Search

Statistical downscaling (SD) and bias adjustment (BA) methods are routinely used to produce regional to local climate change projections from coarse global model outputs. The suitability of these techniques depends on the particular application of interest and, especially, on the required spatial resolution. Whereas SD is appropriate for local (e.g., gauge) resolution, BA may be a good alternative when the gap between the predictor and predictand resolution is small. However, the different sources of uncertainty affecting SD such as reanalysis uncertainty, the choice of suitable predictors, climate model, and/or statistical approach may yield implausible projections in particular situations for which BA techniques may offer a compromise alternative, even for local resolution. In this work, we consider a case study with 41 rain gauges over Malawi and show that, despite producing similar results for a historical period, the use of different predictors may lead to large differences in the future projections obtained from SD methods. For instance, using temperature T (specific humidity Q) produces much drier (wetter) conditions than those projected by the raw global models for the target area. We demonstrate that this can be partially alleviated by substituting T+Q by relative humidity R, which simultaneously accounts for both water availability and temperature, and yields regional projections more compatible with the global one. Nevertheless, large local differences still persist, lacking a physical interpretation. In these situations, the use of simpler approaches such as empirical BA may lead to more plausible (i.e., more consistent with the global model) projections. [ABSTRACT FROM AUTHOR]

The Coordinated Regional Climate Downscaling Experiment (CORDEX) initiative has made available an enormous amount of regional climate projections in different domains worldwide. This information is crucial for the development of adaptation strategies and policy‐making. A relevant open issue in this context is assessing the potential multidomain conflicts that may result in overlapping regions and developing appropriate ensemble methods trying to make the most of all available information. This work addresses this timely topic by focusing on precipitation over the Mediterranean region, a first illustrative case study that is encompassed by both the Euro‐ and Africa‐CORDEX domains. We focus on several mean, extreme, and temporal indices and use variance decomposition to assess the separate contribution of the domain and models to the climate change signal, concluding that the contribution of the domain alone is nearly negligible (below 5% in all cases). Nevertheless, for some cases, the combined model/domain effect triggers up to 40% of the total variance. Plain Language Summary: The Coordinated Regional Climate Downscaling Experiment (CORDEX) provides spatially detailed climate change projections for different regions across the world. These projections are obtained through numerical models that solve the governing equations of the atmosphere over spatial domains, which typically cover continental areas and encompass several regions. The regional climate change information generated by these models presents various sources of uncertainties. This work addresses the uncertainty related to the choice of domain, which has not been properly assessed to date, despite it can potentially affect vast regions of the world for which model simulations coming from different CORDEX domains are available. We focus on precipitation over the Mediterranean region, which is encompassed by both the EURO‐ and AFR‐CORDEX domains, and quantify the separate contribution of the model and domain alone to the total uncertainty for the climate change signals. Our results indicate that this uncertainty comes mostly determined by the choice of model, with little variability coming from the domain. This would allow for combining different model simulations corresponding to overlapping domains since conflicting signals are very unlikely to occur. These findings may ease the decision‐making process in regions for which multimodel and multidomain heterogeneous climate change information is available. Key Points: Overlaps in multidomain CORDEX regional climate change projections (e.g., in the Mediterranean) may result in conflicting messagesA variance analysis shows that domain contribution to the grand ensemble is generally small for several mean, extreme, and temporal indicesWe conclude that combining the available multidomain CORDEX simulations for a given region is an appropriate methodology [ABSTRACT FROM AUTHOR]

This work presents a comprehensive intercomparison of different alternatives for the calibration of seasonal forecasts, ranging from simple bias adjustment (BA)—e.g. quantile mapping—to more sophisticated ensemble recalibration (RC) methods—e.g. non-homogeneous Gaussian regression, which build on the temporal correspondence between the climate model and the corresponding observations to generate reliable predictions. To be as critical as possible, we validate the raw model and the calibrated forecasts in terms of a number of metrics which take into account different aspects of forecast quality (association, accuracy, discrimination and reliability). We focus on one-month lead forecasts of precipitation and temperature from four state-of-the-art seasonal forecasting systems, three of them included in the Copernicus Climate Change Service dataset (ECMWF-SEAS5, UK Met Office-GloSea5 and Météo France-System5) for boreal winter and summer over two illustrative regions with different skill characteristics (Europe and Southeast Asia). Our results indicate that both BA and RC methods effectively correct the large raw model biases, which is of paramount importance for users, particularly when directly using the climate model outputs to run impact models, or when computing climate indices depending on absolute values/thresholds. However, except for particular regions and/or seasons (typically with high skill), there is only marginal added value—with respect to the raw model outputs—beyond this bias removal. For those cases, RC methods can outperform BA ones, mostly due to an improvement in reliability. Finally, we also show that whereas an increase in the number of members only modestly affects the results obtained from calibration, longer hindcast periods lead to improved forecast quality, particularly for RC methods. [ABSTRACT FROM AUTHOR]

Statistical downscaling methods (SDMs) are techniques used to downscale and/or bias‐correct climate model results to regional or local scales. The European network VALUE developed a framework to evaluate and inter‐compare SDMs. One of VALUE's experiments is the perfect predictor experiment that uses reanalysis predictors to isolate downscaling skill. Most evaluation papers for SDMs employ simple statistical diagnostics and do not follow a process‐based rationale. Thus, in this paper, a process‐based evaluation has been conducted for the more than 40 participating model output statistics (MOS, mostly bias correction) and perfect prognosis (PP) methods, for temperature and precipitation at 86 weather stations across Europe. The SDMs are analysed following the so‐called "regime‐oriented" technique, focussing on relevant features of the atmospheric circulation at large to local scales. These features comprise the North Atlantic Oscillation, blocking and selected Lamb weather types and at local scales the bora wind and the western Iberian coastal‐low level jet. The representation of the local weather response to the selected features depends strongly on the method class. As expected, MOS is unable to generate process sensitivity when it is not simulated by the predictors (ERA‐Interim). Moreover, MOS often suffers from an inflation effect when a predictor is used for more than one station. The PP performance is very diverse and depends strongly on the implementation. Although conditioned on predictors that typically describe the large‐scale circulation, PP often fails in capturing the process sensitivity correctly. Stochastic generalized linear models supported by well‐chosen predictors show improved skill to represent the sensitivities. [ABSTRACT FROM AUTHOR]

Highlights • Correlations between structural connectivity and landscape diversity and fauna are studied. • Zones with greater species richness and landscape diversity largely coincide. • Species richness and landscape diversity are correlated with ecological corridors. • There is a loss of landscape diversity between 2006 and 2012 in Castilla y León region. Abstract Connectivity loss has been identified as one of the greatest threats to biodiversity, at both the species and ecosystem levels. This study aims to find possible correlations between structural connectivity and faunal richness and landscape diversity in Spain's largest region, Castilla y León. Based on data provided by the National Biodiversity Inventory and the CORINE Land Cover land-use mapping for 2000 and 2006, species richness was characterized by the number of species occurring in a grid overlaid on the 10 × 10-km-territory. The Shannon Index for land uses was also calculated in each one of the grid cells, providing information on landscape diversity. Structural connectivity was studied using the Morphological Spatial Pattern Analysis, thus providing information on landscape diversity for different edge widths in two different habitat types. Lastly, the analyses showed that there is a slight relationship between structural connectivity and landscape diversity, but not between structural connectivity and faunal richness. [ABSTRACT FROM AUTHOR]

We present an unprecedented ensemble of 196 future climate projections arising from different global and regional model intercomparison projects (MIPs): CMIP3, CMIP5, ENSEMBLES, ESCENA, EURO- and Med-CORDEX. This multi-MIP ensemble includes all regional climate model (RCM) projections publicly available to date, along with their driving global climate models (GCMs). We illustrate consistent and conflicting messages using continental Spain and the Balearic Islands as target region. The study considers near future (2021-2050) changes and their dependence on several uncertainty sources sampled in the multi-MIP ensemble: GCM, future scenario, internal variability, RCM, and spatial resolution. This initial work focuses on mean seasonal precipitation and temperature changes. The results show that the potential GCM-RCM combinations have been explored very unevenly, with favoured GCMs and large ensembles of a few RCMs that do not respond to any ensemble design. Therefore, the grand-ensemble is weighted towards a few models. The selection of a balanced, credible sub-ensemble is challenged in this study by illustrating several conflicting responses between the RCM and its driving GCM and among different RCMs. Sub-ensembles from different initiatives are dominated by different uncertainty sources, being the driving GCM the main contributor to uncertainty in the grand-ensemble. For this analysis of the near future changes, the emission scenario does not lead to a strong uncertainty. Despite the extra computational effort, for mean seasonal changes, the increase in resolution does not lead to important changes. [ABSTRACT FROM AUTHOR]

Species Distribution Models (SDMs) constitute an important tool to assist decision-making in environmental conservation and planning in the context of climate change. Nevertheless, SDM projections are affected by a wide range of uncertainty factors (related to training data, climate projections and SDM techniques), which limit their potential value and credibility. The new package mopa provides tools for designing comprehensive multi-factor SDM ensemble experiments, combining multiple sources of uncertainty (e.g. baseline climate, pseudo-absence realizations, SDM techniques, future projections) and allowing to assess their contribution to the overall spread of the ensemble projection. In addition, mopa is seamlessly integrated with the climate4R bundle and allows straightforward retrieval and post-processing of state-of-the-art climate datasets (including observations and climate change projections), thus facilitating the proper analysis of key uncertainty factors related to climate data. [ABSTRACT FROM AUTHOR]

Extreme weather events are projected to be more frequent and severe across the globe because of global warming. This poses challenging problems for critical infrastructures, which could be dramatically affected (or disrupted), and may require adaptation plans to the changing climate conditions. The INTACT FP7-European project evaluated the resilience and vulnerability of critical infrastructures to extreme weather events in a climate change scenario. To identify changes in the hazard induced by climate change, appropriate extreme weather indicators (EWIs), as proxies of the main atmospheric features triggering events with high impact on the infrastructures, were defined for a number of case studies and different approaches were analyzed to obtain local climate projections. We considered the influence of weighting and bias correction schemes on the delta approach followed to obtain the resulting projections, considering data from the Euro-CORDEX ensemble of regional future climate scenarios over Europe. The aim is to provide practitioners, decision-makers, and administrators with appropriate methods to obtain actionable and plausible results on local/regional future climate scenarios. Our results show a small sensitivity to the weighting approach and a large sensitivity to bias correcting the future projections. [ABSTRACT FROM AUTHOR]

The use and development of bias correction (BC) methods has grown fast in recent years, due to the increased demand of unbiased projections by many sectoral climate change impact applications. Case studies are frequently based on multi-variate climate indices (CIs) combining two or more essential climate variables that are frequently individually corrected prior to CI calculation. This poses the question of whether the BC method modifies the inter-variable dependencies and eventually the climate change signal. The direct bias correction of the multi-variate CI stands as a usual alternative, since it preserves the physical and temporal coherence among the primary variables as represented in the dynamical model output, at the expense of incorporating the individual biases on the CI with an effect difficult to foresee, particularly in the case of complex CIs bearing in their formulation non-linear relationships between components. Such is the case of the Fire Weather Index (FWI), a meteorological fire danger indicator frequently used in forest fire prevention and research. In the present work, we test the suitability of the direct BC approach on FWI as a representative multi-variate CI, assessing its performance in present climate conditions and its effect on the climate change signal when applied to future projections. Moreover, the results are compared with the common approach of correcting the input variables separately. To this aim, we apply the widely used empirical quantile mapping method (QM), adjusting the 99 empirical percentiles. The analysis of the percentile adjustment function (PAF) provides insight into the effect of the QM on the climate change signal. Although both approaches present similar results in the present climate, the direct correction introduces a greater modification of the original change signal. These results warn against the blind use of QM, even in the case of essential climate variables or uni-variate CIs. [ABSTRACT FROM AUTHOR]

Background: In Colombia, human leptospirosis (HL) is a disease that has had a mandatory notification rule since 2007. Humans usually acquire the infection through water contaminated with animal urine that comes into direct contact with cutaneous lesions, eyes or mucous membranes. Objectives: To analyze the spatiotemporal variability in the occurrence of HL cases in Colombia between 2007 and 2016, and its relation with the El Niño Southern Oscillation (ENSO) cycle and the consequent anomalies in rainfall in spatiotemporal clusters. Methods: An ecological study of the HL cases, aggregated by municipality, and reported between 2007 and 2016, is presented. Findings: During the period of study, 9928 cases of HL were reported, and 58.9% of the municipalities reported at least one case of leptospirosis. Six spatiotemporal clusters were identified-five were in the Andean region and one was in the Caribbean region. The assessment of the ENSO cycle and rainfall anomalies suggests the importance of La Niña episodes, and excess rainfall periods in the occurrence of cases of HL. Conclusions: Our results demonstrate the importance of the ENSO cycle, rainfall periods and periods with excess rainfall in the occurrence of cases and outbreaks of HL in Colombia, and suggest the importance of the topography of valleys and flood zones as zones in which the risk of infection is elevated. [ABSTRACT FROM AUTHOR]

In a previous study Pons et al. (Clim Res 54(3):197-207, . doi:) reported a significant decreasing trend of snowfall occurrence in the Northern Iberian Peninsula since the mid 70s. The study was based on observations of annual snowfall frequency (measured as the annual number of snowfall days NSD) from a network of 33 stations ranging from 60 to 1350 m. In the present work we analyze the skill of Regional Climate Models (RCMs) to reproduce this trend for the period 1961-2000 (using both reanalysis- and historical GCM-driven boundary conditions) and the trend and the associated uncertainty of the regional future projections obtained under the A1B scenario for the first half of the twenty-first century. In particular, we consider the regional simulation dataset from the EU-funded ENSEMBLES project, consisting of thirteen state-of-the-art RCMs run at 25 km resolution over Europe. While ERA40 severely underestimates both the mean NSD and its observed trend (−2.2 days/decade), the corresponding RCM simulations driven by the reanalysis appropriately capture the interannual variability and trends of the observed NSD (trends ranging from −3.4 to −0.7, −2.1 days/decade for the ensemble mean). The results driven by the GCM historical runs are quite variable, with trends ranging from −8.5 to 0.2 days/decade (−1.5 days/decade for the ensemble mean), and the greatest uncertainty by far being associated with the particular GCM used. Finally, the trends for the future 2011-2050 A1B runs are more consistent and significant, ranging in this case from −3.7 to −0.5 days/decade (−2.0 days/decade for the ensemble mean), indicating a future significant decreasing trend. These trends are mainly determined by the increasing temperatures, as indicated by the interannual correlation between temperature and NSD (−0.63 in the observations), which is preserved in both ERA40- and GCM-driven simulations. [ABSTRACT FROM AUTHOR]

Climate Impact Indices (CIIs) are being increasingly used in different socioeconomic sectors to transfer information about climate change impacts to stakeholders. Typically, CIIs comprise into a single index several weather variables -such as temperature, wind speed, precipitation and humidity- which are relevant for a particular problem of interest. Moreover, most of the CIIs require daily (or monthly) physical coherence among these variables for their proper calculation. This constraints the number of statistical downscaling techniques suitable for a component-wise approach to this problem. We test the suitability of the alternative 'direct' downscaling approach in which the downscaling method is applied directly to the CII, thus circumventing the multi-variable problem and allowing the use of a wider range of downscaling methods. For illustrative purposes, we consider two popular CIIs -the Fire Weather Index (FWI) and the Physiological Equivalent Temperature (PET), used in the wildfire and tourism sectors, respectively- and compare the performance of the two approaches using the analog method, a simple and popular method providing inter-variable dependence. The results obtained with 'perfect' reanalysis predictors are comparable for both approaches, although smaller accuracy is obtained in general with the direct approach. Moreover, similar climate change 'deltas' are obtained with both approaches when applied to an illustrative future global projection using the ECHAM5 model. Overall, there is a trade-off between performance and simplicity which needs to be balanced for each particular application. [ABSTRACT FROM AUTHOR]

We present future fire danger scenarios for the countries bordering the Mediterranean areas of Europe and north Africa building on a multi-model ensemble of state-of-the-art regional climate projections from the EU-funded project ENSEMBLES. Fire danger is estimated using the Canadian Forest Fire Weather Index (FWI) System and a related set of indices. To overcome some of the limitations of ENSEMBLES data for their application on the FWI System-recently highlighted in a previous study by Herrera et al. (Clim Chang 118:827-840, 2013)-we used an optimal proxy variable combination. A robust assessment of future fire danger projections is undertaken by disentangling the climate change signal from the uncertainty derived from the multi-model ensemble, unveiling a positive signal of fire danger potential over large areas of the Mediterranean. The increase in the fire danger signal is accentuated towards the latest part of the transient period, thus pointing to an elevated fire potential in the region with time. The fire-climate links under present and future conditions are further discussed building upon observed climate data and burned area records along a representative climatic gradient within the study region. [ABSTRACT FROM AUTHOR]

The effect of climate change on wildfires constitutes a serious concern in fire-prone regions with complex fire behavior such as the Mediterranean. The coarse resolution of future climate projections produced by General Circulation Models (GCMs) prevents their direct use in local climate change studies. Statistical downscaling techniques bridge this gap using empirical models that link the synoptic-scale variables from GCMs to the local variables of interest (using e.g. data from meteorological stations). In this paper, we investigate the application of statistical downscaling methods in the context of wildfire research, focusing in the Canadian Fire Weather Index (FWI), one of the most popular fire danger indices. We target on the Iberian Peninsula and Greece and use historical observations of the FWI meteorological drivers (temperature, humidity, wind and precipitation) in several local stations. In particular, we analyze the performance of the analog method, which is a convenient first choice for this problem since it guarantees physical and spatial consistency of the downscaled variables, regardless of their different statistical properties. First we validate the method in perfect model conditions using ERA-Interim reanalysis data. Overall, not all variables are downscaled with the same accuracy, with the poorest results (with spatially averaged daily correlations below 0.5) obtained for wind, followed by precipitation. Consequently, those FWI components mostly relying on those parameters exhibit the poorest results. However, those deficiencies are compensated in the resulting FWI values due to the overall high performance of temperature and relative humidity. Then, we check the suitability of the method to downscale control projections (20C3M scenario) from a single GCM (the ECHAM5 model) and compute the downscaled future fire danger projections for the transient A1B scenario. In order to detect problems due to non-stationarities related to climate change, we compare the results with those obtained with a Regional Climate Model (RCM) driven by the same GCM. Although both statistical and dynamical projections exhibit a similar pattern of risk increment in the first half of the 21st century, they diverge during the second half of the century. As a conclusion, we advocate caution in the use of projections for this last period, regardless of the regionalization technique applied. [ABSTRACT FROM AUTHOR]

The present study assesses the ability of seven Earth System Models (ESMs) from the Coupled Model Intercomparison Project Phase 5 to reproduce present climate conditions in Europe and Africa. This is done from a downscaling perspective, taking into account the requirements of both statistical and dynamical approaches. ECMWF's ERA-Interim reanalysis is used as reference for an evaluation of circulation, temperature and humidity variables on daily timescale, which is based on distributional similarity scores. To additionally obtain an estimate of reanalysis uncertainty, ERA-Interim's deviation from the Japanese Meteorological Agency JRA-25 reanalysis is calculated. Areas with considerable differences between both reanalyses do not allow for a proper assessment, since ESM performance is sensitive to the choice of reanalysis. For use in statistical downscaling studies, ESM performance is computed on the grid-box scale and mapped over a large spatial domain covering Europe and Africa, additionally highlighting those regions where significant distributional differences remain even for the centered/zero-mean time series. For use in dynamical downscaling studies, performance is specifically assessed along the lateral boundaries of the three CORDEX domains defined for Europe, the Mediterranean Basin and Africa. [ABSTRACT FROM AUTHOR]

The effect of four different gas mixtures on the evolution of the fatty acid composition of neutral lipids and polar lipids, and oxidative stability of Merino fresh lamb meat was studied. Merino fresh lamb meat was packed under four different atmospheres (Atmosphere 1: Air; Atmosphere 2: 70% O2 + 30% CO2; Atmosphere 3: 80% O2 + 20% CO2; and Atmosphere 4: 30% CO2 + 69.6% Ar + 0.4% CO) and stored under refrigeration (3±1 °C) for 12 days. Time of storage only affected the proportions of saturated fatty acids of neutral lipids (P<0.05). There were no significant differences among gas mixtures for the fatty acid profile of neutral and polar lipids during storage (P>0.05). Malondialdehyde and hexanal concentrations were higher for the atmospheres with the highest proportion of oxygen (Atmospheres 2 and 3) indicating lower oxidative stability. The atmosphere consisting of 30% CO2 + 69.6% Ar + 0.4% CO is recommended, due to a higher oxidative stability of meat during refrigerated storage. [ABSTRACT FROM AUTHOR]

The article presents an analysis and simulation with statistical downscaling methods of snow trends in Northern Spain. It examines various topics such as the annual snow frequency measure as the annual number of snow days and precipitation occurrence and the simulation of the observed trends utilizing the connection of daily snow occurrence (DSO). Result shows that the downscaling method with typical values of hit and false alarm rates has predicted that the DSO is around 60% and 2%.

In the winter-rain southern Atacama Desert of the Coquimbo Region of Chile, El Niño -- Southern Oscillation (ENSO) events modulate primary productivity. In this region, there are important changes in water availability between La Niña (dry) and El Niño (rainy) years. Using inter-annual comparisons of LANDSAT images from 30° to 31° S latitude, we observed changes in primary productivity between dry and rainy years at the regional level. There were also significant, negative correlations between productivity and elevation, with changes occurring first at low elevation during rainy years. The limiting factors to dryland vegetation primary productivity is different in regard to elevation. Rain during an El Niño year is the main factor that explains the increase in primary productivity at low elevation, while lower temperatures reduce and delay the net primary productivity at mid elevation. [ABSTRACT FROM AUTHOR]

A correction to the article "Forest fire danger projections in the Mediterranean using ENSEMBLES regional climate change scenarios" that was published online in February 21, 2014 issue of the periodical is presented.