Search

As a dynamical system approaches its critical threshold, the probability density distribution of the system will change significantly. Therefore, it is possible to present an early warning signal based on the changing skewness before reaching the critical threshold. Based on a zero‐dimensional climate model and several typical fold models, this paper systematically studies the influence of noise and missing data on the performance of the skewness coefficient as an early warning signal of an abrupt climate change. The results in three types of fold models show that the skewness coefficient has anti‐noise ability to some extent, but strong noise will significantly reduce the magnitude of the skewness coefficient and the time for early warning will also be shortened. In some cases, strong noise even will lead to the result that the skewness does not work in warning an impending abrupt change. However, the influence of strong noise on skewness is insignificant in the zero‐dimensional climate model. Therefore, the influence of strong noise needs to be considered in the practical application of the skewness coefficient as an early warning signal of an abrupt change. In addition, the results of all the models also indicate that different degrees of the missing data have no statistically significant effect on the warning performance of the skewness coefficient, even when the length of the missing data is up to 20% of the total sample size used in the present paper. [ABSTRACT FROM AUTHOR]

Modelling rainfall extremes and dry periods over the Southeast Asia (SEA) region is challenging due to the characteristics of the region, which consists of the Maritime Continent and a mountainous region; it also experiences monsoonal conditions, as it is located between the Asian summer monsoon and the Australian summer monsoon. Representing rainfall extremes is important for flood and drought assessments in the region. This paper evaluates extreme rainfall climatic indices from regional climate models from CORDEX Southeast Asia and compares them with the results of high‐resolution global climate models with a comparable spatial resolution from the HighResMIP experiment. Observations indicate a high intensity of rainfall over areas affected by tropical cyclones and long consecutive dry day periods over some areas in Indochina and the southern end of Indonesia. In the model simulations, we find that both coupled and sea surface temperature‐forced HighResMIP model experiments are more similar to the observations than CORDEX model results. However, the models produce a poorer simulation of precipitation intensity‐related indices due to model biases in the rainfall intensity. This bias is higher in CORDEX than in HighResMIP and is evident in both the low‐ and high‐resolution HighResMIP model versions. The comparable performances of HighResSST (atmosphere‐only runs) and Hist‐1950 (coupled ocean–atmosphere runs) demonstrate the accuracy of the ocean model. Comparable performances were also found for the two different resolutions of HighResMIP, suggesting that there is no improvement in the performance of the high‐resolution HighResMIP model compared to the low‐resolution HighResMIP model. [ABSTRACT FROM AUTHOR]

Sea ice is an important and complex component of the Earth system and is considered a sensitive indicator of climate change. The seasonal sea ice cycle regulates the exchange of heat and salinity, altering the energy balance between high and low latitudes as well as the ocean and atmospheric circulation. The accurate representation of Antarctic sea ice has been considered a hot topic in the climate modelling community and lacks conclusive answers. In this paper, we evaluated the ability of 11 climate models from Coupled Model Intercomparison Project Phase 5 (CMIP5) and Phase 6 (CMIP6) to simulate the sea ice seasonal cycle in Antarctica in terms of area (SIA) and concentration (SIC), as well as the improvements in the most recent models' version, submitted to CMIP6. The results indicated that all models are able to accurately capture the seasonal cycle of the Antarctic SIA, with the minimum (maximum) occurring in February (September). In the Weddell Sea, Amundsen Sea, Bellingshausen Sea, and the Ross Sea, the simulated sea ice concentration revealed a large and systematic bias in February when compared to observations. In September, a large and systematic bias was found nearby the Southern Ocean's northern limit in the Polar Front. Several CMIP6 models exhibited slight improvements on the SIA and SIC estimate over the previous version (CMIP5). All models indicated a significant sea ice loss in the coming years as a response to CO2 forcing. Despite the advancements in the sea ice representation, our findings show that the models are still unable to accurately represent the regional sea ice changes [ABSTRACT FROM AUTHOR]

Climate models' capability of reproducing the present climate at both global and regional scales still needs improvements. The assessment of model performance critically depends on the data sets used as comparators/references. Reanalysis and gridded observational data sets have been frequently used for this purpose. However, none of these can be considered an accurate reference data set because of their associated uncertainties and full representativity. This paper, for the first time, uses in‐situ measurements from National Oceanic and Atmospheric Administration U.S. Climate Reference Network (USCRN) spanning the period 2006–2020 to assess daily temperature and precipitation from a suite of dynamically downscaled regional climate models (RCMs; driven by ERA‐Interim) involved in NA‐CORDEX. The assessment is also extended to the most recent and widely used Earth system reanalyses (ERA5, ERA‐Interim, MERRA2 and NARR) and a few in situ‐based gridded data sets (Daymet, PRISM, Livneh and CPC). Results show that biases for the different data sets are seasonally and subregionally dependent. On average, reanalysis and in situ‐based gridded data sets are warmer (with biases exceeding 0.3°C) than USCRN year‐round, while RCMs are colder (warmer) in winter (summer) with biases ranging from −0.5 (0.9)°C for RCMs at 0.44° to −0.2 (1.4)°C for CRCM5‐UQAM‐11. In situ‐based gridded data sets provide the best performance in most of the Contiguous United States (CONUS) regions compared to reanalyses and RCMs, but still have biases in regions such as the Western mountains and the Pacific Northwest. Furthermore, in most US subregions, reanalysis data sets do not outperform reanalysis‐driven RCMs. Likewise, for both reanalysis data sets and RCMs, temperature and precipitation biases vary considerably depending on the local orography, with larger temperature biases for coarser model resolutions and precipitation biases for reanalysis. [ABSTRACT FROM AUTHOR]

Statistical downscaling of climate projections is an active field of research and numerous intercomparison exercises of different techniques exist. Most evaluation studies make use of perfect predictors from a reanalysis, but neglect the analysis of how imperfect predictors from global climate models (GCMs) affect statistical downscaling. In this paper we evaluate and intercompare five statistical downscaling methods: (a) Analog, (b) Regression, (c) Artificial Neural Networks, (d) Support Vector Machines and (e) Kernel Ridge Regression, over a large ensemble of 18 GCMs participants in the CMIP5 project. These methods have been used to downscale maximum/minimum daily temperatures and daily accumulated precipitation on a high‐resolution observational grid (0.05°) over mainland Spain and the Balearic Islands. Our analysis focuses on the marginal aspects (mean values and tails) of the distributions by season. Results when driven by reanalysis and by GCMs have been compared in order to analyse the sensitivity of statistical downscaling methods to the use of imperfect predictors. Both for temperature and for precipitation all methods achieve, as expected, better results when applied over reanalysis, although no method displays a clearly higher sensitivity to the use of imperfect predictors from GCMs. We have found the existence of outlier GCMs, showing significantly higher errors than the rest of the ensemble. And we have quantified errors over GCMs, so this experiment provides a more precise idea of the reliability and accuracy of the overall performance (i.e., simulations by GCMs plus statistical downscaling) than the perfect predictor experiment and reveals an important source of uncertainty coming from GCMs even in a historical period. [ABSTRACT FROM AUTHOR]

Climate classifications are useful to synthesize the physical state of the climate with a proxy that can be directly related to biota. However, they present a potential drawback, namely a strong sensitivity because of the use of hard thresholds (step functions). Thus, minor discrepancies in the base data produce large differences in the type of climate. However, such an a priori limitation is also a strength because such sensitivity can be used to better gauge model performance. Although previous attempts of classifying climates of the world using global climate model outputs were encouraging, the applicability of their classifications to impact studies has been limited by past model issues. Notwithstanding the persistence of certain imperfections and limitations in current models, the high‐quality physical simulations from phase six of the Coupled Intercomparison Project (CMIP6) has opened new possibilities in the field, thanks to their improved representation of atmospheric and oceanic physics. The purpose of this paper is twofold: to show that climate classifications from CMIP6 are sufficiently robust for use in impact studies, and to use those classifications for identifying error sources and potential issues that deserve further attention in models. Thus, 52 CMIP6 climate models were evaluated by using three climate classifications schemes, classical Köppen, extended‐Köppen, and modified Thornthwaite. We first assessed model ability to reproduce present climate types by comparing their outputs with observational data. Models performed best for the Köppen and extended‐Köppen classification methods (Cohen's kappa κ = 0.7), and had moderate scores for the Thornthwaite climate classification (κ = 0.4). By tracing back the observed biases, we were able to pinpoint the misrepresentation of dry climates as a major source of error. Another finding was that most models still had some difficulties in representing the seasonal variability of precipitation over several monsoonal regions, thereby yielding the wrong climate type there. Models were also evaluated for future climate. Substantial changes in climate types are projected in the SSP5‐8.5 scenario. These changes include a shrinkage of polar/frigid climates (22%) and an increase of dry climates (7%). [ABSTRACT FROM AUTHOR]

This study investigates some of the uncertainties sources associated with the pseudo global warming (PGW) approach which was employed to project future patterns of tropical cyclones (TCs) over the Arabian Sea (AS). First, the climate variables controlling the patterns of tropical cyclones were extracted from reanalysis datasets of ERA5, ERAI, CFSR, and NCEP/NCAR. Then, each dataset was evaluated against long‐term measurements to identify the best‐performing reanalysis dataset. ERA5 showed the best performance for most of the variables. Outputs of 20 CMIP5 global climate models (GCMs) were then evaluated against the ERA5 data resulting in an ensemble of the best performing GCMs. A PGW framework was then used to project the changes in patterns of three significant historical cyclones: Gonu, Phet, and Ashobaa. In doing so, the signals of future climate variables were extracted from the GCMs ensemble to modify the initial and boundary conditions of the WRF model which was previously tuned for reproducing the historical TCs. Different tests were conducted to address the sources of uncertainty in the PGW approach, including the selection of the climate variables contributing to the computation of the signals, the selection of GCMs, and the spatial variation of signals. A considerable sensitivity of the projected track and intensity of TCs to the choice of GCMs was observed, acknowledging the importance of GCMs evaluation before calculating the signals. Moreover, it was found that among all variables, signals of sea surface temperature and air temperature have major effects on the cyclone's track and intensity. Apart from that, when the signals were applied to the domain of the WRF model uniformly, compared to applying spatially varying signals, different tracks and intensities for future TCs were also observed. Overall, the findings of this paper challenge the reliability of the projected changes in TCs patterns obtained from PGW. [ABSTRACT FROM AUTHOR]

There are several methods and software for the homogenization of climate data series but there is not any exact mathematical theory of homogenization. As we see, the basic problem of homogenization is the unreasonable dominance of the practical procedures over the theory. Therefore we try to formulate some questions of homogenization in accordance with the mathematical conventions. The topics to be discussed are as follows. (a) Themes and systems—meteorological, mathematical and software links. (b) Mathematical formulation of climate data series homogenization and special case of normal distribution. (c) Additive and multiplicative spatiotemporal models for climate data series homogenization. (d) Mathematical overview of methodological questions as, spatial comparison of series, inhomogeneity detection and adjustment of series, in accordance with the WMO Guidelines on Homogenisation (2020). (e) The last topic is the new version of MASH for homogenization of mean and standard deviation. The earlier versions of our method MASH (Multiple Analysis of Series for Homogenization; Szentimrey) were developed formerly at the Hungarian Meteorological Service. These procedures aimed to homogenize the daily and monthly data series in the mean, that is, the first order moment. The new version MASHv4.01 has been developed for joint homogenization of mean and standard deviation using some mathematical results. Theoretically, in case of normal distribution, the homogenization of mean and standard deviation is sufficient since if the first two moments are homogenous then the higher order moments are also homogeneous. An interactive automatic algorithm also was developed in this new version in order to make the homogenization easier for the users. We will present a summary of the software MASH where our intention was to develop a flexible, interactive automatic, artificial intelligence (AI) system. We finish the paper with some comments connected to the theoretical evaluation and benchmark validation of the methods. [ABSTRACT FROM AUTHOR]

Sea surface wind (10‐m high) is one of the most important variables for oceanic applications. The zonal and meridional surface wind components from seven global atmospheric reanalyses [National Centers for Environmental Prediction (NCEP)/National Center for Atmospheric Research Reanalysis 1, NCEP/Department of Energy Reanalysis 2, NCEP/climate forecast system reanalysis (CFSR), European Centre for Medium‐Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA‐Interim), Japanese 55‐year Reanalysis (JRA‐55), the Modern‐Era Retrospective Analysis for Research and Application V2, and the ERA5 Reanalysis] were compared with surface winds retrieved from QuikScat/SeaWinds and Ascat scatterometers, and with surface ground winds measured in eight coastal meteorological stations, at the Southwestern South Atlantic Ocean, between 35° and 55°S and 55° and 70°W, approximately. Bias, root mean squared error and the Pearson linear correlation coefficient for the zonal and meridional wind components were estimated in this study. The effects of the atmospheric stability, the surface currents and the discontinuity between land and sea roughness on the satellite data are discussed in this paper. It was concluded that Modern‐Era Retrospective Analysis for Research and Application, Version 2 reanalysis is the product that better fitted with satellite data, and ERA5 follows it in performance. CFSR, ERA‐Interim and JRA‐55 reanalyses also presented very good performances. Surface winds from NR1 and NR2 reanalysis showed the largest differences with satellite data. ERA5 reanalysis was the database that better compared with the coastal observations measured at the meteorological stations. Surface winds measured at meteorological stations are, in general, worse represented by the reanalyses than satellite data. This last could be due to the discontinuity between land and sea roughness, and because the topography is not completely represented by the atmospheric global models. [ABSTRACT FROM AUTHOR]

The hydrological projections provided by the outputs of Global Climate Models (GCMs) combining hydrological models include multi‐source uncertainties, which may challenge the formulation of relevant adaption and mitigation policies. In this paper, the overall uncertainty and the relative contribution of each uncertainty component were investigated for hydrological projections over 408 watersheds in China by using 3 shared socioeconomic pathway emission scenarios (SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5), 21 GCMs, 8 bias correction methods, 4 hydrological models, and 2 sets of optimized hydrological model parameters. The results show that the total uncertainty (T) is mainly contributed by uncertainty related to global climate models (G), with the mean percentage ranging from 60.4 to 64.1%, followed by the interaction uncertainties among all components, with the mean percentage ranging from 22.0 to 26.4%. The uncertainty contribution of hydrological models (H) (6.1–9.4%) ranks third, followed by emission scenarios (S) (2.9–5.9%) and bias correction methods (B) (0.2–1.1%). The uncertainty contribution of the optimized hydrological model parameters (P) (0.2–0.3%) is almost negligible. In terms of spatial variability, the relative contribution of uncertainty related to global climate models (G) is the highest in the near future for northern China (67.5–70.6%) and in the far future for southern China (66.1–66.7%). However, it was found to be lower for the Tibetan Plateau and northwestern China (45.3–57.9%) in the near and far future. The relative contribution of hydrological model uncertainty is higher for southwestern and northwestern China and the Tibetan Plateau (7.2–19.5%) and lower for northern, eastern, and southern China (2.5–6.6%). This study highlights the importance of including multiple GCMs and hydrological models in hydrological impact studies to consider their overall uncertainty. The development of global climate models and hydrological models is still the best way to reduce the uncertainty of climate change impact studies. [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]

Excessive relative humidity (RH) in combination with high temperature can lead to heat stress, often measured by the Wet Bulb Globe Temperature (WBGT). The Clausius‐Clapeyron (CC) relationship implies that warming reduces RH if no extra moisture is added. Over coastal regions like eastern Asia, however, the predominant summer monsoon favours increased moisture transport from surrounding oceans as a result of enhanced evaporation driven by surface temperature increase. This would lessen the RH reduction by potentially two‐thirds. Based on two ensembles of climate model simulations, this paper examines the competing effects of near‐surface temperature, humidity and circulation patterns in this region and the consequential health risks. Under a high emissions scenario (RCP8.5/SSP5‐8.5), surface temperature could increase by 4 – 7°C with WBGT increases of several degrees by the end of the 21st century. Devastating extreme heat health events could therefore become a frequent occurrence as a result. Overall, our results show how humidity can be just as important as temperature when considering the risks to society of excessive heat. [ABSTRACT FROM AUTHOR]

This paper describes annual and seasonal changes in the number of days with 0°C crossings, comparing projections (2071–2100) based upon regional climate model (RCM) results under the medium and high emission scenarios with the period 1971–2000. A trend analysis of historical observations of days with zero‐crossings (DZCs) for the period 1971–2016 showed a general increase in DZCs in cold seasons and regions and a decrease in DZCs in mild seasons and regions. A cold bias in the unadjusted EURO‐CORDEX model ensemble rendered bias‐adjustment necessary before analyses. In regions and seasons that are mild, temperatures will exceed 0°C during a larger part of the year by the end of this century, and the number of DZCs is thus projected to decrease. This decrease was found for lowland regions in spring and coastal regions in winter. In regions and seasons that are cold, temperatures will rise up above 0°C for a larger part of the year, such that the number of DZCs is projected to give more frequent crossings of the 0°C threshold. This increase was found for inland regions in winter and Finnmark, the northernmost county, in spring. Thus, more frequent icing of the snowpack is expected in Finnmark. However, less frequent icing in coastal and lowland regions may indicate a lesser need for winter road maintenance. [ABSTRACT FROM AUTHOR]

In this paper, we used a Bayesian model averaging (BMA) approach to analyse the changes in rainfall extremes in the periods 2041–2070 and 2071–2099 over northeast Bangladesh as a consequence of climate change. Climate change over this region could potentially impact agricultural production, water resources management, and the overall economy of the country. We used six regional climate models (RCMs) over the Coordinated Regional Downscaling Experiment South Asia domain. We used one medium stabilization scenario (RCP4.5) and one high‐emission scenario (RCP8.5) for projecting the extreme rainfall indices. A multi‐model ensemble mean was generated using the BMA approach. The BMA mean is a weighted average related to each RCM's predictive skill during the training period. Most of the rainfall extremes are expected to increase in both pre‐monsoon (March–May) and monsoon (June–September) seasons in the future compared with baseline (1976–2005). The average pre‐monsoon rainfall of the study area is projected to increase by 12.93 and 18.42% under RCP4.5 and 18.18 and 23.85% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. The average monsoon rainfall of the study area is projected to increase by 4.96 and 2.27% under RCP4.5 and 6.56 and 6.40% under RCP8.5 for the periods 2041–2070 and 2071–2099, respectively. All the extreme indices except consecutive wet day are expected to change significantly at the 95% confidence level during the pre‐monsoon season. The study area will potentially be subjected to more frequent floods in the future both in pre‐monsoon and monsoon seasons as a consequence of climate change. Notably, the intensity and the magnitude of flash flooding in the pre‐monsoon season are expected to increase more in the future because the increase in extreme indices is more significant during that season. [ABSTRACT FROM AUTHOR]

Understanding the reliability of global climate models (GCMs) to reproduce the historical surface wind fields is integral part of building robust projections of surface wind‐climate, and other wind‐dependent geophysical climatic variables. Understanding the skill of atmosphere‐only models (AGCM), coupled atmosphere–ocean models (AOGCM) and fully coupled earth system models (ESM) is likewise paramount to assess any systematic model improvements. In this paper, we systematically assess whether surface wind fields obtained from 28 CMIP5 GCMs can represent large‐scale spatial patterns and temporal variability of historical surface winds. We show that inter‐model uncertainty is typically 2–4 times larger than the uncertainty associated with GCM internal variability, although the latter can be significant within specific regions. We also find that CMIP5 models are typically capable of reliably reproducing large‐scale spatial patterns of historical near‐surface winds, but considerable uncertainty lies within the CMIP5 ensemble with strong latitudinal dependence. CMIP5 models show limitations in their ability to reliably represent inter‐annual and inter‐seasonal variability particularly within tropical‐cyclone‐affected regions. In further analysis, we quantify and intercompare historical wind bias from different types of models with different dynamical cores, based on multiple CMIP5 diagnostic experiments. We find that bias in surface wind fields are largely intrinsic to the atmospheric components of the models, and that the inclusion of carbon‐cycle dynamics has insignificant effect on simulated surface winds (at decadal time‐scales). Inconsistencies between AGCM and AOGCM simulations are largely driven by errors in sea surface temperatures (SST); though such differences are not statistically significant relative to the inter‐model uncertainty within the CMIP5 ensemble. These results show that the dominant source of bias in simulated wind fields lies in the underlying physics of the atmospheric component of the models. [ABSTRACT FROM AUTHOR]

This study focuses on Patagonia, where Foehn events observed in the lee of the Andes mountains are not yet well simulated by state‐of‐the‐art climate models. It has been agreed that one source of this shortcoming is related to the poor relief representation in models. To resolve this need, a common method used is to enhance the spatial resolution of the model to retrieve a more complex surface elevation, at the expense of calculation time or surface area covered. This paper tackles the problem from a different angle by addressing the Digital Elevation Model (DEM) generalization, that is, the altitudes generalization from a high‐resolution DEM to a coarser resolution grid model. Most current climate models use DEM generalization methods that smooth the relief, a key controlling factor in Foehn events modelling. The aim of this study is to compare three original methods of DEM generalization (percentile 90 [P90], envelope maximum [EM], and thalweg and crests [TC]) and to evaluate their impact on simulated precipitation and temperature fields on the eastern part of Patagonia, where warm and dry air masses are expected. Thanks to MAR, a Regional Climate Model, we validate the models at 10 and 5 km resolutions against the Climate Research Unit and perform three sensitivity experiments involving a change in the DEM generalization. Our results show that (a) a finer spatial resolution can slightly improve the temperature biases, however, it cannot resolve the precipitation biases and (b) a more appropriate use of DEM generalization induces a significant decrease in precipitation for the P90 and EM methods and an increase in mean temperature for all three methods in the study area. This study serves as a recommendation for a better use of DEM generalization in climate models performing in Patagonia, but also regions sharing the same orographic features as the Patagonian relief. [ABSTRACT FROM AUTHOR]

A characterization of the diurnal cycle of precipitation (DCP) over the whole South America is still lacking in the literature and the scarcity of sub‐daily rain ground measurements limits the data available for analysis and forbids a correct validation of alternative datasets. In this paper we analyse the climatological mean DCP during the monsoon active season using five gridded datasets: two satellite precipitation estimates, two regional climate models and one reanalysis. Amazonia, the Brazilian Highlands, the northeastern South American coast, the Andes and the western Colombian coast are identified as the areas with most prominent DCPs. The afternoon convection triggered by solar heating over land and the coastal and topographic effects are the main modes of sub‐daily variability, based on an EOF decomposition of the 3 hourly mean precipitation fields. We explore the contribution of mean frequency and intensity to amount of precipitation and nighttime–daytime differences. In general, both models precipitate earlier and more frequently than the satellite products and do not reproduce correctly areas of observed predominant nighttime precipitation where mesoscale convective systems are active, like La Plata Basin or Amazonia. Over the analysed areas, the high frequency of precipitation is the driving mechanism of total amount in the models, whereas in the satellite products there is also considerable contribution from intensity. Overall, the reanalysis shows features in between the models and the satellite estimates, sharing characteristics with both types of data. The results presented here point at the diversity of sub‐daily precipitation characteristics in South America, the issues with conventional climate models and the uncertainty in satellite products and reanalyses. The growing interest in the DCP by the scientific community and the development of new techniques like convection permitting modelling will hopefully continue to improve our knowledge of the precipitation dynamics and its influence on climate. [ABSTRACT FROM AUTHOR]

This paper presents the results of a study focused on the projected changes in extreme precipitation indices in Romania during three future periods: 2021–2040, 2041–2070, and 2071–2100. We investigated changes in future climate based on historical and modelled data sets of daily precipitation. We compared the values calculated for the three future periods with those obtained for the historical reference period 1961–1990. The historical observation data recorded at 30 weather stations and data extracted from six regional climate model outputs (ALADIN53, CCLM4‐8‐17, RACMO22E, RCA4, REMO2009, and WRF331F) under moderate (RCP4.5) and pessimistic (RCP8.5) scenarios were employed. Five indices established by the Expert Team for Climate Change Detection Monitoring and Indices were analysed: consecutive dry days, consecutive wet days, heavy precipitation days, very heavy precipitation days, and annual total wet‐day precipitation. The series of indices were generated using ClimPACT2 software. Based on bias correction methods, the quality of the modelled data considerably increased, and the errors obtained for the daily precipitation varied in the range of ±0.05 to ±1.22 mm/day. The main findings revealed an increase in three of the extreme precipitation indices based on all models considered compared to the historical period. The most important increase (more than 50%) compared to the historical period was detected in the frequency of very heavy precipitation days and the annual total wet‐day precipitation. They were followed by the heavy precipitation days index. The consecutive dry days index indicated mainly no change or a slow decrease, while inconsistencies among the results of regional climate models were detected for the consecutive wet days index. For all indices except the consecutive dry days index, the ALADIN53 and WRF331F models provided the highest projected values for both scenarios. The dispersion of values obtained as model output for each location was higher for RCP8.5 than for RCP4.5. [ABSTRACT FROM AUTHOR]

This paper highlights detailed projected changes in rainfall over Thailand for the early (2011–2040), middle (2041–2070) and late (2071–2099) periods of the 21st century under the representative concentration pathways (RCP) 4.5 and RCP 8.5 using the high‐resolution multi‐model simulations of the Coordinated Regional Climate Downscaling Experiment (CORDEX) Southeast Asia. The ensemble mean is calculated based on seven members consisting of six general circulation models (GCMs) and three regional climate models (RCMs). Generally, the ensemble mean precipitation agrees reasonably well with observations, best represented by the Global Precipitation Climatology Center (GPCC) data, over Thailand during the historical period (1976–2005). However, inter‐model variations can be large among ensemble members especially during dry months (December to March) for northern‐central‐eastern parts, and throughout the year for the southern parts of Thailand. Similarly for future projection periods, inter‐model variations in the sign and magnitude of changes exist. The ensemble means of projected changes in rainfall for both RCPs during dry months show distinct contrast between the northern‐central‐eastern parts and the southern parts of Thailand with generally wetter and drier conditions, respectively. The magnitude of change can be as high as 15% of the historical period, which varies depending on the sub‐region, season, projection period, and RCP scenario. In contrast, generally drier conditions are projected during the wet season (June to September) throughout the country for both RCPs where the rainfall reduction can be as high as 10% in some areas. However, the magnitude of projected rainfall changes of some individual models can be much larger than the ensemble means, exceeding 40% in some cases. These projected changes are related to the changes in regional circulations associated with the winter and summer monsoons, which are projected to weaken. The drier (wetter) condition is associated with the enhanced subsidence (rising motion). [ABSTRACT FROM AUTHOR]

Changes in characteristics of the distribution of temperature, in addition to shifts towards warmer temperatures, can have substantial implications for society and the environment. A change in shape, for example, can be caused by differences in the rates of warming of cold and warm extremes relative to average temperatures. These rates of warming vary both spatially and temporally, with strong geographic and seasonal differences in how local extremes are changing relative to local average temperatures. In this paper, we investigate how seasonal warm and cold tails of both daily maximum and minimum temperatures are changing relative to corresponding seasonal mean temperatures. Analysing gridded observations over land areas and climate models from the CMIP5 archive, we show that in recent decades, the greatest differences between warming rates of extremes and the mean occur in the cold tails of the distribution for many regions in the Northern Hemisphere extratropics during all seasons except boreal summer. Globally, June through August show the smallest differences between extremes and mean warming rates. While the climate models show relatively low spatial correlations in their change patterns with each other as well as the observations for past decades, future simulated changes are shown to be systematic and robust, with a clear signal in the warming of extremes relative to the mean. The strongest differences between warming rates of extremes and the mean are projected for Northern Hemisphere mid‐ to high‐latitude regions, where the cold tails warm more than the average for all seasons except boreal summer. This is especially widespread in autumn and spring, where the models predict cold extremes to warm at least 3°C more than mean temperatures for much of the Northern Hemisphere extratropics. The results presented here highlight the importance of considering all aspects of the distribution when analysing changes in temperature. [ABSTRACT FROM AUTHOR]

As solar energy will be an increasingly important renewable energy source in future years, the study of how climate change affects both its temporal and spatial variability is very relevant. In this paper, we study future changes of the solar radiation resource in the Iberian Peninsula (IP) through a set of simulations from ESCENA project (generation of regionalized scenarios of climate change in Spain with high‐resolution models) until mid‐century. The evaluation of the simulations against observations indicates contrasting biases for the different regional climate models (RCMs) in terms of solar irradiation amount and its inter‐annual variability. We propose a diagnostic for the quality of solar energy resource, in which the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) in the IP is well‐captured by the RCMs in general terms. The analysis of scenarios indicates a future increase in solar irradiation, although not all scenarios agree in the geographical distribution of this increase. The quality of solar energy resource is projected to increase, mostly due to a decrease in variability. This is an important result, as a more stable inter‐annual resource should decrease the need for backup sources and also reduce inter‐annual electricity price variations. Finally, results from a first approximation to the issue of the ability of solar energy to cover power demand peaks in summer show important differences between regions of the IP. However, the spatially averaged correlation of solar irradiation and summer surface temperatures for the whole IP is rather high, which is a positive result as the strong interconnections of the power grid within the IP could allow a distribution of solar power surpluses in certain regions for such high‐temperature episodes. Due to the importance of solar energy in the future we study future changes of the solar radiation over Iberian Peninsula through the simulations from ESCENA project. To evaluate the quality of solar energy resource, the gridpoints are classified in four categories depending on the combination of solar irradiation amount and variability. The observed large percentage of points in the optimal category (high irradiation/low variability) is captured by the models. The analysis of scenarios indicates a future increase in solar irradiation amount and variability, although not all scenarios agree in this respect. [ABSTRACT FROM AUTHOR]

Urban heat advection (UHA) can extend the climatic impact of a city into the surrounding countryside. This may lead to an intensification of already well‐documented urban heat island (UHI) impacts on health and infrastructure, and challenge the representativeness of long‐term reference temperature records taken near urban areas. However, previous UHA studies have been unable to accurately quantify surface‐level UHA due to challenges arising from complex urban land‐use patterns. To address this, the numerical Weather Research and Forecasting (WRF) mesoscale model coupled with the Building Energy Parameterization urban canopy scheme is used to simulate meteorological fields for idealized land‐use cases. Hypothetical square cities (up to 32 km in size) are simulated for a year's period. A time‐mean 2‐m temperature field (representing the canopy UHI) shows that the mean UHI intensity (up to 4.3 °C [SD 1.7 °C]), wind speed <3.9 m/s) is linearly related to the logarithm of city size. This finding, entirely derived from numerical modelling, is consistent with the log‐linear relationships previously found in the observational data of many cities in the world. A UHA methodology was then applied to the temperature fields to separate UHA from the UHI, with up to 2.9 °C (SD 1.7 °C) of UHA found downwind of the largest city size. For this hypothetical city size, an UHA intensity of 0.5 °C is found up to 24‐km downwind from the urban boundary. In addition, the UHA‐distance profiles along the central horizontal transect for various urban sizes are found to follow a scaling rule as a good approximation. As a result, the findings of this paper can be used as a starting point for climate impact assessments for areas surrounding urban areas without the need for complex, computation‐intensive simulations. Urban heat advection (UHA) can extend a city's impact on air temperature to the surrounding countryside, and impact upon nearby climate records. However, previous UHA studies have been unable to accurately quantify UHA due to challenges arising from complex urban land‐use patterns. Hypothetical square cities (2–32 km in size) were modelled to overcome this, finding a clear warming of up to 2.9 °C that extended several kilometres downwind. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the variability and its temporal‐scale components of daily air temperature and precipitation simulated by two regional climate models (RCMs) and two driving global climate models (GCMs) from the EURO‐CORDEX and CMIP5 projects. The analysis was performed for eight geographical areas of central Europe for the period 1971–2000. After a brief evaluation of total variance, we focus on the ability of the simulations to represent short‐term, seasonal and long‐term variability components. This analysis was carried out for original time series as well as for series with the annual cycle removed. A fast Fourier transformation and a Kolmogorov–Zurbenko filter were used to separate the three temporal components. The two methods of time series decomposition generally agree, which confirms the robustness of the results. Although the observed total variability of daily air temperature and precipitation is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. Regarding the air temperature, the results for the time series with the annual cycle removed are generally somewhat worse than for original series. The simulations tend to underestimate the short‐term component of temperature variability, while the results for the other two components are inconclusive. Moreover, it was found that temperature variability caused by the annual cycle is overestimated by the simulation of HadGEM2 GCM and CCLM4‐8‐17 RCM driven by HadGEM2, which explains the relatively poor results of these simulations regarding air temperature total variance and short‐term as well as seasonal components in the original temperature series. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. Although the observed total variability of daily air temperature and precipitation over selected European areas is not well represented in many cases and considerable differences among simulations exist, the separation of total variance into the three components is well represented. The RCMs provide better results than GCMs for temperature total variance but our results are rather inconclusive in the case of precipitation total variance and distribution of the variance of both studied variables into the three temporal components. In the figure, location of the eight studied areas situated in the vicinity of central Europe and the orography of the E‐OBS data set. [ABSTRACT FROM AUTHOR]

Rainfall in Vietnam is strongly influenced by climate drivers which are caused by anomalies in sea surface temperature (SST), sea level pressure (SLP) or geopotential height in the Pacific and the Indian Ocean. This paper analyses the rainfall records at different sites in various regions of Vietnam to determine the influence of the three major climate drivers, the ENSO (El Niño–Southern Oscillation), IPO (Interdecadal Pacific Oscillation) and IOD (Indian Ocean Dipole) and their interaction on the rainfall at each of these sites. A statistical technique called Bayesian model averaging (BMA) is used to discuss and address the uncertainties problem in model and variable selection when fitting observed rainfall data with climate drivers' indices. The results show that the ENSO, IPO and IOD and their interaction overall have very minor roles in influencing the rainfall in Northern Vietnam but they have some influences on the rainfall in the central and southern parts of Vietnam. Furthermore, seasonality analysis shows the spring rainfall in Vietnam is strongly influenced by the ENSO, IOD and ENSO*IOD interaction, with ENSO*IOD interaction strongest in the north. The situation in summer is similar but the IOD is most dominant compared to the ENSO and ENSO*IOD interaction. In autumn, the IOD and ENSO influences on rainfall are weaker while the IPO has strong influence in the Central and Southern Vietnam and no climate driver has any influence in the north. And during winter, the IPO, ENSO and IPO*ENSO interaction are associated with rainfall variability across Vietnam while the IOD influence is insignificant. The results also confirm the previous studies on the teleconnection roles of SST in the Pacific and Indian Oceans on rainfall variability in Vietnam. Influence scale of climate drivers on spring rainfall at various sites in Vietnam. [ABSTRACT FROM AUTHOR]

This paper compares extreme value statistics of daily maximum 10 m wind speed in winter simulated by the regional climate model COSMO‐CLM at a horizontal resolution of 15 km (C15) with the reanalyses ERA‐Interim and Arctic System reanalysis (ASR version 1 and 2) and with a satellite data set (CCMPv2). Our C15 simulation (1979/1980–2015/2016, November–April) is thereby the longest high‐resolution simulation available for the Arctic. The results show that the extreme wind speeds tend to increase over the ocean with increasing the horizontal model resolution. A horizontal resolution of ≤15 km is required to sufficiently capture all extreme wind characteristics, in particular for the tip jets and barrier winds over the Irminger Sea and in the Denmark Strait, and for the low‐level jets in the Nares Strait. Of almost equal importance are physical parameterizations of surface fluxes and of turbulence. Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. State‐of‐the‐art global climate models and reanalyses (e.g., ERA‐Interim) underestimate the extreme near‐surface wind speeds in the Arctic and around Greenland. Regional climate models, for example, ASR/WRF or COSMO‐CLM, resolve the mesoscale wind systems that frequently produce extreme winds, which we estimated i.a. as 10‐year return levels (see the figure). Capturing extreme wind characteristics has a direct effect on climate relevant air–ice–ocean interactions, such as triggering open‐ocean deep convection, polynya dynamics, or on the sea ice and freshwater balance of the Arctic. [ABSTRACT FROM AUTHOR]

This paper presents an evaluation of the relationship between near-surface air temperature (T) and precipitation (P) over Europe simulated by regional climate models (RCMs) from the EURO-CORDEX project. Simulations of two RCMs (RCA4 and CCLM4-8-17) driven by four different global climate models (GCMs) in the period 1971–2000 were analysed. Two different coefficients, the Pearson correlation coefficient based on monthly anomalies and the T-P index (TPI) based on daily values, were used for this evaluation. First, the comparison of T-P correlation and TPI patterns calculated from E-OBS data was made. Afterwards, simulated T-P relationship patterns were briefly evaluated. We especially focused on evaluating the RCM and GCM influence on the simulated T-P relationship pattern. Our results indicate that RCM simulations captured the basic seasonal dependence of the T-P relationship over Europe represented by both T-P correlation and TPI patterns. The simulated T-P relationship pattern is more strongly influenced by an RCM than a driving GCM in contrast to temperature means in winter and precipitation and temperatures changes, which are more strongly influenced by driving GCM as shown in previous studies. The simulations of the CCLM4-8-17 RCM demonstrated noticeably better agreement with observed T-P relationship fields than the RCA4 RCM simulations. The T-P relationship simulated by RCA4 tends to shift to positive values in comparison to E-OBS values. These findings were found for both TPI and T-P correlation. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper presents an evaluation study of the Regional Climate Model version 4.3 ( RegCM4.3) over the Loess Plateau in northern China, which is a semi-arid region characterized by complex topography. During recent years, a series of reforestation programmes have been implemented across the region that might influence the local climate in complex ways. To better understand the local climate, the RegCM4.3 was applied to simulate the present-day conditions over the Loess Plateau. The simulation was carried out from 1990 to 2009 at the 50-km horizontal resolution, with lateral boundary conditions taken from the ECMWF-Interim reanalysis. A series of climate variables and processes were evaluated during the winter and summer seasons, such as 2-m air temperature, precipitation, wind circulation, surface energy balance, full moisture budget, and cloud radiative forcing (CRF). The possible origins of the simulation bias and the physical linkages with other model processes were examined. In general, RegCM4.3 is able to reproduce both the spatial and temporal features of the regional climate over the Loess Plateau. However, there are still biases in some meteorological variables including precipitation and 2-m air temperature. In particular, the model tends to produce cold biases during winter and underestimate precipitation during summer. Further analyses indicates that the cold biases in winter may have resulted from the deficiency of the downward longwave radiation fluxes, excessive ground heat fluxes, and negative temperature advection by the seasonal mean circulation. These processes are primarily triggered by deficiencies in CRF and excessive northwesterlies over the plateau. The underestimated precipitation during summer is associated with a weak southerly monsoon in the model. A full moisture budget analysis reveals that the dry bias in this region can be mainly attributed to model deficiency in moisture advection and convergence, and to a lesser extent to that in surface evaporation. [ABSTRACT FROM AUTHOR]

Investigations into the role of anthropogenic emissions in the occurrence of extreme weather often use a method that compares simulations of atmospheric climate models run under a factual scenario of historical boundary conditions observed during the period of the event against simulations run under a counterfactual scenario of what those boundary conditions might naturally have been over that same period in the absence of anthropogenic emissions. A particular requirement for this experiment design is an accurate estimation of ocean surface boundary conditions for use by the counterfactual natural simulations. Here we use output from the CMIP5 multi‐climate‐model archive to develop a robust estimate of sea surface temperatures and sea ice conditions for use in counterfactual natural simulations, intended as a benchmark estimate to facilitate comparison across climate models and across studies. This development includes tests to ensure that the final estimate is stable from year‐to‐year and stable against other perturbations to the methodology, as well as consideration of the strengths and weaknesses in comparison to other available attributable warming estimates. While this estimate is tailored specifically for the International CLIVAR C20C+ Detection and Attribution Project, it can be used by related projects as well. [ABSTRACT FROM AUTHOR]

ABSTRACT In this paper, we analyse observed changes in characteristics of sub-daily heavy summer precipitation and erosive precipitation events for the Czech Republic, taking into consideration 51 years of data (1961-2011) for 17 stations. The sub-daily data were checked for consistency with the daily control records, and unreliable data were removed from the data set. Subsequently, four statistical tests were applied to assess the homogeneity of the data set. The trend magnitude was estimated using the Theil-Sen slope estimator, and two tests were applied to evaluate its significance. For most of the indices, the tests did not indicate inhomogeneity, with the exception of characteristics related to rainfall intensity. The trends are positive for most of the stations and most precipitation characteristics, and significant positive trends are much more frequent than negative trends. The analysis revealed a significant increase in the contribution of heavy precipitation to the precipitation total in half of the stations at short aggregation levels. [ABSTRACT FROM AUTHOR]

ABSTRACT This paper examines the skill of seasonal precipitation forecasts over Iran using one two-tiered model, three National Multi-Model Ensemble ( NMME) models, and two coupled ocean-atmosphere or one-tiered models. These models are, respectively, the ECHAM4.5 atmospheric model that is forced with sea surface temperature ( SST) anomalies forecasted using constructed analogue SSTs ( ECHAM4.5- SSTCA); the IRI-ECHAM4.5-DirectCoupled, the NASA-GMAO-062012 and the NCEP-CFSv2; and the ECHAM4.5 Modular Ocean Model version 3 ( ECHAM4.5- MOM3-DC2) and the ECHAM4.5- GML-NCEP Coupled Forecast System ( CFSSST). The precipitation and 850 hPa geopotential height fields of the forecast models are statistically downscaling to the 0.5° × 0.5° spatial resolution of the Global Precipitation Climatology Centre ( GPCC) Version 6 gridded precipitation data, using model output statistics ( MOS) developed through the canonical correlation analysis ( CCA) option of the Climate Predictability Tool ( CPT). Retroactive validations for lead times of up to 3 months are performed using the relative operating characteristic ( ROC) and reliability diagrams, which are evaluated for above- and below-normal categories and defined by the upper and lower 75th and 25th percentiles of the data record over the 15-year test period of 1995/1996 to 2009/2010. The forecast models' skills are also compared with skills obtained by (a) downscaling simulations produced by forcing the ECHAM4.5 with simultaneously observed SST, and (b) the 850 hPa geopotential height NCEP-NCAR (National Centers for Environmental Prediction-National Center for Atmospheric Research) reanalysis data. Downscaling forecasts from most models generally produce the highest skill forecast at lead times of up to 3 months for autumn precipitation - the October-November-December ( OND) season. For most seasons, a high skill is obtained from ECHAM4.5- MOM3-DC2 forecasts at a 1-month lead time when the models' 850 hPa geopotential height fields are used as the predictor fields. For this model and lead time, the Pearson correlation between the area-averaged of the observed and forecasts over the study area for the OND, November-December-January ( NDJ), December-January-February ( DJF) and January-February-March ( JFM) seasons were 0.68, 0.62, 0.42 and 0.43, respectively. [ABSTRACT FROM AUTHOR]

ABSTRACT The aim of this paper is to study the model's simulated frequency, track, intensity and location of cyclonic storms (CSs) and severe cyclonic storms (SCSs) in the Bay of Bengal (BoB), India. For the purpose, the PRECIS (Providing REgional Climates for Impacts Studies) a regional climate modelling system, of UK Met Office, is used. This model is integrated for the period 1961-1990 (baseline) and the future time period 2071-2100 (High emission scenario, A2). To run the model, the initial and lateral boundary conditions are supplied by UK Met Office. The analysis of frequency, track, intensity and location are carried out for May, June, September and October for the period 1961-1990 and 2071-2100. To evaluate the model's performance in simulating storms frequency during 1961-1990, chi square test is carried out with observed storms for the same period. The model's simulated frequency of storms is an overestimation of observations although the frequency of model's simulated storms during 2071-2100 is less than that of during 1961-1990. In general, model's simulated storms are found moving in the northwest direction from their initial location in all months during 1961-1990 and 2071-2100. In model's simulations, the drop in central pressure is relatively more in the months of May, June, September and October during 2071-2100 compared with that during 1961-1990. During 2071-2100, more intense storms may be possible in the months of May and June compared with that of September and October. It is observed that the model is able to simulate the initial locations of storms during 1961-1990 and 2071-2100 close to observations for the months of June and September, especially. [ABSTRACT FROM AUTHOR]

Climate projections for the UK exhibit substantial uncertainty, and this uncertainty is a hindrance to robust and timely decision making on both adaptation and mitigation policy issues. A large part of the uncertainty is associated with dynamical changes of the regional atmospheric circulation rather than thermodynamic changes which are better constrained by model simulations. Of particular importance for the UK is the extent to which the North Atlantic jet will change over coming decades and the impact this will have on weather and climate in the region. In this article, we propose the use of jet‐based storylines for assessing and communicating uncertainty in climate projections for the UK, wherein changes in each impact are explicitly conditioned on changes in the North Atlantic jet. This approach provides a framework for evaluating the impacts associated with a range of plausible future climate outcomes for the UK, including outcomes that may not be well represented in the current generation of climate models, and for communicating these potential outcomes. We construct a simple yet useful set of future jet storylines for both summer and winter and for 2°C and 4°C global warming levels and illustrate the utility of the approach by evaluating the impact of each jet storyline on future changes in UK precipitation. In doing so, we demonstrate that the relationships between the jet and UK precipitation are consistent between observed interannual variability and projected changes. This finding increases our confidence in projecting changes in UK precipitation associated with each storyline. [ABSTRACT FROM AUTHOR]

Improved seasonal precipitation forecasts can enable more effective water resource management decisions in a number of sectors, including municipal supply, agriculture, hydropower generation, and tourism. This study develops an effective straightforward statistical approach to enhance the quality of seasonal precipitation forecasts through the utilization of El Niño–Southern Oscillation (ENSO) information projected by Coupled General Climate Models (CGCMs). A stochastic weather generation (WG) model is developed to predict seasonal precipitation condition on ENSO condition. The WG model links a nonhomogeneous Markov Chain representing ENSO occurrence model to a bivariate normal distribution for seasonal precipitation conditioned on ENSO phase. Two verification metrics are suggested to measure the degree of predictability of raw, calibrated and climatological seasonal precipitation forecasts over northwest Costa Rica as a case study. Results indicate the potential to narrow the uncertainty of seasonal precipitation forecasts by incorporating CGCMs ENSO cycle information. Precipitation during the late part of the wet season (LS) has more predictability than precipitation in the early part of the wet season (ES). In addition, the degree of predictability decreases with an increase in lead time for a given forecast. A lead time of 1 year maintains a moderate level of predictability likely to support tangible benefits to various decision‐making processes. [ABSTRACT FROM AUTHOR]

Many studies have shown that the climate system has the long‐range correlation (LRC) characteristics, which provide a good test bed for evaluating the performance of climate models. In this study, the LRC characteristics of the observed surface daily temperature data in China is first analysed by detrended fluctuation analysis. It is found that the daily temperature records, including daily mean temperature (Tmean), daily maximum temperature (Tmax), and daily minimum temperature (Tmin), all exhibit LRC characteristics with scaling exponents more than 0.6 in most of China. Then, the performances of two different versions for the Beijing Climate Center Climate System Model (BCC‐CSM), namely, BCC‐CSM1.1m in CMIP5 and BCC‐CMS2‐MR in CMIP6, are quantitatively evaluated by comparing the differences of the LRC characteristics between the observations and the simulations. The results indicate that BCC‐CSM2‐MR performs better than BCC‐CSM1.1(m) in western Northwest China, most of south of the middle and lower reaches of Yangtze River, North China, central Inner Mongolia for the daily Tmean and Tmax. As far as the daily Tmin is concerned, BCC‐CSM1.1(m) performs better than BCC‐CSM2‐MR in western South China and southern Xinjiang, and the performance of BCC‐CSM2‐MR in CMIP6 has not been significant improved. Compared with BCC‐CSM1.1(m), BCC‐CSM2‐MR cannot significantly improve the simulations for the LRC characteristics of the three daily temperature variables in central and eastern Qinghai‐Tibet plateau, and the areas of between lower reaches of Yellow River and Yangtze River. [ABSTRACT FROM AUTHOR]

Climate change effects on subdaily rainfall (from 5 min to a few hours) can hardly be measured in mid‐latitude climates due to the high natural variability of the precipitation patterns and their effects on local topography. The goal of this study was to obtain change projections of intensity–duration–frequency (IDF) curves, for up to 2‐h precipitation events, comparing two approaches that use the daily outputs of the downscaled Coupled Model Intercomparison Project Phase 5 (CMIP5) multi‐model projections: (a) direct scaling of the expected probable precipitation, from 2‐year to 500‐year return periods of daily rainfall and (b) a new semi‐stochastic approach, built by combining the physically forced outputs of climate models (on a daily scale) and stochastic simulation given by the probability distribution of a concentration index (n‐index) for individual rainfall events (on a subdaily scale). The approaches were applied to a set of 27 stations located around Barcelona, Spain, including a long reference series (with 5‐min rainfall records since 1927), representing the highly variable Mediterranean climate. The validation process showed a systematic error (bias) generally smaller than 10%, especially for rainfall extreme events with durations of less than 2 h. The concentration n‐index and IDF curves were projected by 10 downscaled CMIP5 climate models under 2 emission scenarios (RCP4.5 and RCP8.5), obtaining a consensual increase in both relative concentration and absolute intensities in Barcelona. Ensemble projection of rainfall concentration (n‐index) showed an increase up to 10% by 2071–2100 and about 20% (15%–30% range) for maximum intensities of 2‐year to 500‐year return periods. Results provide robustness in decision‐making regarding the design of stormwater management infrastructure at a local scale. [ABSTRACT FROM AUTHOR]

Skilful prediction of the seasonal Indian summer monsoon (ISM) rainfall (ISMR) at least one season in advance has great socio‐economic value. The ISM is a lifeline for about a sixth of the world's population. The ISMR prediction remained a challenging problem with the subcritical skills of the dynamical models due to a limited understanding of the interaction among clouds, convection and circulation. In this study, we have analysed the seasonal mean of high cloud fraction, ice mixing ratio and ice cloud fraction from satellite and reanalysis and demonstrated their importance for ISM. The variability of the mixing ratio of cloud ice in different time scales (3–7 days, 10–20 days and 30–60 days bands) is also examined from reanalysis during ISM. Here, we have shown the teleconnection of different cloud variables over the ISM region with global sea surface temperature. We found that they are tied with slowly varying forcing (e.g., El Niño and Southern Oscillation). Besides, the correlation of cloud ice with different indices (Niño, Pacific Decadal Oscillation, North Atlantic Oscillation and Extratropics) may enhance the potential predictability of ISMR. The representation of deep convective clouds, which involve the ice‐phase processes in a coupled climate model, strongly modulates ISMR variability in association with global predictors. The results from the two sensitivity simulations using coupled global climate model (CGCM) demonstrate the importance of the cloud ice on ISM rainfall predictability. Therefore, this study provides a scientific basis for improving the simulation of the seasonal ISMR by developing the physical processes of the cloud on a subseasonal time scale and motivating further research in this direction. [ABSTRACT FROM AUTHOR]

The performance of the climate simulations by the regional climate model COSMO‐CLM forced by MPI‐ESM‐LR, as well as projected future temperature and precipitation conditions over northwestern Türkiye at the convection‐permitting resolution, are shown in this study. In terms of mean, maximum and minimum temperatures and daily total precipitation, the model response was examined. Comparisons with observations were made on an annual and seasonal basis. The convection‐permitting model (0.0275°) provides a satisfactory representation of annual and seasonal mean temperatures according to bias, MAE and RMSE. Better results were found for precipitation with respect to RE, MAE and RMSE. Finally, we used the RCP8.5 emission scenario to investigate future climate changes in terms of average temperature and precipitation variations for northwestern Türkiye, including Istanbul, over two projection periods (2041–2060 and 2071–2090) compared to the reference period (1991–2005). The model predicts significant warming in northwestern Türkiye, particularly in Istanbul and its environs, by the end of the century, as well as a general decrease in precipitation, especially evident in the spring and summer. The findings of this study can be used to develop climate adaptation policies based on temperature and precipitation variables in and around the study area. [ABSTRACT FROM AUTHOR]

The main goal of this research is to assess the Indian Monsoon Data Assimilation and Analysis (IMDAA), a recently established high‐resolution (0.12° × 0.12°) reanalysis dataset, for observing cloudburst events over the Northwest Himalaya (NWH). In addition, a high‐resolution (0.1° × 0.1°) satellite estimate, the Integrated Multi‐satellitE Retrievals for Global Precipitation Measurement (GPM) version 6 (IMERG‐V06B) (Final run), is validated against the IMDAA. The following is a summary of our significant findings. (1) Reanalysis data from the IMDAA detects 11 out of 16 cloudburst incidences. In addition, 10 events captured in the IMERG‐V06B data are a subset of those captured by IMDAA. According to contingency measures, the probability of detection (POD) of IMERG‐V06B at 99.99th percentile is 33.33%–63.39% along the geographical extent of NWH. When utilizing the improved probability of detection (IPOD) to account for time, the possibility of IMERG‐V06B detecting cloudburst occurrences ranges from 41.24% to 68.25%. (2) According to the finding, the IMDAA accurately detects cloudburst events in Jammu and Kashmir (J&K), where data from the India Metrological Department (IMD) at a resolution of 0.25° × 0.25° have underperformed. As a result, we can deduce that IMDAA can be used to observe extreme events in the Himalayas. IMERG‐V06B is also relevant satellite data for monitoring cloudburst events and validating climate models. [ABSTRACT FROM AUTHOR]

Five regional climate models (RCMs)—CCLM, RegCM, HadGEM3‐RA, SNURCM and WRF—participating in the Coordinated Regional Climate Downscaling Experiment‐East Asia (CORDEX‐EA) Phase 2 project are evaluated for their ability to simulate spatiotemporal variability in Asian summer precipitation. For this purpose, two dynamical downscaling sets, experiments forced by ERA‐Interim reanalysis data (reproduction experiment) and historical data from three Coupled Model Intercomparison Project 5 (CMIP5) general circulation models (GCMs) (historical experiment) are analysed. The horizontal resolution of the downscaled results is 25 km, and the analysis period is from 1981 to 2005 (25 years). The RCMs show reasonable performance in simulating the spatial and temporal characteristics of summer precipitation in CORDEX‐EA Phase 2 domain. To assess spatiotemporal patterns in Asian precipitation, cyclostationary empirical orthogonal function (CSEOF) analysis is used. In the first mode representing seasonal variations, the model ensemble results of both the reproduction experiment (Rep_ENS) and the historical experiment (His_ENS) simulate the peak times, location of precipitation and progression of the monsoons in Asia reasonably. Their features are greatly influenced by the moisture flux, indicating that the relation between precipitation and 850 hPa moisture flux is depicted well by RCM ensembles. In the second mode, which represents intraseasonal variations, the Rep_ENS depicts the western North Pacific summer monsoon break (WNPSM break) phenomenon similar to the observation. Although the His_ENS simulates the WNPSM break later than the observation, it does present the intraseasonal variation in the East Asian summer monsoon region. Based on these results, the five RCM ensemble results show the ability to capture spatiotemporal characteristics of summer precipitation in CORDEX‐EA Phase 2 domain, as well as added value from dynamical downscaling. [ABSTRACT FROM AUTHOR]

The present research focuses on temperature change signals over the Carpathian Basin with a special focus on selected lowland and mountainous subregions. High‐resolution (0.11°) EURO‐ and Med‐CORDEX regional climate model (RCM) simulations of near‐surface air temperature are analysed based on raw and bias‐adjusted data. The mini‐ensemble consists of eight RCM simulations driven by five different general circulation models for the period 1976–2099 under the high‐end RCP8.5 scenario. The high‐resolution, homogenized and quality controlled CARPATCLIM was used as a reference dataset. The selected subregions cover eight municipalities located at diverse altitudes: Bratislava, Budapest, Brassov, Debrecen, Hoverla, Novi Sad, Pécs and Poprad. The following climate indices are assessed: summer days, ice days, frost days, tropical nights, the coldest day, the warmest day, the coldest night and the warmest night. In general, for the reference period (1976–2005) bias‐adjusted RCM data showed almost perfect match with observations. Accordingly, no best performing RCM is found for all indices. The ensemble mean of the bias‐adjusted RCM simulations projects an increase (decrease) of 32% and 112% (18% and 25%) in the annual number of summer days and tropical nights (frost days and ice days) for the period 2021–2050. For 2070–2099 we can expect more frequent tropical nights (about five times) with respect to the reference period and the frequency of frost days can be halved. Profound warming manifests in the increase of the warmest temperature of day of up to 2–3°C by the near future and of 5–7°C by the end of the 21st century, which means the absolute maximum temperature can reach 44–47°C for the period 2070–2099. Our results also highlight the need for bias‐adjusted data adapted by different sectors (human health, agriculture, transport, disaster management, heritage conservation) under the national adaptation strategies. [ABSTRACT FROM AUTHOR]

Extreme precipitation events are a major natural hazard and cause significant socio‐economic damages. Precipitation events are spatially extended and, thus, can cause large water accumulations, which can lead to flooding events. In order to help design flood protection infrastructure, a detailed investigation of the temporal and spatial dependencies of extreme precipitation is essential. Here, we use a statistical spatial extremes framework to systematically study the historical and projected spatial–temporal characteristics of extreme precipitation in Germany. For this purpose, we use data from 10 high‐resolution global climate model‐regional climate model (GCM‐RCM) combinations from the EURO‐CORDEX initiative and derive a statistical spatial extremes precipitation model. Our results show that there are large spreads in reproducing the temporal–spatial characters of extreme precipitation. Few climate simulations can well present the temporal clustering of observed extreme precipitation in both summer and winter. In reproducing the spatial dependencies of the observations, most GCM‐RCM combinations behave well in summer, while in winter most RCMs produce too many spatially localized extreme precipitation events. The derived statistical model, which accounts for both the spatial and temporal variability, performs well in representing the spatial dependency and intensity characteristics in summer. Furthermore, global warming will have a significant impact on the temporal and spatial dependencies of extreme precipitation in Germany. There will be more temporal‐dependent and homogeneous extreme precipitation in summer; and more temporal‐independent and localized extreme precipitation in winter. The intensity quantified by the 25‐year return level of the 10 GCM‐RCM combinations is increasing; with relative changes ranging from 5.33% to 53.24% in summer and from −15.38% to 32.33% in winter under RCP8.5. A future projection by our statistical spatial extremes model using projected temperature from GCM‐RCM combinations as a covariate shows that the 25‐year return level will increase by 3.02% under RCP2.6 and 4.16% under RCP8.5 in winter. [ABSTRACT FROM AUTHOR]

The depth of the aerosol layer at the Villum Research Station at Station Nord in the high Arctic is analysed based on 8 years of observations from a ceilometer and one full year from a wind lidar. The layer is of particular interest for aerosol process modelling and atmospheric chemistry studies. The depth of the aerosol layer is assigned to the inflection point in the attenuated backscatter profile by two methods; one is based on polynomial approximation of the profile and the other is direct numerical differentiation. The analysis is based on two types of hourly profiles; one consists of averaging the attenuated backscatter observations and the other by computing the median. Due to sporadic occurrence of outliers in the ranges around 50 m in the ceilometer observations, this part of the profile is not used in this study. Restricting the observations to heights above 100 m, the depths of the aerosol layer are found to be typically ≈230 m. It varies little between winter and summer, but the spread in the depth is larger during the winter as compared to summer. To extend the study of the aerosol‐layer depth below 100 m, a method is applied that combines the ceilometer measurements with the carrier‐to‐noise ratio from the wind lidar. The results are available for 2018 only, and they show aerosol‐layer depths below ≈80 m as well as depths around 230 m and they show few observations between ≈80 and ≈230 m. Near the ground, the observed backscatter exhibits a pronounced seasonal variation, having low values during the summer and high values during the winter. The strength of the seasonal variability decreases with height, especially above the aerosol‐layer depth, and is virtually absent at 1 km. [ABSTRACT FROM AUTHOR]

Classifications by self‐organizing maps (SOMs) have been used to study links between extreme weather events and atmospheric circulation. However, SOMs may often provide a too generalized image of circulation, which is too coarse to properly resolve meteorological extremes. Several approaches have been used to circumvent this limitation, including an increase in the number of nodes and/or relaxation of the stress on topological ordering of nodes, through adjusting the neighbourhood radius. Here, we (1) test whether weighting of training fields based on extremity of their circulation could lead to SOMs that would better represent circulation extremes without impairing the topological ordering of nodes, and (2) we investigate whether Sammon maps (SM), which have so far been limited to validation of SOMs in climatology, could provide a viable alternative to SOMs in studying weather extremes. We show that removing a certain percentage of circulation fields closest to the climatological mean has a greater effect on the representation of circulation extremes than more than doubling the SOM array size. Additionally, this step allows for decreasing the neighbourhood radius, the parameter responsible for topological ordering, which would not otherwise be possible without impairing the topology of the map. The SOMs are compared with two newly developed methods that discretize SMs based on arbitrarily chosen and distribution‐based bins. We demonstrate the usefulness of both methods in climate model validation and show that both methods are able to capture circulation variability in its extremes in more detail than the SOMs. The distribution‐based method does not suffer from sample‐size issues and outperforms all other classifications in extracting ERA5 patterns leading to extreme temperature minima over central Europe. Our results suggest that classifications based on SM could be a viable alternative to SOMs when SOMs lack the ability to discriminate circulation outliers leading to temperature extremes. [ABSTRACT FROM AUTHOR]

In this study, we focus on the features and predictability of winter 2‐m air temperature (t2m) over China under different intensities of La Niña events. The seasonal and intraseasonal prediction skills of winter t2m over China are examined by four state‐of‐art seasonal climate forecast models. The results show that the China winter t2m anomaly is sensitive to the intensity of La Niña. Strong and moderate (S/M) La Niña events are often accompanied by cold winter over China, while a positive anomaly of mean t2m over China frequently occurs in the weak La Niña events. The intensity difference of the East Asian winter monsoon (EAWM) results in this discrepancy. All models have a slightly higher anomaly correlation coefficient (ACC) for the S/M La Niña than that for the weak La Niña in leading 1 month (LT1). Both individual models and MME exhibit higher skills of anomaly sign consistency rate (ASCR) for the S/M La Niña, which implies that models predict t2m anomaly over China more accurately in the S/M La Niña events. Furthermore, the winter t2m over China presents larger intraseasonal variability for the S/M La Niña than that for the weak La Niña. Models can capture the t2m anomaly over China more accurately when the temperature varies gently with small intraseasonal variability during winter for the S/M La Niña. [ABSTRACT FROM AUTHOR]

Drought is one of the most serious natural disasters exacerbated by climate change. Changes in precipitation and temperature in the future increase the likelihood of drought in China. In this study, a stepwise cluster ensemble downscaling (SCED) model was developed to bias‐correct projections of temperature and precipitation from multiple RCM outputs, and further characterized the drought hazards. The developed SCED model was used to aggregate and correct the results of multiple regional climate models, and its performance was proved to be reliable by comparing with the observed results. The proposed SCED method has been applied for drought projections over the Fujian province, China. The results showed that the changes of precipitation and temperature in Fujian would have obvious spatial heterogeneous characteristics. The temperature in the southeast coastal areas will increase by up to 4°C and the precipitation will decrease by 3.1% in the late 21st century, while the temperature rises and precipitation increases in the southwest. Temperature in inland areas will be lower and precipitation will be less. The drought hazards were also characterized by both SPI and SPEI based on biased‐corrected projections from SCED model. According to the SPI and SPEI indices, although the number of dry months in Fujian province will not change significantly in future, the spatial and temporal heterogeneity may become more explicit. Moreover, the moderate drought (from SPI) may increase while the general drought may decrease (from both SPI and SPEI). For extreme droughts, there would not be visible changes detected by SPI, but an increasing trend characterized since the impact of temperature was included in SPEI. In addition, there would be an increasing trend on drought when increasing temperature and precipitation occurred simultaneously. [ABSTRACT FROM AUTHOR]

The Iberian Peninsula is a known climate change hotspot. In the last decades, the Coupled Model Intercomparison Project (CMIP) has allowed for thousands of Global Climate Model (GCM) simulations to be conducted, an important tool to assess and understand future changes in Earth's climate. The comparison of past future projections with observations provides a robust evaluation of the models' skill according to different emission scenarios. Here, a comprehensive performance assessment of GCM‐simulated mean, maximum, and minimum temperatures, and accumulated precipitation is conducted for Iberia, by retrospectively comparing historical simulations and past future projections from CMIPs 1 to 6 with reference datasets. From means to extremes, and multi‐year intra‐annual cycles to inter‐annual trends, GCM simulations are compared with Iberia0.1 and E‐OBS observational gridded datasets, and ERA5 reanalysis. The matching between the variables' distributions is assessed through the distribution added values (DAVs), a measure of gain or loss in performance between CMIPs. Results show relevant improvements in the description of the Iberian climate throughout the CMIP effort, for historical and past future periods. While the representation of intra‐annual cycles (inter‐annual trends) is enhanced after CMIP3 (CMIP2), GCMs from all CMIPs are generally able to depict the observed warming trend. Nevertheless, until 2021, a slight detrimental effect in the performance of CMIP6 models is found, in comparison with CMIP5 ones, with positive DAVs obtained only for past future temperature projections (less than 2%). A continuous monitorization of modelling accuracy for Iberia is needed, considering the increasing relevance of climate change information for adaptation strategies. [ABSTRACT FROM AUTHOR]

ABSTRACT Sub-daily rainfall extremes may be associated with flash flooding, particularly in urban areas but, compared with extremes on daily timescales, have been relatively little studied in many regions. This paper describes a new, hourly rainfall dataset for the UK based on ∼1600 rain gauges from three different data sources. This includes tipping bucket rain gauge data from the UK Environment Agency (EA), which has been collected for operational purposes, principally flood forecasting. Significant problems in the use of such data for the analysis of extreme events include the recording of accumulated totals, high frequency bucket tips, rain gauge recording errors and the non-operation of gauges. Given the prospect of an intensification of short-duration rainfall in a warming climate, the identification of such errors is essential if sub-daily datasets are to be used to better understand extreme events. We therefore first describe a series of procedures developed to quality control this new dataset. We then analyse ∼380 gauges with near-complete hourly records for 1992-2011 and map the seasonal climatology of intense rainfall based on UK hourly extremes using annual maxima, n-largest events and fixed threshold approaches. We find that the highest frequencies and intensities of hourly extreme rainfall occur during summer when the usual orographically defined pattern of extreme rainfall is replaced by a weaker, north-south pattern. A strong diurnal cycle in hourly extremes, peaking in late afternoon to early evening, is also identified in summer and, for some areas, in spring. This likely reflects the different mechanisms that generate sub-daily rainfall, with convection dominating during summer. The resulting quality-controlled hourly rainfall dataset will provide considerable value in several contexts, including the development of standard, globally applicable quality-control procedures for sub-daily data, the validation of the new generation of very high-resolution climate models and improved understanding of the drivers of extreme rainfall. [ABSTRACT FROM AUTHOR]

Daily climate data are extremely important to drive ecological models, but the outputs of climate models are often deviated from the observations. The bias correction methods mostly highlight the target statistical parameters (e.g., mean and variance) for a target period, but perturb the simulated change trend and variability. In this study, we utilize a trend‐preserving bias correction method that can simultaneously consider the frequency and intensity of climatic variables to adjust the daily maximum and minimum temperature, and precipitation (Tmax, Tmin, and Pre) from CORDEX‐EA regional climate models for the period of 2021–2050 over China. Results show that: (a) the bias correction method greatly improves the spatial patterns of simulated Tmax, Tmin, and Pre since the corrected data has higher correlation and smaller differences across the whole domain. The differences in mean Tmax, Tmin, and Pre between climate models and observations have been reduced from −4.5 ± 6.41°C, −2.06 ± 0.58°C, 177.25 ± 51.55 mm before correction to 0.01 ± 0.02°C, −0.01 ± 0.02°C, −26.22 ± 6.39 mm after correction, respectively. In addition, the simulated change trend and climate variability at all time scales are well preserved. (b) Spatially, the warming rates in the west would be higher than in the east, and they are larger under the most severe future emissions scenario (RCP8.5) relative to the low‐mid emissions scenario (RCP4.5). The Pre change would mainly occur over east China with the significant upward trend. The dataset presented in this article has been published in the Network Common Data Form (NetCDF) File Format at https://doi.org/10.5281/zenodo.5058620. [ABSTRACT FROM AUTHOR]

Using 2001 and 2016 land use (LU) data and three developed deforestation LU scenarios, namely Deforest I, II and III, the possible impacts of the ongoing land use land cover change (LULCC) over West Africa (WA) and the uncertainty regarding the impacts on regional climate were assessed. Twelve Weather Research and Forecasting (WRF) model simulations were configured with the default and integrated MODIS satellite dataset for two sets of 3 months (December–February DJF 2011–2012 and July–September JAS 2012) over WA. Between 2001 and 2016, changes that occurred in precipitation, temperature and dew point temperature due to LULCC are mostly not significant at the 95% level with a decrease of 0.8, 0.0 and 0.1%, respectively. Deforest I, II and III caused significant changes with an increase of 44.4, 44.7, 41.5%; 0.8, 0.9, 1.1%; and 0.3, 0.1, −0.2%, respectively, for precipitation, temperature and dew point temperature. Precipitation was lower in the simulation with 2016 LU than it was with 2001 LU, which may have also been influenced by the reforestation that occurred between the periods. However, Deforest III resulted in a widespread nonsignificant rise in precipitation, as the modified areas showed a significant decrease during JAS period. Areas where forests were converted into barren lands revealed severe increase in the surface albedo which is a strong factor that its alteration is capable of influencing precipitation and other variables over WA. LULCC also altered the atmospheric column by modifying the strength of the African Easterly Jet (AEJ), Tropical Easterly Jet (TEJ), wind speed, and monsoonal, vertical, meridional and zonal winds. Heat related fields, such as dew point and air temperature, and moisture fields such as relative humidity were also modified by LULCC. Therefore, as WA reacts strongly to LULCC, sustainable land management practices should be encouraged to control its adverse effects. [ABSTRACT FROM AUTHOR]

Western Disturbances (WDs) are the primary contributor of winter precipitation over the Western Himalayas (WH). The precipitation falling as snow is essential for Himalayan glaciers accumulation, contributing to Himalayan rivers supporting downstream population of the Indian subcontinent. Climate change has affected the Himalayas, which is noticeable through lesser snowfall and higher melt leading to critical water resource stresses and managements. Available climate models are one of the best available tools to assess precipitation changes over and around the Himalayas. Inherent uncertainties and errors in these numerical models need a certain urge for improvement. Integration of statistical models and methods on top of dynamical models provide better insight and utility in associated processes. Thus, a statistical relationship between various predictors and precipitation over the region is investigated. Available observations from APHRODITE and ERA‐I and model fields from EC‐EARTH‐RCA4 (a CORDEX‐SA member) is used. Multiple linear regression (MLR) model is used to establish statistical estimates using meteorological variables as predictors and precipitation. The correlation between the predictors and precipitation is determined, and a contour depicting maximum correlation is used to estimate regression coefficients. The MLR model is trained during present (1987–2005) and tested during 2006–2007. Higher positive correlations between observations and MLR model are found. This developed MLR model is then implemented for assessing future WDs under RCP8.5 scenario. The intensity and frequency of WDs are calculated during the present and future. Model‐based WDs' intensities slightly deviate from the corresponding observations in present. But the model‐based WDs' frequencies match with the corresponding observations in present. The results indicate that WDs show a decreasing trend in frequency and intensity in the present and near‐future. But in far‐future intensity of WDs shows an increasing trend. [ABSTRACT FROM AUTHOR]