed to achieve a more accurate spatially continuous water balance simulation with a better representation of root zone soil water content (SWC) impacts on plant development. High-resolution grid-based rainfall data from a meteorological radar system were used as input to HYDRUS-1D. Twenty-two commercial wheat fields in Israel were used to validate the model in two seasons (2010/11 and 2011/12). Results show that root zone SWC was accurately simulated by HYDRUS-1D in both seasons, particularly at the top 10-cm soil layer. Observed vs simulated AGB and GY were highly correlated with R2 = 0.93 and 0.72 (RMSE = 171 g m−2 and 70 g m−2) having low biases of -41 g m−2 (8%) and 52 g m−2 (10%), respectively. Model sensitivity test showed that HYDRUS-1D was mainly driven by spatial variability in the input soil characteristics while the integrated wheat production model was mostly affected by rainfall spatial variability indicating the importance of using accurate high-resolution rainfall data as model input. Using the integrated model, we predict decreases in AGB and GY of c. 10.5% and c. 12%, respectively, for 1 °C of warming and c. 7.7% and c. 7.3% for 5% reduction in rainfall amount in our study sites. The suggested model could be used by scientists to better understand the causes of spatial and temporal variability in wheat production and the consequences of future scenarios such as climate change. [ABSTRACT FROM AUTHOR]