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We performed a systematic analysis of blood DNA methylation profiles from 4483 participants from seven independent cohorts identifying differentially methylated positions (DMPs) associated with psychosis, schizophrenia, and treatment-resistant schizophrenia. Psychosis cases were characterized by significant differences in measures of blood cell proportions and elevated smoking exposure derived from the DNA methylation data, with the largest differences seen in treatment-resistant schizophrenia patients. We implemented a stringent pipeline to meta-analyze epigenome-wide association study (EWAS) results across datasets, identifying 95 DMPs associated with psychosis and 1048 DMPs associated with schizophrenia, with evidence of colocalization to regions nominated by genetic association studies of disease. Many schizophrenia-associated DNA methylation differences were only present in patients with treatment-resistant schizophrenia, potentially reflecting exposure to the atypical antipsychotic clozapine. Our results highlight how DNA methylation data can be leveraged to identify physiological (e.g., differential cell counts) and environmental (e.g., smoking) factors associated with psychosis and molecular biomarkers of treatment-resistant schizophrenia.

Global climate model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue, many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However, most state-of-the-art hydrological models require more forcing variables, in addition to precipitation and temperature, such as radiation, humidity, air pressure, and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the JULES land surface model set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four effect categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global-scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial-scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

Abstract. Global Climate Model (GCM) outputs feature systematic biases that render them unsuitable for direct use by impact models, especially for hydrological studies. To deal with this issue many bias correction techniques have been developed to adjust the modelled variables against observations, focusing mainly on precipitation and temperature. However most state-of-art hydrological models require more forcing variables, additionally to precipitation and temperature, such as radiation, humidity, air pressure and wind speed. The biases in these additional variables can hinder hydrological simulations, but the effect of the bias of each variable is unexplored. Here we examine the effect of GCM biases on historical runoff simulations for each forcing variable individually, using the land surface model JULES set up at the global scale. Based on the quantified effect, we assess which variables should be included in bias correction procedures. To this end, a partial correction bias assessment experiment is conducted, to test the effect of the biases of six climate variables from a set of three GCMs. The effect of the bias of each climate variable individually is quantified by comparing the changes in simulated runoff that correspond to the bias of each tested variable. A methodology for the classification of the effect of biases in four Effect Categories (ECs), based on the magnitude and sensitivity of runoff changes, is developed and applied. Our results show that, while globally the largest changes in modelled runoff are caused by precipitation and temperature biases, there are regions where runoff is substantially affected by and/or more sensitive to radiation and humidity. Global maps of bias ECs reveal the regions mostly affected by the bias of each variable. Based on our findings, for global scale applications, bias correction of radiation and humidity, in addition to that of precipitation and temperature, is advised. Finer spatial scale information is also provided, to suggest bias correction of variables beyond precipitation and temperature for regional studies.

There are well-known difficulties to run numerical weather prediction (NWP) and climate models at resolutions traditionally referred to as 'grey-zone' (similar to 3-8 km) where deep convection is neither completely resolved by the model dynamics nor completely subgrid. In this study, we describe the performance of an operational NWP model, HARMONIE, in a climate setting (HCLIM), run at two different resolutions (6 and 15 km) for a 10-yr period (1998-2007). This model has a convection scheme particularly designed to operate in the 'grey-zone' regime, which increases the realism and accuracy of the time and spatial evolution of convective processes compared to more traditional parametrisations. HCLIM is evaluated against standard observational data sets over Europe as well as high-resolution, regional, observations. Not only is the regional climate very well represented but also higher order climate statistics and smaller scale spatial characteristics of precipitation are in good agreement with observations. The added value when making climate simulations at similar to 5 km resolution compared to more typical regional climate model resolutions is mainly seen for the very rare, high-intensity precipitation events. HCLIM at 6 km resolution reproduces the frequency and intensity of these events better than at 15 km resolution and is in closer agreement with the high-resolution observations.

Solar power is the third major renewable energy, constituting an increasingly important component of global future—low carbon—energy portfolio. Accurate climate information is essential for the conditions of solar energy production, maximization, and stable regulation and planning. Climate change impacts on energy output projections are thus of crucial importance. In this study the effect of projected changes in irradiance and temperature on the performance of photovoltaic systems in Greece is examined. Climate projections were obtained from 5 regional climate models (RCMs) under the A1B emissions scenario, for two future periods. The RCM data present systematic errors against observed values, resulting in the need of bias adjustment. The projected change in photovoltaic energy output was then estimated, considering changes in temperature and insolation. The spatiotemporal analysis indicates significant increase in mean annual temperature (up to 3.5°C) and mean total radiation (up to 5 W/m2) by 2100. The performance of photovoltaic systems exhibits a negative linear dependence on the projected temperature increase which is outweighed by the expected increase of total radiation resulting in an up to 4% increase in energy output.

AbstractAn improved bias correction method for daily general circulation model (GCM) precipitation is presented. The method belongs to the widely used family of quantile mapping correction methods. The method uses different instances of gamma function that are fitted on multiple discrete segments on the precipitation cumulative density function (CDF), instead of the common quantile‐quantile approach that uses one theoretical distribution to fit the entire CDF. This imposes to the method the ability to better transfer the observed precipitation statistics to the raw GCM data. The selection of the segment number is performed by an information criterion to poise between complexity and efficiency of the transfer function. The global precipitation output of Institut Pierre Simon Laplace Coupled Model for the period 1960–2000 is bias corrected using the precipitation observations of WATCH Forcing Data. The 1960–1980 period of observations was used to calibrate the bias correction method, while 1981–2000 was used for validation. The proposed method performs well on the validation period, according to two performance estimators.