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Aerosols significantly affect cloud microphysics and energy budget in different ways. The contribution of the direct, semi‐direct, and indirect effects of aerosols on radiation are here investigated over the North Atlantic tropical ocean under different aerosol loadings. The Weather Research and Forecasting Model is used to perform a set of numerical idealized experiments, which are forced with prescribed aerosol profiles. We evaluate the effects of aerosols on modeled shallow clouds and surface radiative budget. The results indicate that large aerosol loadings are associated with enhanced cloudiness and reduced precipitation. While the change in rainfall is mainly due to the larger number of smaller droplets, the change in cloudiness is attributed to the effects of absorbing aerosols, mainly dust particles, which are responsible for a rise of temperature that feeds back onto specific humidity. As in the boundary layer the increase of moisture dominates, the net effect is a higher relative humidity, which favors the formation of thin low non‐precipitating clouds. The feedback accounts for a dynamical change in the lower troposphere: shortwave radiation absorption increases temperature at the top of the marine atmospheric boundary‐layer and reduces entrainment of warm and dry air, increasing low level moisture content. Despite the overall increase in cloudiness, daytime cloud cover is reduced. The semi‐direct effect of aerosols on clouds results in a warming of the surface, opposite to the indirect effect. [ABSTRACT FROM AUTHOR]

A field campaign in a valley near Seefeld, Austria, well known for the frequent occurrence of cold‐air pools, was conducted to identify the processes leading to the formation and erosion of the cold‐air pool. Here, we focus on a case study in January 2020 that featured cold‐air pool formation interrupted by a wind disturbance. Simulations with the Weather Research and Forecasting model were performed at a horizontal grid spacing of 40 m and compared with measurement results. The model was able to reproduce the intense cooling in the beginning of the night and the cold‐air pool erosion in the middle of the night caused by the wind disturbance, but stronger winds than observed prevented the cold‐air pool from fully re‐establishing in the model after the disturbance. The dominant cooling processes were long‐wave radiative heat loss and turbulent exchange, both of which are parametrized and cool the air locally. Advection was the most important warming contribution during the cold‐air pool disturbances, especially its cross‐valley and vertical components. Owing to numerical constraints and the shallow nature of the cold‐air pool, its extent was limited to the lowest model level. Further improvements to the cold‐air pool's representation in the model would require a finer grid resolution. [ABSTRACT FROM AUTHOR]

Reliable precipitation simulation over the Tibetan Plateau (TP) remains a challenge, manifested by a prominent systematic wet bias in the warm season. Previous studies have generally neglected the potential linkage between surface radiation energy budget and precipitation bias. Prevalent scattered cumulus and thunderstorms over the TP in summer strongly influence surface radiation. A cloud fraction scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF model and tested for a month‐long simulation. It is found that the scheme better reproduces the surface solar radiation compared to a default cloud fraction scheme in the WRF model. Using abundant surface observations, we find that overestimation of the downward surface shortwave radiation (DSSR) would lead to wet bias. DSSR overestimation contributes to higher surface temperature and larger evaporation and enhanced atmospheric instability, which favor more simulated convective precipitation. The study suggests that a better simulation of clouds and surface radiation would benefit precipitation simulation over the plateau. Plain Language Summary: Summer precipitation over the Tibetan Plateau (TP) is generally overestimated in general circulation models as well as regional weather forecasts and climate models. The prevailing "popcorn‐like" clouds over the TP in summer would strongly impact surface radiation. Considering the potential linkage between surface radiation energy budget and precipitation bias, a statistical cloud fraction and subgrid condensation scheme (GS‐PDF scheme) is implemented in the WRF model and tested for a month‐long simulation. The GS‐PDF scheme better captures clouds over the TP with more mid‐low cloud water (ice) than the default Xu‐Randall scheme. Increased mid‐low cloud water in the GS‐PDF scheme reduced simulated downward surface shortwave radiation, reduced simulated surface temperatures, atmospheric instabilities and convective precipitation, leading to alleviated wet bias over the TP. This study suggests that better simulations of cloud and surface radiation would be beneficial for simulations of hydrological cycles over the TP. Key Points: A new cloud macrophysics scheme considering subgrid temperature and humidity fluctuations is implemented in the WRF modelThe new macrophysics scheme reduced simulated downward surface shortwave radiation, surface temperatures and atmospheric instabilitiesA better simulation of clouds and surface radiation would benefit precipitation simulation over the Tibetan Plateau [ABSTRACT FROM AUTHOR]

Climate change is expected to have far‐reaching effects at both the global and regional scale, but local effects are difficult to determine from coarse‐resolution climate studies. Dynamical downscaling can provide insight into future climate projections on local scales. Here, we present a new dynamically downscaled dataset for Indiana and the surrounding regions. Output from the Community Earth System Model (CESM) version 1 is downscaled using the Weather Research and Forecasting model (WRF). Simulations are run with a 24‐hr reinitialization strategy and a 12‐hr spin‐up window. WRF output is bias corrected to the National Centers for Environmental Protection/National Center for Atmospheric Research 40‐year Reanalysis project (NCEP) using a modified quantile mapping method. Bias‐corrected 2‐m air temperature and accumulated precipitation are the initial focus, with additional variables planned for future releases. Regional climate change signals agree well with larger global studies, and local fine‐scaled features are visible in the resulting dataset, such as urban heat islands, frontal passages, and orographic temperature gradients. This high‐resolution climate dataset could be used for down‐stream applications focused on impacts across the domain, such as urban planning, energy usage, water resources, agriculture and public health. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP. [ABSTRACT FROM AUTHOR]

Precipitation is a crucial influencing factor in the global hydrological cycle and terrestrial ecosystems. Previous studies have mostly focused on the historical precipitation variations over the Tibetan Plateau (TP). However, little attention has been given to the differences between convective and stratiform precipitation under future climate change scenarios. In order to acquire the high‐resolution precipitation information over the TP, the dynamical downscaling simulation was conducted utilizing a regional climate model (Weather Research and Forecasting [WRF] model) forced with a global climate model (Community Climate System Model [CCSM]). In this study, we focus on projections of future changes in convective and stratiform precipitation over the TP. The downscaling results during a 26‐year period 1980–2005 are compared for dry season, wet season and annual mean precipitation against the gridded observation dataset. Then, the changes in the wet season convective and stratiform precipitation with warming between CCSM and WRF‐CCSM were compared in the future period 2070–2099 under two scenarios (RCP4.5 and RCP8.5). Compared with the coarse‐resolution forcing, the historical precipitation of dry season, wet season and annual mean over the TP were found to better reproduce in WRF‐CCSM simulation. WRF‐CCSM projects an opposite spatial pattern of total precipitation change between the northern and southern TP in the future under different scenarios, which were attributed to the decreased stratiform precipitation in the southern TP and the increased convective precipitation in the northern TP, contrasting with the uniform increase of total precipitation in its forcing. The surface air temperature projected in WRF‐CCSM is larger increase than in the CCSM forcing. Compared to the CCSM forcing, WRF‐CCSM projects more pronounced increase in convective precipitation in response to rapid warming, and increasingly dominates changes of total precipitation with temperature exceeds the Clausius–Clapeyron rate over the northern TP. [ABSTRACT FROM AUTHOR]

In this study screen‐level analyses of air temperature and humidity are used to objectively evaluate the impact of a new land‐surface package being considered for implementation in Environment and Climate Change Canada (ECCC)'s medium‐range global deterministic numerical weather prediction (NWP) system. Through its control of heat, moisture, and momentum fluxes to the atmosphere, the land surface has a substantial impact on near‐surface meteorology and on the atmospheric boundary layer. The approach examined in this study is based on the comparison between model forecasts and screen‐level analyses. It demonstrates the impact on medium‐range NWP of a new land‐surface package that includes (i) a new set of databases to specify soils and land‐cover characteristics, (ii) improved land‐surface initial conditions obtained by the assimilation of space‐based remote‐sensing observations, and (iii) a more sophisticated scheme for land‐surface modelling. The evaluation method is shown to provide useful information on the impact of the new land‐surface package, including lead‐time‐averaged difference maps as well as plots showing the evolution with lead time of the standard deviation of errors (STDE) and of the temporal correlation between forecasts and analyses. The new land‐surface package has a positive impact on near‐surface forecasts of air temperature and humidity for a summertime period, with smaller STDE and larger temporal correlation for both variables. The improvement is greater for humidity than for air temperature. The maximum impact is found around seven‐day lead time, with substantial gains in absolute and relative values for STDE and temporal correlation. The positive impact is also quantified in terms of prediction hours, with gains of about one day at the medium range. Details of the pros and cons for this objective evaluation approach are discussed. [ABSTRACT FROM AUTHOR]

Summary Clusters of heavy showers and thunderstorms affected portions of East Anglia, central southern and southeast England on 25 July 2021, producing local flash flooding in places. A quasi-stationary multicell thunderstorm affected parts of mid-Suffolk on Sunday 25 July 2021. The first showers appeared as radar echoes around 1230 utc, and over the following hours (Figure 3) multiple clusters of thunderstorms developed along the CZ and drifted to the west-southwest before weakening, their westerly outflow winds at the surface helping to reinforce the existing CZ away to the east. Of particular interest was a quasi-stationary multicell thunderstorm that affected parts of mid-Suffolk, especially the village of Brettenham where 181.3mm of rain was recorded by a storage rain gauge that was formerly part of the Environment Agency registered network, most of which fell in less than 2 h. [Extracted from the article]

Data assimilation of satellite microwave measurements is one of the important keys to improving weather forecasting over the Arctic region. However, the use of surface‐sensitive microwave‐sounding channel measurements for data assimilation or retrieval has been limited, especially during winter, due to the poorly constrained sea ice emissivity. In this study, aiming at more use of those channel measurements in the data assimilation, we propose an explicit method for specifying the surface radiative boundary conditions (namely emissivity and emitting layer temperature of snow and ice). These were explicitly determined with a radiative transfer model for snow and ice and with snow/ice physical parameters (i.e. snow/ice depths and vertical distributions of temperature, density, salinity, and grain size) simulated from the thermodynamically driven snow/ice growth model. We conducted 1D‐Var experiments in order to examine whether this approach can help to use the surface‐sensitive microwave temperature channel measurements over the Arctic sea ice region for data assimilation. Results show that (1) the surface‐sensitive microwave channels can be used in the 1D‐Var retrieval, and (2) the specification of the radiative boundary condition at the surface using the snow/sea ice emission model can significantly improve the atmospheric temperature retrieval, especially in the lower troposphere (500 hPa to surface). The successful retrieval suggests that useful information can be extracted from surface‐sensitive microwave‐sounding channel radiances over sea ice surfaces through the explicit determination of snow/ice emissivity and emitting layer temperature. [ABSTRACT FROM AUTHOR]

The article provides several updates on weather-related events around the world. It mentions that the upcoming hurricane season in the Atlantic basin is expected to be active due to high sea temperatures and a transition from El Niño to La Niña. The article also discusses the impact of Arctic ice melt on global timekeeping and the need to remove a second from Coordinated Universal Time (UTC). Additionally, it mentions the depleted ice coverage on the Great Lakes and the consequences of reduced ice for the environment. The article concludes with information about a campaign called "Weather Kids" that aims to raise awareness about climate change and the migration of forecast models to a new supercomputer at the Danish Meteorological Institute. [Extracted from the article]

Extreme temperatures were considered natural hazards because they could increase morbidity and mortality. Understanding the extreme temperature changes at different warming levels is crucial to climate change mitigation and adoption for human health. This study projected climate change effects on the intensity, occurrence, and duration of extreme temperatures in Taiwan with 2 and 4°C global warming scenarios using the Weather Research and Forecasting model. The future climate simulations were conducted with the pseudo‐global warming approach, and the future climate changes were obtained from the ensemble mean of simulations in the Coupled Model Intercomparison Project 5. The simulated daily mean temperature increased by 1.40 and 3.09°C under 2 and 4°C global warmings. In a warming world, the daily maximum temperature was projected to increase by 1.35–3.00°C, whereas the daily minimum temperature was even higher, leading to weaker diurnal temperature variation in most regions. The simulation results show that intensified heatwaves with frequent and prolonged durations become par for the course, whereas extremely cold days disappear gradually. The occurrence of heatwaves in the future is projected to be five times that in the current climate. Comparing the global warming impacts over different land‐use types, the heatwave occurrence over urban areas rose more quickly than over other land‐use types; forests are less vulnerable to global warming. On the contrary, the changes in extremely cold days over urban areas were weaker than over other land‐use types. Overall, the effects of global warming on temperature revealed that extreme events were more severe with increased temperature than with the mean state of air temperature. Nonlinear behaviours indicated that global warming should be limited to 2°C, and the additional 2°C warming (from 2 to 4°C) should be addressed carefully. [ABSTRACT FROM AUTHOR]

Agricultural food production and natural ecological systems depend on a range of seasonal climate indicators that describe seasonal patterns in climatological conditions. This article proposes a probabilistic forecasting framework for predicting the end of the freeze‐free season, or the time to a mean daily near‐surface air temperature below 0°C (referred to here as hard freeze). The forecasting framework is based on the multimodel seasonal forecast ensemble provided by the Copernicus Climate Data Store and uses techniques from survival analysis for time‐to‐event data. The original mean daily temperature forecasts are statistically postprocessed and downscaled with a mean and variance correction of each model system before the time‐to‐event forecast is constructed. In a case study for a region in Fennoscandia covering Norway for the period 1993–2020, the proposed forecasts are found to outperform a climatology forecast from an observation‐based data product at locations where the average predicted time to hard freeze is less than 40 days after the initialization date of the forecast on October 1. [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 present study examines the temporal and spatial variability of near‐surface air temperature and the canopy layer urban heat island (UHI) in Singapore. Observations collected at 20 locations across the island city‐state and during 6 years make this one of the most extensive studies carried out in a tropical city. Local climate zones (LCZs), defined as urban built and rural land cover types which produce a unique air temperature response, are used to standardize intersite comparison. The results show that the choice of the rural reference can affect the night‐time UHI magnitude by up to 2°C under "ideal" (dry, clear, calm) conditions. The most frequently observed median UHI magnitude increases from 2.8 during all‐weather to 3.7°C during "ideal" conditions, respectively. A seasonality is present with lowest (highest) mean all‐weather values of ~2.0°C (~3.3°C), observed during the wet (dry) December–January (April–October) period. Mean night‐time UHI intensity across seven built‐type LCZs ranges between 1.8 and 3.5°C (2.5 and 4.3°C) for all‐weather ("ideal") conditions. Corresponding daily values are 1.1–2.3°C (1.5–2.7°C). The lowest (highest) night‐time magnitudes are associated with open low‐rise LCZ 6 (compact high‐rise LCZ 1) built type. In the middle of the day LCZ 1 can experience a cool island effect. The average UHI values presented here give an indication of the extra warmth experienced in the built‐up spaces of Singapore relative to one possible rural reference land cover type (scattered trees LCZ B). Highest daytime heat exposure is observed in LCZs 3 and 8 which are characterized by low building heights, high impervious surface fraction and lack of vegetation. The present results can be used to support the development and evaluation of urban climate models, develop urban planning policies and improve local weather forecasts and the delivery of integrated urban services. [ABSTRACT FROM AUTHOR]

In this study, a new lightning data assimilation (LDA) scheme using Geostationary Lightning Mapper (GLM) flash extent density (FED) is developed and implemented in the National Severe Storms Laboratory Warn-on-Forecast System (WoFS). The new LDA scheme first assigns a pseudo relative humidity between the cloud base and a specific layer based on the FED value. Then at each model layer, the pseudo relative humidity is converted to pseudo dewpoint temperature according to the corresponding air temperature. Some sensitivity experiments are performed to investigate how to assign and use GLM/FED in an optimum way. The impact of assimilating this pseudo dewpoint temperature on a short-term severe weather forecast is preliminarily assessed in this proof-of-concept study. A high-impact weather event in Kansas on 24 May 2021 is used to evaluate the performance of the new scheme on analyses and subsequent short-term forecasts. The results show that the assimilation of additional FED-based dewpoint temperature observations along with radar, satellite radiance, and cloud water can improve short-term (3-hr) forecast skill in terms of quantitative and qualitative verifications against the observations. The improvement is primarily due to the direct and indirect adjustment of dynamic and thermodynamic conditions through the LDA process. More specifically, the assimilation of FED-based dewpoint temperature, in addition to the other observations currently used in WoFS, tends to enhance the ingredients required for thunderstorm formation, namely moisture, instability, and lifting mechanism. [ABSTRACT FROM AUTHOR]

Previous studies indicate that convective heating variability of the western North Pacific summer monsoon (WNPSM) influences strongly weather and climate over East Asia. Based on daily reanalysis data and interpolated outgoing longwave radiation (OLR) data, this study demonstrates that the WNPSM convection can also cause severe weather events remotely over the Antarctic through the exciting of the Australia‐South Pacific‐Atlantic wave train (ASPA pattern). Surface air temperature (SAT) rises over the Ross Sea‐Mawson‐Dumont d'Urville Seas sector and over the Weddell Sea, while the SAT drops over the Amundsen–Bellingshausen Seas. Concurrently, sea ice concentration (SIC) is reduced over the Ross Sea and enhanced over the Amundsen–Bellingshausen Seas. The result suggests that the newly found ASPA pattern may serve as an important bridge linking the WNPSM convection and the weather over the Antarctic region. The dynamics of ASPA's formation and propagation are also investigated comprehensively. Day‐to‐day energy budget analysis suggests that after its initiation by WNPSM convection, the ASPA pattern is driven by the baroclinic energy conversion from the climatological flow and nonlinear term. The barotropic energy conversion from the climatological flow contributes to positive KE tendency before day +1 and negative KE tendency after day +1. It is therefore extremely important to improve the representations of the climatological‐mean sea ice and jet stream, wave‐mean flow interaction and wave‐wave interaction in the mid‐ and high‐latitudes of the Southern Hemisphere, as well as the convection over the WNPSM region of the Northern Hemisphere in numerical model for a better weather prediction for the Antarctic. [ABSTRACT FROM AUTHOR]

In June of 2021 the Southwest United States experienced a record‐breaking heatwave. This heatwave came at a time when the region was in severe drought. As drought alters the surface energy budget in ways that affect lower atmosphere temperature and circulations, it is possible that the combined drought‐heat event was a cascading climate hazard, in which preexisting drought exacerbated the heatwave. We apply satellite observation and numerical experiments with the Weather Research and Forecasting (WRF) model to test for land‐atmosphere feedbacks during the heatwave consistent with drought influence. We find a modest positive drought‐heat effect, as WRF simulations that include the drought have marginally higher air temperatures than those that exclude the initial drought conditions, with more substantial effects in wetter, forested areas. Evidence of drought‐heat‐drought‐coupled feedbacks was similarly modest in our simulations, as accounting for drought preconditioning led to a small reduction in simulated precipitation in the region. Plain Language Summary: In June 2021 the Southwest United States was hit by a record‐breaking heatwave at the same time as a severe drought. In this study, we look into the response of both events effect on one another. The WRF computer model is designed to test the observed events besides the use of satellite images during the same time. We find a small positive response between the two events that was more considerable over wetter and green regions. We also find a decrease in rainfall over the study region as a response to the drought‐heatwave conditions. Key Points: The record‐setting 2021 US heatwave struck at a time when surface conditions were anomalously dry, bright, and barren due to droughtSatellite observations indicate that drought led to anomalous land‐atmosphere fluxesModel simulations also indicate a small but systematic positive feedback of drought on heat [ABSTRACT FROM AUTHOR]

Forecasts from NOAA's Global Forecast System (GFS) and the High‐Resolution Rapid Refresh (HRRR) weather models are matched to surface observations for the winter season of November 2019 to March 2020 at 210 airports across the United States. The 2‐m temperature errors, conditioned on observed weather conditions such as cloud cover amount and wind speed, are used to determine the nature of systematic model biases. We observe a strong diurnal cycle in 2‐m temperature errors in the GFS in conditions with ≤50% and ≤25% sky cover, with a 1°C warm bias at night and a 2°C cold bias during the day. The HRRR, which uses a different set of physical parameterizations, does not have a clear diurnal cycle in errors under the same conditions. These results highlight the utility of weather‐conditional comparisons across the diurnal cycle to diagnose sources of model weaknesses and to target model improvements. Plain Language Summary: We evaluate the output from weather forecast models compared to observations at 210 airports across the United States during the November 2019 to March 2020 winter season. We focus on near‐surface air temperature errors in the Global Forecast System (GFS) and High‐Resolution Rapid Refresh (HRRR) weather models for different times of day and subsets of observed weather conditions. The GFS is 1°C too warm at night and 2°C too cold during the day in conditions with ≤50% and ≤25% cloud cover. The daily high and low temperatures have smaller errors in the HRRR model, which has different algorithms than the GFS model. Model refinement and development efforts would benefit from a focus on accurate representation of the diurnal cycle of temperatures as this basic characteristic of weather can reveal strengths and weaknesses in the model physics. Key Points: National Oceanic and Atmospheric Administration (NOAA)'s global forecast system (GFS) model struggles to adequately represent the diurnal cycle of temperatures under observed conditions of ≤50% and 25% cloud coverNOAA's high‐resolution rapid refresh (HRRR) model uses a different physics suite and does not have a strong diurnal cycle of temperature errors under the same conditionsExamination of errors using similar weather conditions helps to constrain the portion of model physics that can yield larger forecast errors [ABSTRACT FROM AUTHOR]

To assess the potential change in agroclimatic indices in western Canada, this study used a convection-permitting Weather Research Forecasting (WRF) model to conduct simulations for the current climate (CTL, 2000-2015) and future climate under the RCP8.5 scenario based on a pseudo-global-warming (PGW) approach. Both CTL and PGW simulations were bias-corrected to the GEM-CaPA dataset using a multivariate quantile mapping method. An evaluation of the CTL simulation of daily maximum and minimum temperatures and precipitation during the growing season against the gridded observations has been performed, indicating good agreements in the spatial patterns of air temperature and precipitation in western Canada. The PGW-CTL differences in several selected agroclimatic indices were then examined. Due to rising temperatures, substantial increases in growing degree-days (GDD) by 800-1,200◦ days and reductions in frost days by 10 to 20 days, favouring regional crop production, are found in southern Alberta and Saskatchewan. However, globalwarming also poses great risks to Canadian agriculture by modifying heat accumulations and water availability during the growing season. Plant heat stress will substantially increase by ∼50◦ days in southern Alberta and Saskatchewan, offsetting the positive effects caused by the reduction in frost days and increase in GDD. The southern Canadian Prairies will experience statistically significant increases in the number of dry days and precipitation deficit, suggesting an exacerbation of water stress on the Canadian Prairies by global warming. [ABSTRACT FROM AUTHOR]

As one of the effective ways to study urban climate and environmental problems, the performance of numerical model could be improved in urban areas by enhancing the description of urban morphology. One of the largest challenges faced by researchers is the lack of real urban canopy parameters (UCPs) to represent urban morphological characteristics. In this study, the urban morphological parameters were estimated and a high‐resolution urban morphological parameter data set for the main cities in China was developed. The results show that the distribution of urban morphological parameters is of regional characteristics and cities in China are mainly dominated by low‐rise buildings. Chinese cities can be broadly divided into two types based on the probability distribution of building height: single‐peak and double‐peak. The number of low‐rise buildings in single‐peak cities is much larger than that of double‐peak cities, which contributes to the difference between the two types of cities. The equivalent street width of northern cities in China is higher than that of southern cities, and the equivalent building width is almost the same. However, the default UCPs of the Weather Research and Forecasting (WRF) model distort the morphology of Chinese cities. The sensitivity of the WRF model to urban morphological parameters was also investigated in Nanjing. Overall, our results indicate that urban morphological parameters have a significant influence on the energy balance process in cities and improve the performance of the WRF model in simulating the air temperature and canopy wind speed. Plain Language Summary: The urban morphological parameters are important for simulation in urban areas using numerical models. In this study, we develop a data set of the urban morphological parameters for the main cities in China. The regional characteristics of urban morphological parameters in China are summarized, and we find that Chinese cities can be divided into two types: single‐peak cities and double‐peak cities. The sensitivity of the urban canopy parameterization schemes of Weather Research and Forecasting (WRF) to the new data set is also investigated. The urban morphological parameters can significantly improve the performance of WRF in simulations of wind speed and temperature in urban areas compared with the default values in WRF. Key Points: A data set of the urban morphological parameters for the main cities in China is developedThe regional characteristics of urban morphological parameters in China are summarizedThe urban morphological parameters can improve the performance of Weather Research and Forecasting in simulations in urban areas compared with the default values [ABSTRACT FROM AUTHOR]

Urban development exacerbates urban heat island effects of cities and thermal discomfort of the residents worldwide. However, such urban effects vary with the geographical and climatic conditions of the respective cities. This study is the first to quantitatively estimate the impact of past (1878), present (2012), and future (2050) urbanization on surface air temperatures and human thermal comfort in Sofia, the largest city of Bulgaria. We used the state-of-the-art Weather Research and Forecasting (WRF) climate model, with a 1-km horizontal resolution for three cases: LU1878, LU2012, and LU2050. We first validated the results of the control simulation (case LU2012) against actual observations. The model satisfactorily reproduced the diurnal variation of the observed monthly mean surface air temperatures for July (2011-2013) with a mean bias of ±0.6°C. We then estimated the potential impacts of past and future urbanization on surface temperature. Our results suggest that because of urbanization of Sofia, the surface air temperature at 0600 Local Standard Time (LST) has increased approximately 4.0°C in the past (1878-2012) and approximately 0.4°C in future (2012-2050). We also evaluated the impact of the urban thermal environment on human comfort levels. Considering the LU2050 case in the central part of Sofia, the temperature-humidity index during mid-afternoon (1400-1700 LST) is estimated to be 24-26, indicating that approximately 50% of the residents could feel perceptibly uncomfortable. [ABSTRACT FROM AUTHOR]

Forecasts with the European Centre for Medium-Range Weather Forecasts’ numerical weather prediction model are evaluated using an extensive set of observations from the Arctic Ocean 2018 expedition on the Swedish icebreaker Oden. The atmospheric model (Cy45r1) is similar to that used for the ERA5 reanalysis (Cy41r2). The evaluation covers 1 month,with the icebreaker moored to drifting sea ice near the North Pole; a total of 125 forecasts issued four times per day were used. Standard surface observations and 6-hourly soundings were assimilated to ensure that the initial model error is small. Model errors can be divided into two groups. First, variables related to dynamics feature errors that grow with forecast length; error spread also grows with time. Initial errors are small, facilitating a robust evaluation of the second group; thermodynamic variables. These feature fast error growth for 6–12 hr, after which errors saturates; error spread is roughly constant. Both surface and near-surface air temperatures are too warm in the model. During the summer both are typically above zero in spite of the ongoing melt; however, the warm bias increases as the surface freezes. The warm bias is due to a too warm atmosphere; errors in surface sensible heat flux transfer additional heat from the atmosphere to the surface. The lower troposphere temperature error has a distinct vertical structure: a substantial warm bias in the lowest few 100m and a large cold bias around 1 km; this structure features a significant diurnal cycle and is tightly coupled to errors in themodelled clouds. Clouds appear too often and in a too deep layer of the lower atmosphere; the lowest clouds essentially never break up. The largest error in cloud presence is aligned with the largest cold bias at around 1 km. [ABSTRACT FROM AUTHOR]

A summertime convective event that developed on 5 September 2017 over the United Arab Emirates (UAE) is investigated in this study. Atmospheric profiles from a ground‐based microwave radiometer along with satellite observations and in situ data from three weather stations in the UAE were used. The event was simulated using the Weather Research and Forecasting (WRF) model, forced with four input datasets: Global Forecast System (GFS), Climate Forecast System Reanalysis (CFSR), and the European Centre for Medium‐Range Weather Forecasts ERA‐5 and ERA‐Interim reanalyses. The afternoon and evening convection was triggered by the intrusion of a mid‐level trough from midlatitudes and was favoured by the convergence over land of thermally forced maritime air masses. Near‐surface observations at a weather station revealed a 7 °C drop in air temperature, a doubling of the wind speed to 9 m·s−1 in 30 min, and a shift in wind direction from easterly, to southerly and then westerly in about 45 min, associated with the passage of the cold pool emanating from a Mesoscale Convective System (MCS). At the location where the microwave radiometer was deployed, the pre‐squall low and wake low signatures are also captured, with a 5 m·s−1 increase in the wind speed in just 5 min. The observed features of the studied MCS were found to compare with those reported for MCSs in the Tropics. The four experiments gave a similar performance, although the GFS simulation generally generated higher skill scores. The investigated MCS event was not captured by WRF, which was attributed to a misrepresentation of soil moisture in the model. This study highlights the difficulties regional models like WRF may have in reproducing MCSs over arid/hyperarid regions, which may result in a misrepresentation of their impacts in climate projection studies. [ABSTRACT FROM AUTHOR]

A World Meteorological Organization (WMO) Extremes Evaluation Committee investigated an observation of −69.6 °C by Klinck Automatic Weather Station (AWS) in Greenland on 22 December 1991 as the lowest temperature observed in Greenland, thereby making it the lowest recorded near‐surface air temperature for the Northern and Western Hemispheres and for WMO Region VI. The committee examined the metadata and observations of the station as well as the regional synoptic circulation. The committee concluded that the observation is credible in terms of instrument calibration, monitoring of the station and the synoptic situation. Consequently, the WMO Rapporteur accepted the observation as the officially lowest observed near‐surface air temperature for Greenland, the Northern and Western Hemisphere and for WMO Region VI. As a supplement to this investigation, the committee also recommends that opportunities be investigated such that AWS data from Greenland can be efficiently incorporated into real‐time weather forecasts and hence into reanalysis datasets. [ABSTRACT FROM AUTHOR]

The Weather Research and Forecasting model (WRF) comprises a number of parameterization schemes, and therefore the different combined physical parameterization schemes should be discussed before determining optimal configurations. The regional climate of central Asia is simulated from 2003 to 2008 using the WRFv4.0.1 with nine physical combinations (microphysics [MP], cumulus [CU], planetary boundary layer [PBL]) on a 25‐km horizontal grid. The goal is to identify a reasonable configuration assemble of physical parameterization schemes for long‐term simulations through the sensitivity experiments of regional climate respond to microphysics, convection, and boundary layer processes. Compared to the observation data, all the groups mostly simulate the daily and seasonal variations of precipitation but with a shift from wet bias to dry bias based on the daily precipitation categories of the Tropical Rainfall Measuring Mission (TRMM). The large‐scale precipitation caused by the microphysics schemes is 3–4 times the amount of precipitation caused by the subscale precipitation generated by the cumulus schemes. The simulated difference of the vertical temperature shows results similar to those of the surface air temperature. The microphysics schemes play the dominant role among the three physical options, and the Thompson scheme addresses better precipitation and surface temperature simulations in contrast to the WDM6 scheme. By combining with the bias, correlation and root‐mean‐square error (RMSE) between the reproduced temperature and precipitation, it can be concluded that the best optimal physical schemes combination is the Thompson–Tiedtke–YSU, which can be used for long‐term simulations. [ABSTRACT FROM AUTHOR]

Ensemble weather forecasting generally suffers from bias and under-dispersion, which limit its predictive power. Several post-processing methods have been developed to overcome these limitations, and an intercomparison is needed to understand their performance. Four state-of-the-art methods are compared in post-processing precipitation and air temperature of the Global Ensemble Forecasting System (GEFS) reforecasts using a simple bias correction (BC) method as a reference. These methods include extended logistic regression (ExLR), generator-based post-processing (GPP), Bayesian model averaging (BMA) and affine kernel dressing (AKD). All these methods are tested over 659 national standard meteorological stations in China. The research concerns are the influence of region and forecast date and the role of BC on ensemble weather forecasting. It was found that: (1) the deterministic methods (GPP and ExLR) are more skilful than the probabilistic methods (BMA and AKD) in obtaining the well-calibrated and skilful ensemble forecasts; (2) the forecast skill of the postprocessed ensemble weather forecasts is comparably high in the northern arid areas for precipitation, while the forecast skill for air temperature is only low in the Qinghai-Tibetan Plateau area; (3) the skill difference of the post-processed forecasts on different forecast date is only evident for air temperature, while not apparent for precipitation; and (4) only correcting bias for the ensemble weather forecasts can achieve about 0-70% (for precipitation) and 30-100% (for air temperature) forecast skill improvement for deterministic methods. [ABSTRACT FROM AUTHOR]

Derived from realistic global warming scenarios, long‐term projections of spatial patterns in precipitation and temperature in hydrology and climatology can serve to evaluate climate risk, explore sources of renewable energies and allow local‐scale data to inform decisions regarding agricultural, ecosystem, social, recreational and economic activities. Under the CORDEX‐EA project, the precipitation and temperature projections (2020–2045) for the economically and socially important region of Northwestern China were derived from high‐resolution regional climate model (RCM) simulations for RCP 4.5 and 8.5 scenarios, and compared against a historical period or baseline of 1980–2005. Drawing data from four key RCMs [Weather Research and Forecasting (WRF); Regional Spectral Model (RSM); Regional Climate Model version 4.0 (RegCM4); Mesoscale Model version 5 (MM5)], reliable local‐scale projections were generated by applying suitable bias correction that accords with the multivariate bias correction (MBC) approach. To validate this approach and then evaluate climate change impacts, the adjusted precipitation and temperature estimated from bias‐corrected models for the historical period were compared to the observed data. The results showed that the simulated spatiotemporal distribution of multiyear average precipitation and temperature appear to fit relatively well with the observations, however, the wet‐cold and dry‐warm climate‐related biases were still evident for the high altitude regions. Bias‐corrected future projections of RCMs indicated spatially averaged annual precipitation is expected to rise by about 23.6 and 35.3 mm under the RCP 4.5 and 8.5 scenarios, respectively, while spatially averaged annual temperature is expected to rise by about 1.95 and 1.10°C. Precipitation is projected to increase in all seasons, albeit, more so in the cold season (i.e., boreal winter and spring) than the warm season (i.e., boreal summer and autumn). The annual increase is expected to be about 56.7% under the RCP 4.5 scenario, compared to 67.6% under the RCP 8.5 scenario. The changes of mean temperature in winter are expected to be significant, −37.8% under both RCP scenarios. For spring, the mean temperature will rise by 23.1% (28.9%) under the RCP 4.5 (8.5) scenario. A MBC approach was found to be effective in yielding reliable projected changes in precipitation and temperature variables. The proposed approach has important implications for climatological studies and evaluation of climate change impacts on localized regions, not only in China, but also in other similar areas of the world, where decisions for managing climate risk must be implemented by policy makers, government, industry and stakeholders. [ABSTRACT FROM AUTHOR]

Forecasts of maximum and minimum air temperatures are essential to mitigate the damage of extreme weather events such as heat waves and tropical nights. The Numerical Weather Prediction (NWP) model has been widely used for forecasting air temperature, but generally it has a systematic bias due to its coarse grid resolution and lack of parametrizations. This study used random forest (RF), support vector regression (SVR), artificial neural network (ANN) and a multi‐model ensemble (MME) to correct the Local Data Assimilation and Prediction System (LDAPS; a local NWP model over Korea) model outputs of next‐day maximum and minimum air temperatures (Tmaxt+1 and Tmint+1) in Seoul, South Korea. A total of 14 LDAPS model forecast data, the daily maximum and minimum air temperatures of in‐situ observations, and five auxiliary data were used as input variables. The results showed that the LDAPS model had an R2 of 0.69, a bias of −0.85 °C and an RMSE of 2.08 °C for Tmaxt+1 forecast, whereas the proposed models resulted in the improvement with R2 from 0.75 to 0.78, bias from −0.16 to −0.07 °C and RMSE from 1.55 to 1.66 °C by hindcast validation. For forecasting Tmint+1, the LDAPS model had an R2 of 0.77, a bias of 0.51 °C and an RMSE of 1.43 °C by hindcast, while the bias correction models showed R2 values ranging from 0.86 to 0.87, biases from −0.03 to 0.03 °C, and RMSEs from 0.98 to 1.02 °C. The MME model had better generalization performance than the three single machine learning models by hindcast validation and leave‐one‐station‐out cross‐validation. Key Points: Machine learning based bias correction of air temperature forecasts of a numerical modelAll machine learning models improved prediction skills of air temperatureAn ensemble of three machine learning models resulted in more robust bias correction than individual machine learning models [ABSTRACT FROM AUTHOR]

The impact of local climate conditions on air temperatures during a hot summer period over the United Kingdom in 2018 is studied using simple sensitivity experiments with a state‐of‐the‐art regional numerical weather prediction system. The experiments are designed to investigate the influence of sea surface temperature (SST) and soil moisture on the air temperatures over land. They involved applying changes to the analysed SST and soil moisture patterns with magnitudes consistent with the differences between forecast analysis and climatology. The results from daily 5‐day forecasts over an 11‐day period show that a 3°C reduction in SSTs reduces the simulated air temperatures averaged over all land points by just over 1°C. Moistening the soil while using the control SSTs reduces the temperatures by a little less than 1°C on average although this can be larger than 3°C locally. While the reduction in SST impacts the daily maximum and minimum temperatures to a similar extent, the increase in soil moisture had little impact on the daily minimum temperatures. [ABSTRACT FROM AUTHOR]

The application of a novel method of adaptive neuro‐fuzzy inference system (ANFIS) for the prediction of air temperature is investigated. The paper discusses the improvement of the ANFIS when used with genetic algorithm (GA), particle swarm optimization (PSO), ant colony optimization for continuous domains (ACOR) and differential evolution (DE). For this purpose, three input of multiple variables are selected in order to predict monthly minimum, average and maximum air temperatures for 34 meteorological stations in Iran. The co‐efficient of determination (R2), root mean square error (RMSE), mean absolute error (MAE) and Nash–Sutcliffe efficiency (NSE) are used as evaluation criteria. A comparison of suggested fuzzy models indicates that the ANFIS with the GA has the best performance in the prediction of maximum temperatures. It decreases the RMSE of the classic ANFIS model in the validation stage from 1.22 to 1.12°C for Mashhad, from 1.26 to 1.01°C for Zahedan, from 1.20 to 0.98°C for Ahvaz, from 1.76 to 1.24°C for Rasht and from 1.21 to 0.95°C for Tabriz. [ABSTRACT FROM AUTHOR]

Citizen‐science thermometer measurements have the potential to provide information about surface air temperature fields on scales smaller than is typically quantified by the official monitoring network. As such, national meteorological services are becoming increasingly interested in these measurements as a possible source of data for use in weather monitoring or forecasting. However, in order for the information to be used, biases in the data need to be assessed. The most important source of bias is the potential overheating of the thermometer due to inadequate shielding or exposure. Previous research has indicated that information about the nature of the instrument and its exposure is important for correcting this bias. However, in the majority of cases this information is unavailable for amateur stations. In this paper a statistical correction for short wave radiation bias is developed for the air temperature data recorded at 159 Weather Observations Website (WOW) stations across the Netherlands during the period 2015–2016. Generalized additive mixed modelling (GAMM) is used to quantify and correct for short wave radiation bias in the hourly measurements using a background temperature field generated from the official 34 automatic weather stations along with satellite‐derived short wave radiation estimates. It is demonstrated that the corrected WOW data add local detail to the hourly temperature field, which may provide a useful source of data to supplement official measurements. [ABSTRACT FROM AUTHOR]

We present the new Atmospheric Raman Temperature and Humidity Sounder (ARTHUS). We demonstrate that ARTHUS measurements resolve (1) the strength of the inversion layer at the planetary boundary layer top, (2) elevated lids in the free troposphere during daytime and nighttime, and (3) turbulent fluctuations in water vapor and temperature, simultaneously, also during daytime. Very stable and reliable performance was demonstrably achieved during more than 2,500 hr of operations time experiencing a huge variety of weather conditions. ARTHUS provides temperature profiles with resolutions of 10–60 s and 7.5–100 m vertically in the lower free troposphere. During daytime, the statistical uncertainty of the water vapor mixing ratio is <2 % in the lower troposphere for resolutions of 5 min and 100 m. Temperature statistical uncertainty is <0.5 K even up to the middle troposphere. ARTHUS fulfills the stringent WMO breakthrough requirements on nowcasting and very short range forecasting. Plain Language Summary: The observation of atmospheric moisture and temperature profiles is essential for the understanding and prediction of earth system processes. These are fundamental components of the global and regional energy and water cycles; they determine the radiative transfer through the atmosphere and are critical for the cloud formation and precipitation. Also, it is expected that the assimilation of high‐quality, lower tropospheric WV and T profiles will result in a considerable improvement of the skill of weather forecast models particularly with respect to extreme events. Here we present the Atmospheric Raman Temperature and Humidity Sounder, an exceptional tool for observations in the atmospheric boundary layer during daytime and nighttime with a very short latency. This performance serves very well the next generation of very fast rapid‐update‐cycle data assimilation systems for nowcasting and short‐range weather forecasting. Ground‐based stations and networks can be set up or extended for climate monitoring, verification of weather, climate and earth system models, and data assimilation for improving weather forecasts. Key Points: Fulfills World Meteorological Organization breakthrough requirements for nowcasting/very short range forecasting in the lower troposphereResolves strength of the inversion layer at the planetary boundary layer top and elevated lids above during daytime and nighttimeProvides statistics on turbulent fluctuations in water vapor and temperature simultaneously in the lower troposphere [ABSTRACT FROM AUTHOR]

The data submitted by our VCN users include some high-profile weather events, such as the highest UK maximum temperature on record of 38.7°C at Cambridge, Botanic Garden on 25 July 2019 (as shown in Figure 2). Nowcasting WOW is also being used to combine crowdsourced data with observations from the Met Office's official network of automatic surface stations, the Meteorological Monitoring System (MMS). WOW is therefore able to provide a more detailed picture of the current weather situation than is possible when using data from the MMS network alone. [Extracted from the article]

Estimating cloud motion with complex background through the sequence of satellite images plays an important role in weather forecasting. This motion can be used for characterization of clouds and predicting storms. Optical flow is used here for motion estimation which gives horizontal and vertical velocity components. Velocity field vector alone is not sufficient to analyze the cloud behavior for predicting extreme weather conditions and there is a need to develop some visual features for enhancing the weather prediction strategies. In this paper, we utilize the optical flow to localize the high alert regions. To have a better localization and motion signatures, we develop a fractional order technique to compute optical flow. Also, the localization is characterized by brightness of image, magnitude, directions, vorticity and irrotational components of the optical flow. We did analysis on sequence of images for Mumbai, India heavy rain that happened during August 28–29, 2017, cyclonic data sets for May 16, 2018, September 19–20, 2018 and October 14, 2018. Visual features show different patterns for extreme and normal weather situations. A study on interpolation and extrapolation of the image sequence is also presented. Optical flow based interpolation and Advection‐anisotropic‐diffusion based extrapolation model give promising results. [ABSTRACT FROM AUTHOR]

A large data set from 40 weather stations in Iceland is explored for persistence in monthly mean temperatures. There are great seasonal and regional variations in the persistence. Extremely high values of correlation (r > 0.8) of temperatures with subsequent months are found. These values are higher than reported elsewhere in the scientific literature. The highest values are found in coastal regions in the summer, while in the early winter there is overall little correlation. In general, there are two distinct maxima in the temperature correlations, one in late winter/early spring and one in the summer. In most seasons, there is greater correlation at the coast than inland. The high correlations are linked to snow melt, persistence in sea surface temperatures, weak winds and strong static stability. Remarkably low correlations reveal a negative feedback process: A warm May leads to less snow in inland regions, which favours a cold sea breeze in coastal regions in June. In regions of high correlation, the persistence is indeed useful for subseasonal forecasting of mean temperatures in late winter/spring and summer. Persistence in mean monthly temperatures is explored, revealing great seasonal and regional variabilities in the persistence. The correlation of temperatures of subsequent months ranges from being negative to extremely high (r > 0.8), being useful for regional subseasonal forecasting. The greatest correlation is in late winter and summer and there is greater correlation at the coast than inland. The high correlations are linked to snow, sea surface temperatures, winds and static stability. The study reveals an interesting feedback by thermally driven mesoscale winds. Correlation of monthly mean temperatures for six weather stations (coloured) and the mean of all 40 weather stations (black dashed). [ABSTRACT FROM AUTHOR]

Accurate forecasts of the ocean state and the estimation of forecast uncertainties are crucial when it comes to providing skilful seasonal predictions. In this study we analyse the predictive skill and reliability of the ocean component in a seasonal forecasting system. Furthermore, we assess the effects of accounting for model and observational uncertainties. Ensemble forcasts are carried out with an updated version of the ECMWF seasonal forecasting model System 4, with a forecast length of ten months, initialized every May between 1981 and 2010. We find that, for essential quantities such as sea surface temperature and upper ocean 300 m heat content, the ocean forecasts are generally underdispersive and skilful beyond the first month mainly in the Tropics and parts of the North Atlantic. The reference reanalysis used for the forecast evaluation considerably affects diagnostics of forecast skill and reliability, throughout the entire ten‐month forecasts but mostly during the first three months. Accounting for parametrization uncertainty by implementing stochastic parametrization perturbations has a positive impact on both reliability (from month 3 onwards) as well as forecast skill (from month 8 onwards). Skill improvements extend also to atmospheric variables such as 2 m temperature, mostly in the extratropical Pacific but also over the midlatitudes of the Americas. Hence, while model uncertainty impacts the skill of seasonal forecasts, observational uncertainty impacts our assessment of that skill. Future ocean model development should therefore aim not only to reduce model errors but to simultaneously assess and estimate uncertainties. Accounting for model uncertainty in ten‐month ocean forecasts with a seasonal ensemble forecasting system leads to improved forecast skill for oceanic and atmospheric variables. (a) shows the reference forecast mean squared skill score (REF) for 2 m temperature over the Pacific for the last three months of the forecast, while (b) shows the skill for the set‐up with stochastic ocean parametrizations (STO) that account for model uncertainty, and (c) the difference STO–REF. Skill is significantly improved over the North Pacific, North America and the southeastern subtropical Pacific. [ABSTRACT FROM AUTHOR]

A systematic warm bias in the tropical tropopause layer (TTL) is commonly found in both climate and numerical weather prediction models. In this study, the nature of this temperature bias is examined by integrating the MetOffice Unified Model (MetUM) with various ozone concentrations in the TTL. Like other models, the long‐term integration of MetUM with the Atmospheric Model Intercomparison Project (AMIP) configuration shows a notable warm bias (∼2 K) in the TTL with a comparable cold bias in the tropical stratosphere above ∼70 hPa. We demonstrate that these biases are particularly sensitive to the tropical ozone concentration prescribed in the model. By replacing the background ozone, which is typically used for AMIP‐type simulations, with the Southern Hemisphere ADditional OZonesondes (SHADOZ) measurements or the Binary Data Base of Profiles (BDBP), the dipolar temperature biases in the TTL and tropical stratosphere are significantly reduced. Further sensitivity tests show that the tropical ozone amount in a 14–20 km layer is a key contributor to this change. These results suggest that accurate ozone forcing in the TTL is crucial for reliable weather and climate simulations. Latitude–pressure distributions of zonal‐mean temperature bias for the 20‐year (1989–2008) AMIP simulations: (a) CTR, (b) SOZ, (c) BDBP, (d) SOZ_TTL and (e) BDBP_TTL. The biases are computed from ERAI temperatures for the same period. Shading and contour intervals are 0.2 and 0.5 K, respectively. [ABSTRACT FROM AUTHOR]

The potential of early warning for severe cold conditions is explored using the Subseasonal to Seasonal (S2S) Prediction research project data archive. We explore the use of a two‐dimensional phase space based on the leading empirical orthogonal functions (EOFs) of mid‐tropospheric flow computed over the Euro‐Atlantic region in order to study the time evolution of flow patterns associated with high‐impact temperature anomalies. We find that the phase space is an effective tool for monitoring predictions of regime transitions at medium and extended ranges. We show that a number of S2S systems have some skill in the prediction of cold spells over Europe, even beyond the medium range. In particular, the ECMWF (European Centre for Medium‐Range Weather Forecasts) model represents well the observed preferential transition paths. We reveal that the impact of the Madden–Julian Oscillation (MJO) on the predictive skill of large‐scale flow over Europe is asymmetric. The impact of the MJO on the Brier skill scores and reliability is significantly positive for predictions of the negative phase of the North Atlantic Oscillation (NAO): beyond week one, forecasts with the MJO in their initial state are significantly more reliable than forecasts with no MJO in their initial conditions. In contrast, the predictive skill for positive NAO shows little sensitivity to the MJO. Subseasonal predictions provide enormous potential for early warnings of severe cold events over Europe. As the image shows, several forecasting systems can provide skillful predictions of large‐scale patterns, typically associated with severe cold events over Europe, more than 10 days in advance. However, the success of forecasting weeks ahead changes in large‐scale flow that lead to cold conditions depends on the type of transitions. [ABSTRACT FROM AUTHOR]

The diagonal score is proposed as a new scoring rule for the assessment of univariate probabilistic forecasts. First, a general framework for the definition of proper user‐oriented scores is set up using the concept of elementary scoring rules. Building on this fundamental notion, a complete overview of forecast skill is provided by a new verification tool. The forecast skill card introduced displays the forecast value as a function of two decision parameters, a probability level and an event threshold, simultaneously. The diagonal score emerges as a summary score focusing on the diagonal of the skill card, that is, by fixing the relationship between the event base rate and forecast probability level. The properties of the new score, as well as interpretations from the perspectives of users and developers, are discussed based on synthetic datasets and 2 m temperature forecasts of the operational ECMWF ensemble prediction system. Example of a forecast skill card of the operational ECMWF ENS forecasts for 2 m temperature anomaly. The card shows the elementary skill score (or forecast value) as a function of the event of interest (expressed in terms of base rate πθ and threshold θ) and as a function of the user's cost‐loss ratio α. The diagonal score emerges as a summary score focusing on the diagonal of the skill card. [ABSTRACT FROM AUTHOR]

In this study six wooden Stevenson screen configurations (five aspirated, one non‐aspirated) were evaluated at the Centre for Atmospheric Research Experiments in Egbert, Ontario, Canada. The field experiment was performed over a 1‐year period to evaluate aspirated and non‐aspirated Stevenson screen configurations used at the Meteorological Service of Canada’s Automated Weather Stations. The results show the non‐aspirated screen is warmer than the aspirated screen duct location by 0.11 °C on average with a significant increase in the maximum daily temperature of 0.22 °C due to radiant heating effects. Temperature differences up to 2.1 °C were observed between the aspirated and non‐aspirated screens with a mean increase of 0.46 °C following 30‐min periods of mean global radiation greater than 200 W/m2 and mean wind speed less than 2 m/s. Temperature differences between the two screens are more variable with standard deviations up to 0.3 °C for wind speeds below 2 m/s. The maximum daily temperature for the historic minimum and maximum thermometer locations and upper regions of the aspirated screen is also found to be significantly warmer during radiant heating conditions. The three locations within the aspiration duct provided very similar results with mean differences of −0.02 and −0.01 °C below the probe uncertainty. The aspirated Stevenson screen with motor off produced low overall bias with lower maximum daily temperatures and higher minimum daily temperatures compared to the aspirated screen. Comparison between the historic wooden frame and new aluminium mounting is not significant with the wooden frame cooler by 0.02 °C overall. Differences between the new aluminium and new plastic ducts showed similar results of 0.02 and 0.01 °C overall. The aspirated Stevenson screen configuration is recommended at new Automatic Weather Station installations where AC power is available. [ABSTRACT FROM AUTHOR]

ABSTRACT: UK seasonal mean temperature and precipitation conditions are extremely variable from one year to the next but in the last decade have featured several cool, wet summers and mild, wet winters interspersed with some notable cold winter episodes. Jet stream variability is a major determinant of these fluctuations and is often represented by the North Atlantic Oscillation (NAO) index. Recent work has shown some evidence of promising predictability in the winter NAO from 1 to 2 months ahead, while summer predictability remains very limited. Although the phase and magnitude of the NAO influences total UK rainfall, there are regional variations which it does not explain. Here we examine the relationship between UK regional summer and winter precipitation and temperature and a range of North Atlantic atmospheric circulation indices. While the NAO shows a significant relationship with temperature in both seasons and summer rainfall over most of the UK, the picture in winter is more complicated, with other circulation indices such as the East Atlantic pattern explaining rainfall anomalies in southern England. Other indices also show significant relationships with precipitation in regions where the NAO does not. Because UK weather is determined by the interplay between different circulation indices, attention should be given to developing seasonal forecasts of other circulation indices to complement the NAO forecasts. We also find that some potential drivers of jet stream variability are significantly associated with UK temperature and rainfall variability, particularly in summer. This provides further scope for producing seasonal forecasts based directly on these drivers. Improved seasonal forecasts will be useful to a range of end users in agriculture, energy supply, transport and insurance industries and can be extended to other UK weather variables such as extreme rainfall events and storm frequency, and related metrics such as wind power capacity and solar energy. [ABSTRACT FROM AUTHOR]

ABSTRACT: The correction of heat‐biased temperatures measured by radiosondes during the daytime is affected by other environmental parameters such as ventilation, air temperature and pressure. Among them, the effect of low temperature on temperature sensors under irradiation is not well known. This paper studies the combined effect of low temperature and low pressure on the air‐temperature measurement under irradiation to obtain a correction formula using dual thermistors with different emissivities. The temperature variation of both thermistors under irradiation (about 1500 W m–2) is increased as temperature and pressure are decreased from 25 to −80 °C and from 1000 to 10 hPa, respectively. The observed behaviour is explained using heat‐transfer equations. In order to calculate the irradiance with no pyranometer, the temperature difference between dual thermistors is formulated as a function of irradiance, temperature and pressure through chamber‐based experiments. After the relationship between parameters is established, the temperature difference between thermistors is solely used to calculate the irradiance. The uncertainty of the calculated irradiance using dual thermistors is 6.2% (94 W m−2) at the coverage factor k = 1. The calculated irradiance is used for the correction of the heat‐biased temperatures measured by the thermistors. The combined uncertainty of corrected temperature is 0.79 K (k = 1), which includes uncertainties owing to the irradiance, pressure and temperature sensors as well as modelling. The dual thermistor‐based technique using ground‐calibration facilities can provide the traceability to the International System of Units (SI) in the measurement of irradiance and temperature in the upper air. [ABSTRACT FROM AUTHOR]

ABSTRACT: This study documents monthly temperature and precipitation anomalies over Korea during the different phases of El Niño‐Southern Oscillation (ENSO) for the two types of ENSO evolution. The evolution process of ENSO can be classified into two groups based on whether El Niño turns into La Niña in the subsequent year. The first group involves the transition process from El Niño to La Niña, while the second group shows the prolonged El Niño or neutral conditions after the mature phase of El Niño. Because the mid‐latitude atmospheric responses as well as the equatorial heating anomalies for the two groups of ENSO are different each other, the ENSO‐related climate variation over Korea are investigated separately for the two ENSO evolution groups. In particular, this study focuses on the entire monthly evolution of the temperature and precipitation over Korea during the different phases of ENSO. The statistically robust signals can be found in several particular months, which provides statistical basis for predicting monthly climate over Korea. In addition to the observational data analyses, we further investigate the forecast skill of the Asia‐Pacific Economic Cooperation Climate Center (APCC) Multi‐Model Ensemble (MME) in simulating two groups of ENSO evolution and their impacts on Korean climate. The result shows that the MME reasonably predicts the two different evolution of ENSO as in observation but their prediction skills for the ENSO‐related Korean climate are diverse, which largely depends on the phase of ENSO. [ABSTRACT FROM AUTHOR]

ABSTRACT Permafrost is sensitive to climate change. In recent decades, a growing body of research has focused mainly on the study of permafrost thaw, but leaving the climate change in the permafrost region that has not been adequately assessed, which is of first importance for the research on permafrost thaw. Using gridded observations from the Climatic Research Unit ( CRU), in conjunction with the European Centre for Medium-Range Weather Forecasts Reanalysis Interim ( ERA-Interim) and Japanese 55-year Reanalysis ( JRA-55) data, this study investigates characteristics of air temperature evolution in the region of permafrost throughout the 20th century. Results show that yearly air temperatures in the permafrost region of the Northern Hemisphere experienced a statistically significant warming, with trends of 0.13 °C decade−1 for 1901-2014 and 0.40 °C decade−1 for 1979-2014. Winter air temperatures showed the greatest increase during 1901-2014, while autumn air temperatures increased the most during 1979-2014. In addition, increases in air temperature in high-latitude permafrost sub-region are greater than those in high-elevation permafrost sub-region, and air temperatures in the permafrost sub-region of Mongolia have the largest trend from 1901 to 2014, followed by those in Russia, Alaska, Canada, and China. Air temperatures in the permafrost region increased 1.7 times more than temperatures globally from 1901 to 2014, and underwent an increase at a rate of 0.32 °C decade−1 during the period 1998-2014, when the global warming hiatus occurred with a trend of 0.06 °C decade−1. This implies that permafrost thaw may have continued during the global warming hiatus period. The close agreement between CRU data and ERA-Interim and JRA-55 reanalysis data indicates good reliability of air temperature evolution characteristics. These results provide information relevant to climate change in the permafrost region, and are useful for researching and understanding historical permafrost change. [ABSTRACT FROM AUTHOR]

This study mainly focuses on the effects of uncertainty in land surface information on mesoscale numerical simulation. The Weather Research and Forecasting (WRF) model was used to simulate meteorological fields over China at a spatial resolution of 10 km during 2006. Near-surface temperature and precipitation values obtained from WRF were evaluated using site observations. The effects of accurate and updated land surface information, including Shuttle Radar Topography Mission (SRTM) data, Moderate resolution imaging spectroradiometer (MODIS) land use data, vegetation fraction derived from MODIS normalized difference vegetation index (NDVI) and Harmonized World Soil Database (HWSD) data (LAST simulation), on WRF's performance were investigated by comparison with a simulation using the default land surface information (BASE simulation). WRF reproduced the temporal and spatial variations of near-surface temperature and precipitation over China accurately, although its performance varied significantly by season and region. WRF underestimated near-surface temperatures in most areas of the Yunnan–Guizhou Plateau, the Tibetan Plateau, the Northeast Plain, and the southeastern coastal regions, but temperatures were overestimated in most areas of the North China Plain, the Loess Plateau, Sichuan Basin, and western Xinjiang. WRF overestimated (underestimated) precipitation in most humid (arid) areas. A positive (negative) bias in simulated precipitation is found in summer (winter). With updated land surface information, WRF's performance in terms of both daily average values and extremes improved, and the root mean squared error values for daily mean temperature and daily accumulated precipitation decreased by 7 and 2.3%. These improvements are significant for temperature, but not significant for precipitation. The uncertainty in land surface information has a greater influence on temperature than on precipitation. These findings are very important for weather forecasting and studies involving climatological analyses covering East Asia. [ABSTRACT FROM AUTHOR]

Seasonal rainfall forecasts are an important tool for risk management across many sectors. However, significant challenges arise in the development of skilful and practically useful seasonal forecasts for regions where the temporal and spatial variability of rainfall is large and/or knowledge about what causes this variability is in its infancy. This is evident in the state of South Australia (SA), where seasonal rainfall currently has low predictive skill. The key climate processes have yet to be fully identified in SA and therefore may not be adequately represented in forecast models. The aim of this paper is to identify and quantify relationships between large-scale ocean-atmospheric processes and seasonal rainfall variability across SA. We identify two distinct climate zones: (1) the arid northern region, where rainfall is mostly influenced by climate processes stemming from the tropical Indian and/or Pacific Oceans and (2) southern SA, which is dominated by Southern Ocean processes. The average percent of variability of SA rainfall accounted for by any single large-scale climate process (i.e. linear regression using a single predictor) is 8% in summer, 19% in autumn, 33% in winter and 24% in spring. However, when two or more processes are considered in combination (through multiple linear regression), this rises to 13, 26, 46, and 33%, respectively, highlighting the importance of capturing the interaction among multiple climate processes. Importantly, the findings from this study provide a set of metrics against which existing statistical and dynamical forecasting schemes can be tested and highlight processes that should be focused on in order to improve (or develop new) forecasting schemes. The study also recommends the need for further investigations into non-linear relationships between rainfall and large-scale ocean-atmospheric processes and the development of more objective methods for determining which climate process, or combination of processes, are most important for a certain season or location. [ABSTRACT FROM AUTHOR]

Progresses in understanding climate variation and its ecosystem responses and feedback in Central Asia have been limited by availability of high resolution and quality climate data. To ease this limitation, the Weather Research and Forecasting (WRF) model is implemented in Central Asia in this study after repeated yearly sensitivity evaluations. The optimized WRF is used to downscale the ERA-Interim reanalysis data, producing a new regional dataset of 20 × 20 km spatial resolution covering 1980-2015. The downscaled precipitation and daily maximum and minimum temperatures of seven years from 1980-1986, when the observational data are the most abundant, are compared in statistical measures to station observations and also contrasted with the comparison results between the ERA-Interim data and the observations. Results of those comparisons show that the downscaled data have overall comparable accuracy in precipitation and daily extreme temperatures to the ERA-Interim data. An outstanding improvement of the downscaled data is in their describing the variance in precipitation and temperatures at daily to annual timescales, while the downscaled extreme precipitation and temperature events also show more accurate comparisons to the observed at some sub-regions in Central Asia. The validated WRF model, which can be used to further downscale past as well as future climate data for specific research needs, and the downscaled high resolution and quality data present both a tool and a useful dataset for ecological and detailed climate research in Central Asia. [ABSTRACT FROM AUTHOR]

Lateral and lower boundary conditions derived from a suitable global reanalysis data set form the basis for deriving a dynamically consistent finer resolution downscaled product for climate and hydrological assessment studies. A problem with this, however, is that systematic biases have been noted to be present in the global reanalysis data sets that form these boundaries, biases which can be carried into the downscaled simulations thereby reducing their accuracy or efficacy. In this work, three Weather Research and Forecasting (WRF) model downscaling experiments are undertaken to investigate the impact of bias correcting European Centre for Medium range Weather Forecasting Reanalysis ERA-Interim (ERA-I) atmospheric temperature and relative humidity using Atmospheric Infrared Sounder (AIRS) satellite data. The downscaling is performed over a domain centered over southern Africa between the years 2003 and 2012. The sample mean and the mean as well as standard deviation at each grid cell for each variable are used for bias correction. The resultant WRF simulations of near-surface temperature and precipitation are evaluated seasonally and annually against global gridded observational data sets and compared with ERA-I reanalysis driving field. The study reveals inconsistencies between the impact of the bias correction prior to downscaling and the resultant model simulations after downscaling. Mean and standard deviation bias-corrected WRF simulations are, however, found to be marginally better than mean only bias-corrected WRF simulations and raw ERA-I reanalysis-driven WRF simulations. Performances, however, differ when assessing different attributes in the downscaled field. This raises questions about the efficacy of the correction procedures adopted. [ABSTRACT FROM AUTHOR]

ABSTRACT Temperature changes in the Arctic due to anthropogenic climate change are larger in magnitude than those at lower latitudes, with sea ice extent and thickness diminishing since the dawn of the satellite era. Aerosols play a role in determining the changes to the Arctic surface temperature. While Part 1 of this two-part study investigated the changes in Arctic surface temperature due to the total aerosol effect, in Part 2, the total aerosol effect is separated into the changes caused by the aerosol direct effect, the aerosol semi-direct effect, and the aerosol indirect effects through the use of additional Weather Research and Forecasting Chemistry ( WRF-Chem) runs. While Part 1 of the study showed that aerosols may have both a cooling and warming effect, largely depending upon the time of day and aerosol concentration, Part 2 of this study shows that the indirect effects are the dominant component of the total aerosol effect on the cooling and warming of the Arctic surface temperature throughout the diurnal cycle. It is also shown that the size distribution of aerosols is important, as smaller aerosols dominate the aerosol indirect effects. The aerosol direct effect contributes to cooling in the region, while the semi-direct effect is negligible. [ABSTRACT FROM AUTHOR]