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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]

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]

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]

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]

High‐resolution simulation is important for understanding climate change and its impact over regions with complex terrain. Northern Asia (NA) is such a region and sensitive to global warming. However, the performance of the regional model over NA is still unclear. In this study, we conducted a two‐decade‐long high‐resolution (25 km) simulation over NA using the Weather Research and Forecasting (WRF) model forced by the ERA‐Interim reanalysis and then evaluated the performance of WRF against the observations. The results show that the WRF simulation, set up with selections of the physical schemes, can reasonably reproduce the spatial and temporal features of surface air temperature and precipitation over NA. In addition, the WRF simulation can add more regional details on the climate features than the ERA‐Interim data, especially over the NA regions with complex terrain. The spatial‐pattern correlation coefficient of summer precipitation over NA between the WRF simulation and observations is higher than that of the ERA‐Interim data. WRF can reasonably reproduce the annual cycles of precipitation. Moreover, the observed year‐to‐year variations in summer surface air temperature and precipitation over most NA subregions could be generally captured by the WRF. In conclusion, although systemic model bias still exists, the evaluations in this study indicate that WRF shows reasonable performance in a high‐resolution simulation of climate over NA, consequently providing a scientific basis for future simulation studies over the region. [ABSTRACT FROM AUTHOR]

The surface air temperature seasonal cycle in China has clearly changed in response to global warming, but its future projection has yet to be studied. Here, we investigate the performance and projection of the temperature seasonal cycle in China in 32 global climate models participating in the Coupled Model Intercomparison Project 6 (CMIP6). The characteristics of the temperature seasonal cycle, as represented by the amplitude and phase, are measured by the Fourier transform. The results show that the CMIP6 models can reasonably capture the observed typical temperature seasonal cycle feature with clear north–south gradients both for amplitude and phase in China. In the twenty‐first century, the models robustly project a delay in the phase across the whole country and a reduction in the amplitude in most of China relative to the reference period. Those changes are similar in the two shared socioeconomic pathway (SSP) scenarios, with overall larger magnitudes in the SSP5‐8.5 scenario than in the SSP2‐4.5 scenario. Projected amplitude changes are mainly linked to surface energy flux changes, especially latent heat flux changes. In response to future global warming, larger increases in the latent heat flux and thus stronger surface evaporative cooling lead to weaker surface warming in summer than in winter, contributing to a decrease in the amplitude of the temperature seasonal cycle in most regions of China. [ABSTRACT FROM AUTHOR]

We introduce a technique for identifying daily precipitation records based on the analysis of maximum daily precipitation events within running 30‐day periods. The technique is applied to the E‐OBS daily precipitation dataset for the European region spanning the period 1950–2018. We find that, on a Europe wide scale, the number of actual precipitation records as defined above exceeds the theoretical value expected under stationary climate conditions by a factor increasing with time by up to 1.6–1.8. This result, which has substantial subregional variability, is indicative of an increase of daily precipitation extremes consistent with what expected from climate warming conditions. We also apply the technique to the ERA5 reanalysis and four illustrative 21st century projections conducted with two regional climate models under the high end RCP8.5 and low end RCP2.6 greenhouse gas concentration pathways. We find a sustained increase of precipitation records throughout the 21st century with respect to the expected values for the RCP8.5 scenario, and a much reduced signal for the RCP2.6. In both cases the overall increase in actual to expected ratios of precipitation records is consistent with the warming trends found in the two scenarios, although this signal is highly variable in space and depends on the model being analysed. Our technique can be useful in studies of detection and attribution of extreme precipitation events and in the assessment of the impacts of increasing extremes under global warming conditions. [ABSTRACT FROM AUTHOR]

This article investigates seasonal and annual trends of near‐surface temperature (NST) during the last century (1901–2018) over Ghana. The ability of 24 global climate models (GCMs) from the sixth phase of the Coupled Model Intercomparison Project (CMIP6) to simulate abrupt mean temperature changes in Ghana from 1980 to 2014 was assessed. The trends' magnitude and abrupt changes were detected using Sen Slope Estimator (SSE) and sequential Mann–Kendall (SQMK) tests, respectively. Root mean square difference, correlation coefficient, and mean bias (B) were used to determine the skilfulness of GCMs in simulating NST relative to Climatic Research Unit (CRU TS4.04) datasets. Ghana's Southern belt is characterized by low temperatures, averaging 25°C to 27°C, whilst the northern belt is characterized by high temperatures (29–31°C). The inter‐annual variability of temperature over Ghana exhibits an increasing trend. Findings depict a significant increase in both the annual and seasonal temperatures of Tmin and Tmean, with a steady increase in Tmax. Results revealed the rate of increase had been higher in the country's northern regions (0.5°C) than in the south (0.3–0.4°C) in recent decades. The model performances for the interannual variability of annual and FMAM temperatures are better than their JJAS seasonal performances. The study's overall model ranking shows that Ghana's best performing models for annual, FMAM and JJAS seasons are INM‐CM5‐0, NorCPM1, and KACE‐1‐0‐G, respectively. Observations and comparisons provide useful reference values for a comprehensive assessment of various applications. [ABSTRACT FROM AUTHOR]

Tropical cyclones (TCs) forming over the South China Sea (SCS) are hard to predict as they often make landfall shortly after their formation, resulting in a severe loss of properties and human lives to the adjacent countries. As climate change continues to pose a threat to our planet, it is of importance to understand how TCs forming over the SCS might change under global warming. In this study, we examine the potential changes of local TC activity over the SCS based on HiRAM (high‐resolution atmospheric model) simulations and projections at 0.23 × 0.23° horizontal resolution with the representative concentration pathway (RCP) 8.5 scenario. The SCS TC activity, including genesis number and passage frequency, is projected to decrease significantly (about 59%) in the late 21st century period (2075–2099) compared to the present‐day period (1980–2004). Analysing the genesis potential index (GPI) clearly indicates that the suppression of SCS TC activity comes mainly from the decrease in low‐level vorticity over the SCS. From a large‐scale circulation view, the decrease in low‐level vorticity can be linked to the weakened monsoon gyre along with the southwestward retreat of monsoon trough under global warming. [ABSTRACT FROM AUTHOR]

In this study the simulated extreme and moderate El Niño events (e.g., frequency of occurrence and spatial variability) are characterized and the historical teleconnections on precipitation and temperature climate extremes in South America are analysed. Future projected changes in these teleconnections under a global warming context are also addressed. For this purpose a set of 10 Coupled Model Intercomparison Project Phase 5 (CMIP5) global climate models (GCMs) simulations derived from three experiments (historical, RCP4.5 and RCP8.5) are considered. The study period is 1916–2100. As El Niño is seasonally phase‐locked, peaking in late austral spring and summer, the present analysis is carried out for these two seasons, as well as annually. Results indicate that extreme El Niño events are projected to double their occurrence considering RCP4.5 and RCP8.5, while a less pronounced increase is projected for moderate El Niño events. Most of these events show a similar pattern of sea surface temperature (SST) anomalies: a positive centre in the central equatorial Pacific that extends eastward to the coast of America. In a global warming context, the resemblance between these events may decrease. Overall, the spatial structure of the impacts of El Niño are expected to be similar between moderate and extreme events, although in some cases (regions and seasons), a higher intensity is observed considering the events characterized as extreme El Niños rather than moderate ones. As extreme El Niño events are expected to be more frequent in the future, the impacts on climate extremes as well as the associated socioeconomic impacts would also take place much more frequently too. [ABSTRACT FROM AUTHOR]

This study investigates possible impacts of four global warming levels (GWLs: GWL1.5, GWL2.0, GWL2.5, and GWL3.0) on drought characteristics over Niger River basin (NRB) and Volta River basin (VRB). Two drought indices—Standardized Precipitation Index (SPI) and Standardized Precipitation‐Evapotranspiration Index (SPEI)—were employed in characterizing droughts in 20 multi‐model simulation outputs from the Coordinated Regional Climate Downscaling Experiment (CORDEX). The performance of the simulation in reproducing basic hydro‐climatological features and severe drought characteristics (i.e., magnitude and frequency) in the basins were evaluated. The projected changes in the future drought frequency were quantified and compared under the four GWLs for two climate forcing scenarios (RCP8.5 and RCP4.5). The regional climate model (RCM) ensemble gives a realistic simulation of historical hydro‐climatological variables needed to calculate the drought indices. With SPEI, the simulation ensemble projects an increase in the magnitude and frequency of severe droughts over both basins (NRB and VRB) at all GWLs, but the increase, which grows with the GWLs, is higher over NRB than over VRB. More than 75% of the simulations agree on the projected increase at GWL1.5 and all simulations agree on the increase at higher GWLs. With SPI, the projected changes in severe drought is weaker and the magnitude remains the same at all GWLs, suggesting that SPI projection may underestimate impacts of the GWLs on the intensity and severity of future drought. The results of this study have application in mitigating impact of global warming on future drought risk over the regional water systems. [ABSTRACT FROM AUTHOR]

Global warming acts to prolong thermal summers and shorten winters. In this work, future changes in the lengths and timing of four thermal seasons in northern Europe, with threshold temperatures 0 and 10°C, are derived from bias‐adjusted output data from 23 CMIP5 global climate models. Three future periods and two Representative Concentration Pathway (RCP) scenarios are discussed. The focus is on the period 2040–2069 under RCP4.5, which approximately corresponds to a 2°C global warming relative to the preindustrial era. By the period 2040–2069, the average length of the thermal summer increases by nearly 30 days relative to 1971–2000, and the thermal winter shortens by 30–60 days. The timing of the thermal springs advances while autumns delay. Within the model ensemble, there is a high linear correlation between the modelled annual‐mean temperature increase and shifts in the thermal seasons. Thermal summers lengthen by about 10 days and winters shorten by 10–24 days per 1°C of local warming. In the mid‐21st century, about two‐thirds of all summers (winters) are projected to be very long (very short) according to the baseline‐period standards, with an anomaly greater than 20 days relative to the late‐20th century temporal mean. The proportion of years without a thermal winter increases remarkably in the Baltic countries and southern Scandinavian peninsula. Implications of the changing thermal seasons on nature and human society are discussed in a literature review. [ABSTRACT FROM AUTHOR]

The Red Sea trough (RST) is a low‐pressure trough extending from south towards the Levant. Unlike previous synoptic classifications covering all systems that affect the region, our algorithm focuses on the RST alone. It uses sea‐level pressure (SLP) and relative geostrophic vorticity for identifying the existence of an RST and classifying it to one of three types, according to the location of the trough axis with respect to 35°E longitude. The following conditions were imposed to assure the existence of an RST: (a) north to south SLP drop across the Levant, (b) average positive sea‐level relative vorticity over the region of interest, (c) existence of a distinct and continuous trough axis from the south towards the region of interest and (d) absence of any pronounced closed cyclone near the Levant. The algorithm was applied on the NCEP/NCAR reanalysis, 2.5° × 2.5° resolution and the ERA‐Interim, 2.5° × 2.5° and 0.75° × 0.75° resolutions. An evaluation of the algorithm against subjective identification, based on the NCEP reanalysis, showed an agreement of 93% for RST identification and 79% for correct classification. The use of fine resolution data may insert noise that reduce the identification rate of RSTs but improves the axis locating. The autumn is the main season of RST, with a maximum in November and a consistent decrease towards the July minimum. The annual frequency varies among the data sources between 17.6 and 24.6%. The trough axis is shown to have a diurnal oscillation; towards the eastern coast of the Mediterranean at nighttime and eastward, inland, at noontime. No consistent long‐term trend was found for the period 1979–2016, during which the global warming was persistent. This automated algorithm is flexible in the sense that it is not confined to any predetermined spatial resolution and is applicable to operational forecast model as well as to climate model outputs. [ABSTRACT FROM AUTHOR]

Future climate change predictions by global climate models or earth system models diverge significantly, most likely due to their different cloud responses to global warming. There is an uncertainty as to how the cloud frequency (or cloud fraction) and height will change, in turn, affecting the sign, and amount of cloud feedbacks. While satellite observations have been very useful in augmenting information on clouds, it is mostly related to cloud tops, and there is a lack of information on cloud base height (CBH). In this study, a unique record of CBH information was collected at 706 Automated Surface Observing System (ASOS) ceilometer stations to evaluate the ability of the North American Regional Reanalysis (NARR) model to correctly simulate similar information. It was found that NARR can capture the geographical distribution and the seasonal variation of CBH and cloud base frequency (CBF). On average, the CBF values of NARR were 7% fewer and the CBH values of NARR were 631 m lower than those from observations that span the distance from the surface to 7,600 m. NARR simulates CBH better in arid area in the west of the contiguous United States (CONUS) than in humid areas, where NARR frequently predicted cloud bases too low compared with observations. In the west coast area of the CONUS, the discontinuity between high cloud bases over arid inland areas and low marine cloud layers from the Pacific Ocean over coastal areas produced especially large deviations between the NARR‐simulated and ASOS‐observed CBHs. [ABSTRACT FROM AUTHOR]

This research assesses the utility and validity of using simulated radar reflectivity to detect potential changes in linear and nonlinear mesoscale convective system (MCS) occurrence in the Midwest United States between the early and late 21st century using convection‐permitting climate simulation output. These data include a control run and a pseudo‐global warming (PGW) run that is based on RCP 8.5. First, using a novel segmentation, classification, and tracking procedure, MCS tracks are extracted from observed and simulated radar reflectivity. Next, a comparison between observed and the control run MCS statistics is performed, which finds a negative summertime bias that agrees with previous work. Using a convolutional neural network to perform probabilistic predictions, the MCS dataset is further stratified into highly organized, quasi‐linear convective systems (QLCSs)—which can include bow echoes, squall lines, and line echo wave patterns—and generally less‐organized, non‐QLCS events. The morphologically stratified data reveal that the negative MCS bias in this region is largely driven by too few QLCSs. Although comparisons between the control run and a PGW run suggest that all MCS events are less common in the future (including QLCS and non‐QLCS events), these changes are not spatially significant, whereas the biases between the control run and observations are spatially significant. A discussion on the importance and challenges of simulating QLCSs in convection‐permitting climate model runs is provided. Finally, potential avenues of exploration are suggested related to the aforementioned issues. [ABSTRACT FROM AUTHOR]

Simulations of the regional climate model REMO at 25 and 50 km (0.44° and 0.22°, respectively) grid spacing are evaluated over Central Africa for the period 1998-2010. The model's ability to simulate various aspects of the observed climate is assessed with a main focus on precipitation characteristics. The results show that the REMO model has a good skill to reproduce the main regional features of the mean climate fields as well as seasonal and annual cycles, although some biases still exist. The model captures the variability in precipitation anomalies between different events associated with El Niño/Southern Oscillation. Generally, the findings remain largely insensitive to the refinement of the horizontal resolution, especially when considering the regional scale. This points the fact that the model dynamical and physical setup may have to be adjusted when setting a focus on the Central African region, in order to better represent the climate mechanisms. It is further found that REMO is capable of capturing the basic features of climatic extremes and accurately represents the shape of the frequency distribution of daily precipitation data. Daily characteristics of precipitation are simulated much better by REMO at 25 km. [ABSTRACT FROM AUTHOR]

ABSTRACT We examine the ability of an ensemble of 10 Regional Climate Models ( RCMs), driven by ERA-Interim reanalysis, in skillfully reproducing key features of present-day precipitation and temperature (1990-2008) over West Africa. We explore a wide range of time scales spanning seasonal climatologies, annual cycles and interannual variability, and a number of spatial scales covering the Sahel, the Gulf of Guinea and the entire West Africa. We find that the RCMs show acceptable performance in simulating the spatial distribution of the main precipitation and temperature features. The occurrence of the West African Monsoon jump, the intensification and northward shift of the Saharan Heat Low ( SHL), during the course of the year, are shown to be realistic in most RCMs. They also capture the mean annual cycle of precipitation and temperature, including, single and double-peaked rainy seasons, in terms of timing and amplitude over the homogeneous sub-regions. However, we should emphasize that the RCMs exhibit some biases, which vary considerably in both magnitude and spatial extent from model to model. The interannual variability of seasonal anomalies is best reproduced in temperature rather than precipitation. The ensemble mean considerably improves the skill of most of the individual RCMs. This highlights the importance of performing multi-model assessment in properly estimating the response of the West African climate to global warming at seasonal, annual and interannual time scales. [ABSTRACT FROM AUTHOR]