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Flash floods are among the most devastating and lethal natural hazards. In 2018, three flash-flood episodes resulted in 46 casualties in the deserts of Israel and Jordan alone. This paper presents the hydrometeorological analysis and forecasting of a substantial storm (25–27 April 2018) that hit an arid desert basin (Zin, ∼1400 km2, southern Israel) claiming 12 human lives. This paper aims to (a) spatially assess the severity of the storm, (b) quantify the timescale of the hydrological response, and (c) evaluate the available operational precipitation forecasting. Return periods of the storm's maximal rain intensities were derived locally at 1 km2 resolution using weather radar data and a novel statistical methodology. A high-resolution grid-based hydrological model was used to study the intra-basin flash-flood magnitudes which were consistent with direct information from witnesses. The model was further used to examine the hydrological response to different forecast scenarios. A small portion of the basin (1 %–20 %) experienced extreme precipitation intensities (75- to 100-year return period), resulting in a local hydrological response of a high magnitude (10- to 50-year return period). Hillslope runoff, initiated minutes after the intense rainfall occurred, reached the streams and resulted in peak discharge within tens of minutes. Available deterministic operational precipitation forecasts poorly predicted the hydrological response in the studied basins (tens to hundreds of square kilometers) mostly due to location inaccuracy. There was no gain from assimilating radar estimates in the numerical weather prediction model. Therefore, we suggest using deterministic forecasts with caution as it might lead to fatal decision making. To cope with such errors, a novel cost-effective methodology is applied by spatially shifting the forecasted precipitation fields. In this way, flash-flood occurrences were captured in most of the subbasins, resulting in few false alarms.

In this paper we studied the impact of winds on Arctic sea ice through the ocean's memory by using numerical simulations. We found that the changes in halosteric height induced by wind perturbations can significantly affect the Arctic sea ice drift, thickness, concentration and deformation rates regionally even years after the wind perturbations. Changes in the Arctic liquid freshwater content and thus in halosteric height can cause changes in the sea surface height and surface geostrophic currents, which further enforce a lasting and strong impact on sea ice. The changes in both sea surface height gradient force (due to changes in sea surface height) and ice–ocean stress (due to changes in surface geostrophic currents) are found to be important in determining the overall ocean effects. The revealed ocean effects are mainly associated with changes in sea ice dynamics, not thermodynamics. Depending on the preceding atmospheric mode driving the ocean, the ocean's memory of the wind forcing can lead to changes in Arctic sea ice characteristics with very different spatial patterns. We obtained these spatial patterns associated with Arctic Oscillation, Arctic Dipole Anomaly and Beaufort High modes through dedicated numerical simulations. The dynamical impact of the ocean has strong seasonal variations, stronger in summer and weaker in winter and spring. This implies that declining trends of Arctic sea ice will very possibly allow a stronger ocean impact on the sea ice in a warming climate.

Streamflow regime is sensitive to changes in land use and climate in a river basin. Quantifying the isolated and integrated impacts of land use and climate change on streamflow is challenging as well as crucial to optimally manage water resources in the river basin. This paper presents a simple hydrologic modelling based approach to segregate the impacts of land use and climate change on streamflow of a river basin. The upper Ganga basin in India is selected as the case study to carry out the analysis. Streamflow in the river basin is modelled using a calibrated variable infiltration capacity hydrologic model. The approach involves development of three scenarios to understand the influence of land use and climate on streamflow. The first scenario assesses the sensitivity of streamflow to land use changes under invariant climate. The second scenario determines the change in streamflow due to change in climate assuming constant land use. The third scenario estimates the combined effect of changing land use and climate over streamflow of the basin. Based on the results obtained from the three scenarios, quantification of isolated impacts of land use and climate change on streamflow is addressed. Future projections of climate are obtained from dynamically downscaled simulations of six general circulation models (GCMs) available from the Coordinated Regional Downscaling Experiment (CORDEX) project. Uncertainties associated with the GCMs and emission scenarios are quantified in the analysis. Results for the case study indicate that streamflow is highly sensitive to change in urban area and moderately sensitive to change in crop land area. However, variations in streamflow generally reproduce the variations in precipitation. Combined effect of land use and climate on streamflow is observed to be more pronounced compared to their individual impacts in the basin. It is observed from the isolated effects of land use and climate change that climate has a more dominant impact on streamflow in the region. The approach proposed in this paper is applicable to any river basin to isolate the impacts of land use change and climate change on the streamflow.

In recent years many methods for statistical downscaling of the precipitation climate model outputs have been developed. Statistical downscaling is performed under general and method-specific (structural) assumptions but those are rarely evaluated simultaneously. This paper illustrates the verification and evaluation of the downscaling assumptions for a weather typing method. Using the observations and outputs of a global climate model ensemble, the skill of the method is evaluated for precipitation downscaling in central Belgium during the winter season (December to February). Shortcomings of the studied method have been uncovered and are identified as biases and a time-variant predictor–predictand relationship. The predictor–predictand relationship is found to be informative for historical observations but becomes inaccurate for the projected climate model output. The latter inaccuracy is explained by the increased importance of the thermodynamic processes in the precipitation changes. The results therefore question the applicability of the weather typing method for the case study location. Besides the shortcomings, the results also demonstrate the added value of the Clausius–Clapeyron relationship for precipitation amount scaling. The verification and evaluation of the downscaling assumptions are a tool to design a statistical downscaling ensemble tailored to end-user needs.

Made available in DSpace on 2015-10-21T20:46:27Z (GMT). No. of bitstreams: 0 Previous issue date: 2015-01-01. Added 1 bitstream(s) on 2015-10-22T09:52:58Z : No. of bitstreams: 1 WOS000352160900018.pdf: 818294 bytes, checksum: 55e5cbde8ea1d579dac8df04f359611a (MD5) National Climate Center of Algeria Climatology Group Research group 443 GenCat at the University of Barcelona In this paper, the spatial and temporal distribution of the daily precipitation concentration index (CI) in Algeria (south Mediterranean Sea) has been assessed. CI is an index related to the rainfall intensity and erosive capacity; therefore, this index is of great interest for studies on torrential rainfall and floods. Forty-two daily rainfall series based on high-quality and fairly regular rainfall records for the period from 1970 to 2008 were used. The daily precipitation CI results allowed the identification of three climate zones: the northern country, characterized by coastal regions with CI values between 0.59 and 0.63; the highlands, with values between 0.57 and 0.62, except for the region of Biskra (CI = 0.70); and the southern region of the country, with high rainfall concentrations with values between 0.62 and 0.69. Natl Meteorol Off, Natl Climate Ctr, Algiers, Algeria Univ Barcelona, Climatol Grp, Barcelona 08001, Spain UNESP Ourinhos, Dept Geog, BR-1500 Sao Paulo, Brazil UNESP Ourinhos, Dept Geog, BR-1500 Sao Paulo, Brazil National Climate Center of Algeria: CGL2011-29263-C02-01 Climatology Group: CGL2011-29263-C02-01

Anticipation and preparedness for large-scale flood events have a key role in mitigating their impact and optimizing the strategic planning of water resources. Although several developed countries have well-established systems for river monitoring and flood early warning, figures of population affected every year by floods in developing countries are unsettling. This paper presents the Global Flood Awareness System, which has been set up to provide an overview on upcoming floods in large world river basins. The Global Flood Awareness System is based on distributed hydrological simulation of numerical ensemble weather predictions with global coverage. Streamflow forecasts are compared statistically to climatological simulations to detect probabilistic exceedance of warning thresholds. In this article, the system setup is described, together with an evaluation of its performance over a two-year test period and a qualitative analysis of a case study for the Pakistan flood, in summer 2010. It is shown that hazardous events in large river basins can be skilfully detected with a forecast horizon of up to 1 month. In addition, results suggest that an accurate simulation of initial model conditions and an improved parameterization of the hydrological model are key components to reproduce accurately the streamflow variability in the many different runoff regimes of the Earth., JRC.H.7-Climate Risk Management

The transport and storage of heat by the ocean is of crucial importance because of its effect on ocean dynamics and its impact on the atmosphere, climate and climate change. Unfortunately, limits to the amount of data that can be collected and stored mean that many experimental and modelling studies of the heat budget have to make use of mean datasets where the effects of short term fluctuations are lost. In this paper we investigate the magnitude of the resulting errors by making use of data from OCCAM, a high resolution global ocean model. The model carries out a proper heat balance every time step so any imbalances that are found in the analysis must result from the use of mean fields. The study concentrates on two areas of the ocean affecting the El Nino. The first is the region of tropical instability waves north of the Equator. The second is in the upwelling region along the Equator. It is shown that in both cases, processes with a period of less than five days can have a significant impact on the heat budget. Thus, analyses using data averaged over five days or more are likely to have significant errors. It is also shown that if a series of instantaneous values is available, reasonable estimates can be made of the size of the errors. In model studies, such values are available in the form of the datasets used to restart the model. In experimental studies they may be in the form of individual unaveraged observations.

ECOOP project allowed the improvement of the regional and coastal operational forecasting systems for the different European Seas. In the Iberia-Biscay-Ireland area (IBI) a regional system has been developed and improved for the project in order to provide IBI partners the best initial and boundary conditions to their embedded coastal systems. End users could also get access to the regional hindcasts and forecasts through the ECOOP website. This system has been upgraded as follow: a first existing version V0, a second base-line version V1 ready for the ECOOP Target Operational Period and a third one V2 which consists in a new generation regional system. This paper especially pays attention to the improvements from the V1 system, whose physics are close to a large scale basin system, to the V2 one which physics are more adapted to shelf and coastal issues. Strong developments which allow further regional physics resolution in the NEMO OGCM such as tide, non linear free surface and adapted vertical mixing scheme among other have been carried out from V1 to V2 versions for the project. Thus, regional thermal fronts due to tidal mixing appear in V2 solution and are quite well placed. Moreover, simulation of the stratification in shelf areas is also improved in V2.

A fundamental problem in paleoclimatology is to take fully into account the various error sources when examining proxy records with quantitative methods of statistical time series analysis. Records from dated climate archives such as speleothems add extra uncertainty from the age determination to the other sources that consist in measurement and proxy errors. This paper examines three stalagmite time series of oxygen isotopic composition (δ18O) from two caves in western Germany, the series AH-1 from the Atta Cave and the series Bu1 and Bu4 from the Bunker Cave. These records carry regional information about past changes in winter precipitation and temperature. U/Th and radiocarbon dating reveals that they cover the later part of the Holocene, the past 8.6 thousand years (ka). We analyse centennial- to millennial-scale climate trends by means of nonparametric Gasser–Müller kernel regression. Error bands around fitted trend curves are determined by combining (1) block bootstrap resampling to preserve noise properties (shape, autocorrelation) of the δ18O residuals and (2) timescale simulations (models StalAge and iscam). The timescale error influences on centennial- to millennial-scale trend estimation are not excessively large. We find a "mid-Holocene climate double-swing", from warm to cold to warm winter conditions (6.5 ka to 6.0 ka to 5.1 ka), with warm–cold amplitudes of around 0.5‰ δ18O; this finding is documented by all three records with high confidence. We also quantify the Medieval Warm Period (MWP), the Little Ice Age (LIA) and the current warmth. Our analyses cannot unequivocally support the conclusion that current regional winter climate is warmer than that during the MWP.

The AMMA (African Monsoon Multidisciplinary Analysis) program is dedicated to providing a better understanding of the West African monsoon and its influence on the physical, chemical and biological environment regionally and globally, as well as relating variability of this monsoon system to issues of health, water resources, food security and demography for West African nations. Within this framework, an intensive field campaign took place during the summer of 2006 to better document specific processes and weather systems at various key stages of this monsoon season. This campaign was embedded within a longer observation period that documented the annual cycle of surface and atmospheric conditions between 2005 and 2007. The present paper provides a large and regional scale overview of the 2006 summer monsoon season, that includes consideration of of the convective activity, mean atmospheric circulation and synoptic/intraseasonal weather systems, oceanic and land surface conditions, continental hydrology, dust concentration and ozone distribution. The 2006 African summer monsoon was a near-normal rainy season except for a large-scale rainfall excess north of 15° N. This monsoon season was also characterized by a 10-day delayed onset compared to climatology, with convection becoming developed only after 10 July. This onset delay impacted the continental hydrology, soil moisture and vegetation dynamics as well as dust emission. More details of some less-well-known atmospheric features in the African monsoon at intraseasonal and synoptic scales are provided in order to promote future research in these areas.

Climate change is expected to strongly impact the hydrological and thermal regimes of Alpine rivers within the coming decades. In this context, the development of hydrological models accounting for the specific dynamics of Alpine catchments appears as a one of the promising approaches to reduce our uncertainty on future mountain hydrology. This paper describes the improvements brought to StreamFlow, an existing model for hydrological and stream temperature prediction built as an external extension to the physically-based snow model Alpine3D. StreamFlow's source code has been entirely written anew, taking advantage of object-oriented programming to significantly improve its structure and ease the implementation of future developments. The source code is now publicly available online, along with a complete documentation. A special emphasis has been put on modularity during the re-implementation of StreamFlow, so that many model aspects can be represented using different alternatives. For example, several options are now available to model the advection of water within the stream. This allows for an easy and fast comparison between different approaches and helps in defining more reliable uncertainty estimates of the model forecasts. In particular, a case study in a Swiss Alpine catchment reveals that the stream temperature predictions are particularly sensitive to the approach used to model the temperature of subsurface runoff, a fact which has been poorly reported in the literature to date. Based on the case study, StreamFlow is shown to reproduce hourly mean discharge with a Nash–Sutcliffe efficiency (NSE) of 0.82, and hourly mean temperature with a NSE of 0.78.