Cold Bias Research Articles

Meteorological Factors Responsible for Major Power Outages during a Severe Freezing Rain Storm over Eastern Canada

Abstract Winter precipitation is the source of many inconveniences in many regions of North America, for both infrastructure and the economy. The ice storm that hit the Canadian Maritime Provinces on 24–26 January 2017 remains one of the most expensive in history for the province of New Brunswick. Up to 50 mm of freezing rain caused power outages across the province, depriving up to one-third of New Brunswick residences of electricity, with some outages lasting 2 weeks. This study aims to use high-resolution atmospheric modeling to investigate the meteorological conditions during this severe storm and their contribution to major power outages. The persistence of a deep warm layer aloft, coupled with the slow movement of the associated low pressure system, contributed to widespread ice accumulation. When combined with the strong winds observed, extensive damage to electricity networks was inevitable. A 2-m temperature cold bias was identified between the simulation and the observations, in particular during periods of freezing rain. In the northern part of New Brunswick, cold-air advection helped keep temperatures below 0°C, while in southern regions, the 2-m temperature increased rapidly to slightly above 0°C because of radiational heating. The knowledge gained in this study on the processes associated with either maintaining or stopping freezing rain will enhance the ability to forecast and, in turn, to mitigate the hazards associated with those extreme events. Significance Statement A slow-moving low pressure system produced up to 50 mm of freezing rain for 31 h along the east coast of New Brunswick, Canada, on 24–26 January 2017, causing unprecedented power outages. Warm-air advection aloft, along with a combination of higher wind speeds and large amounts of ice accumulation, created ideal conditions for severe freezing rain. The storm began with freezing rain along the entire north–south cross section of eastern New Brunswick and changed to rain only in the south, when local temperatures increased to >0°C. Near-surface cold-air advection kept temperatures below 0°C in the north. Warming from the latent heat produced by freezing contributed to persistent near-0°C conditions during freezing rain.

Investigating NUCAPS Skill in Profiling Saharan Dust for Near-Real-Time Forecasting

Dust outflows off Northwest Africa often propagate westward across the North Tropical Atlantic Basin (NTAB) into the greater Caribbean and US. From a health perspective, weather forecasters in these regions often monitor hazardous air quality associated with this dust. However, forecasters can be constrained by sparse data observations upwind over the Atlantic of the impacted populated areas. Global satellite sounding retrievals can potentially augment and enhance the operational forecasting toolkit for monitoring Saharan dust episodes. The focus of this paper was to examine the skill of the NOAA Unique Combined Atmospheric Processing System (NUCAPS) temperature and water vapor profiles within the dust and non-dust conditions during the March 2019 NOAA Aerosols and Ocean Science Expedition (AEROSE). During this time, the NOAA Ron Brown research ship launched radiosondes to coincide with satellite overpasses that served as independent ground truth data for evaluating NUCAPS. Compared to RAOBs from the Ron Brown, the SNPP and NOAA-20 NUCAPS-derived soundings showed skill in profiling atmospheric conditions supporting Saharan dust monitoring. Outside of dust regions, the NOAA-20 NUCAPS surface temperature bias peaks at 2.0 K; the surface water vapor bias is minimal (~1000 hPa), with a small cold bias that peaks at −50% between 742 and 790 hPa. Corresponding temperature RMS values are less than 2.0 K; water vapor RMS values are generally below 70%. Within the dust regions, NOAA-20 NUCAPS temperature soundings show a cold bias peak of 2.6 K at 918 hPa and 113% of a moist bias peak at the same level. Corresponding temperature RMS values maximize at 3.5 K at 945 hPa; the water vapor RMS shows a peak value of 106% at the same level. Weather forecasters can apply NUCAPS across the NTAB in issuing timely and accurate hazardous air quality warnings and visibility alerts to health officials and the general public.

Aridity‐Dependent Land Surface Skin Temperature Biases in CMIP5/6

AbstractLand surface skin temperature, a critical indicator of climate change, connects the water and energy cycles between the land and the atmosphere. Here, we evaluate the simulations of land surface skin temperature from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and CMIP6 models with satellite‐based datasets and reanalysis. We find systematic cold skin temperature biases over arid regions in CMIP5/CMIP6 simulations. Over arid and semi‐arid regions, latent heat biases drive skin temperature biases by evaporative cooling. Over humid regions, surface downward shortwave and albedo biases are relatively more critical. Spatial patterns of biases remain similar in the latest CMIP6 simulations, suggesting systematic biases in land‐atmosphere interactions. These biases need to be corrected or considered while using models for future projections.

Remote effect of model systematic bias in tropical SST on the cold bias over the Tibetan Plateau

Most state-of-the-art climate models substantially underestimate the near-surface air temperature (SAT) over the Tibetan Plateau (TP), especially for the cold season. While previous studies have attributed this cold bias to local factors such as the elevation difference, coarse resolution, and excessive snow cover, this investigation addresses the potential contributions of the systematic bias of tropical sea surface temperature (SST) to the TP cold bias. Experiments with the NCAR Community Atmosphere Model demonstrate that tropical SST bias results in an apparent cold bias over the TP, especially in boreal winter, and explains about 40% of the deviation in multi-model mean SAT over the TP relative to station observations. Forced by the tropical SST bias, heat flux exhibits an anomalous divergence over the plateau, causing a cooling center in the mid- and lower-troposphere over the TP. This atmospheric cooling in turn leads to a reduction of the downward longwave radiative fluxes reaching the surface, less energy supply, and thus a cold bias over the TP.

Comparison of MODIS surface temperatures to in situ measurements on the Greenland Ice Sheet from 2014 to 2017

AbstractRemotely sensed land surface temperature (LST) data, such as the Moderate Resolution Imaging Spectroradiometer (MODIS) LST thermal infrared products, are useful for monitoring surface processes on the Greenland Ice Sheet in remote areas but must be validated to ensure accuracy. Using data from the Programme for Monitoring the Greenland Ice Sheet (PROMICE), we conducted a MODIS LST validation (MOD/MYD11 C6 swath level product) using radiometric in-situ skin temperature records from 2014 to 2017 over 17 PROMICE sites mostly in the ice sheet's ablation zone. There is a significant cold bias in MODIS LST when compared to PROMICE skin temperature, particularly when PROMICE records temperatures below 0°C (mean bias: 2.4 ± 0.01°C mean ± standard error, RMSE = 3.2°C). Multiple linear regression analysis reveals the difference between MODIS LST and PROMICE skin temperature is larger at lower temperatures, lower latent heat fluxes and higher specific humidity. Our results confirm the presence of a progressive cold bias in the MODIS LST that should be considered in use of this product, and we identify and corroborate areas for ongoing algorithm development.

Evaluation of MERRA-2 land surface temperature dataset and its application in permafrost mapping over China

The second-generation Modern-ERA Retrospective analysis for Research and Applications (MERRA-2) land surface temperature (LST) dataset has been widely used for permafrost mapping in specific areas; however, its accuracy and application need to be evaluated over China. In this study, the MERRA-2 LST was evaluated against meteorological observations and three other reanalysis datasets including the first-generation MERRA, Japanese 55-year Reanalysis (JRA-55), and European Centre for Medium-Range Weather Forecasts (ECMWF) Interim Reanalysis (ERA-Interim), using multiple statistical methods over the period from 1980 to 2018. The results revealed that the MERRA-2 LST generally exhibited cold bias compared to meteorological observations while performing better than the JRA55, ERA-Interim, and MERRA datasets in China, particularly in high-altitude permafrost regions. The comparison indicated that the time series trends for the MERRA-2 LST was consistent with that observed until 2000, and noticeably amplified cold bias, particularly for the period after 2005, was observed. Moreover, two correction methods were proposed and compared to reduce the error range for the MERRA-2 dataset, which was caused by the difference in elevation and land cover types. Calibrated results demonstrated that the linear regression method (Method1) between the elevation difference and mean bias error (MBE) for the LST performed well with root mean square error (RMSE) ranged from 2.15 to 5.97 °C to 1.09–2.53 °C. Moreover, in comparison with the MODIS LST dataset, the results showed that the adjusted MERRA-2 LST was in good agreement with smaller RMSEs against the observations. The surface frost number model was used for mapping the permafrost distribution over China based on the daily adjusted MERRA-2 LST dataset. According to the simulation results, the permafrost extent had a slightly continued degradation trend with a rate of 3–5% per decade over the past 39 years. The simulated permafrost area over China for the years 2010–2018 was approximately 1.63 × 106 km2, which accounts for 16.9% of mainland China. Thus, the adjusted MERRA-2 LST with high spatial–temporal consistency is the optimal choice to investigate permafrost distribution on a large scale.

Evaluation of ERA5-Land and HARv2 Reanalysis Data at High Elevation in the Upper Dudh Koshi Basin (Everest Region, Nepal)

Abstract We present a multisite evaluation of meteorological variables in the Everest region (Nepal) from ERA5-Land and High Asian Refined Analysis, version 2 (HARv2), reanalyses in comparison with in situ observations, using classical statistical metrics. Observation data have been collected since 2010 by seven meteorological stations located on or off glacier between 4260 and 6352 m MSL in the upper Dudh Koshi basin; 2-m air temperature, specific and relative humidities, wind speed, incoming shortwave and longwave radiations, and precipitation are considered successively. Overall, both gridded datasets are able to resolve the mesoscale atmospheric processes, with a slightly better performance for HARv2 than that for ERA5-Land, especially for wind speed. Because of the complex topography, they fail to reproduce local- to microscale processes captured at individual meteorological stations, especially for variables that have a large spatial variability such as precipitation or wind speed. Air temperature is the variable that is best captured by reanalyses, as long as an appropriate elevational gradient of air temperature above ground, spatiotemporally variable and preferentially assessed by local observations, is used to extrapolate it vertically. A cold bias is still observed but attenuated over clean-ice glaciers. The atmospheric water content is well represented by both gridded datasets even though we observe a small humid bias, slightly more important for ERA5-Land than for HARv2, and a spectacular overestimation of precipitation during the monsoon. The agreement between reanalyzed and observed shortwave and longwave incoming radiations depends on the elevation difference between the station site and the reanalysis grid cell. The seasonality of wind speed is only captured by HARv2. The two gridded datasets ERA5-Land and HARv2 are applicable for glacier mass and energy balance studies, as long as either statistical or dynamical downscaling techniques are used to resolve the scale mismatch between coarse mesoscale grids and fine-scale grids or individual sites.

Accounting for Topographic Effects on Snow Cover Fraction and Surface Albedo Simulations Over the Tibetan Plateau in Winter

AbstractThe Tibetan Plateau (TP) is the highest land in the world and has a very complex topography. However, the influence of topography on TP snow cover simulations has not been adequately addressed in most land surface models. The analysis of satellite observations indicates that snow cover fraction (SCF) simulation biases increase with the topography complexity, and this increasing trend slows down when the standard deviation of topography is greater than 200 m. The result also shows that using the SCF schemes without consideration of topography leads to a consistent overestimation in winter. To account for the topographic effects, we introduce a modified topographic factor to the SCF schemes. Then we conduct regional simulations using the Simplified Simple Biosphere Model version 3 (SSiB3) and evaluate the results at the location of observation sites to reduce the uncertainty induced by forcing data. Compared with the default SCF scheme, the mean winter SCF bias is reduced from 3.83% to −0.10%. The optimization of SCF simulations further improves the winter surface albedo and land surface temperature (LST) simulations. The winter surface albedo bias over the TP is reduced from 0.020 to 0.007, with a maximum reduction by −0.133. The winter LST bias is reduced from −3.33 to −3.04 K, with a maximum reduction by 3.60 K. This study highlights the importance of topographic effects in simulating snow cover distribution and land surface energy budget for reducing the “cold bias” in winter climate simulations over the TP.

Mean State of the Northern Hemisphere Stratospheric Polar Vortex in Three Generations of CMIP Models

Abstract Progress and persistent biases in the simulation of the stratospheric polar vortex from three generations of Coupled Model Intercomparison Projects (CMIPs) are assessed. On average, the stratospheric cold bias is largest in CMIP3, but is improved in CMIP5 and CMIP6. The climatological ridge in the stratosphere over the North Pacific is underestimated persistently across CMIPs. Four parameters of the stratospheric polar vortex are evaluated among models and among CMIPs, and some common biases are identified for most models, including the too-large size of the vortex periphery, overstrong strength, too-small aspect ratio of the vortex shape, and too-westward displaced a vortex centroid. Intermodel spread in some parameters is highly correlated with the sea surface temperature (SST) bias in northern tropical oceans. Namely, a cold SST bias in the northern tropical Pacific is associated with a too-strong and too-large polar vortex, and a cold SST bias in the northern tropical Atlantic seems to be related to the westward bias of the vortex centroid. The implications of biases in the mean state of the vortex for stratospheric events such as sudden stratospheric warmings (SSWs) are also identified in this study. Models with a climatological vortex that is too strong, has too low an aspect ratio, or has a westward-biased centroid usually produce fewer SSWs, whereas ones with weaker strength, high aspect ratio, and eastward-biased centroid produce more. Based on the multimodel mean, the biases of all parameters have improved across three generations of CMIPs, although persistent biases in some individual models exist across CMIPs. Intermodel relationships between the polar vortex and SST highlights the important role of the SST simulation for the stratosphere.

Noah‐MP With the Generic Crop Growth Model Gecros in the WRF Model: Effects of Dynamic Crop Growth on Land‐Atmosphere Interaction

AbstractIn this paper we coupled a crop growth model to the Weather Research and Forecasting model with its land surface model Noah‐MP and demonstrated the influence of the weather driven crop growth on land‐atmosphere (L‐A) feedback. An impact study was performed at the convection permitting scale of 3 km over Germany. While the leaf area index (LAI) in the control simulation was the same for all cropland grid cells, the inclusion of the crop growth model resulted in heterogeneous crop development with higher LAI and stronger seasonality. For the analyses of L‐A coupling, a two‐legged metric was applied based on soil moisture, latent heat flux and convective available potential energy. Weak atmospheric coupling is enhanced by the crop model, the terrestrial coupling determines the regions with the L‐A feedback. The inclusion of the crop model turns regions with no L‐A feedback on this path into regions with strong positive coupling. The number of non‐atmospherically controlled days between April and August is increased by 10–15 days in more than 50% of Germany. Our work shows that this impact results in a reduction of both cold bias and warm biases and thus improves the metrics of distributed added value of the monthly mean temperatures. The study confirms that the simulation of the weather driven annual phenological development of croplands for the regional climate simulations in mid‐latitudes is crucial due to the L‐A feedback processes and the currently observed and expected future change in phenological phases.

How well have CMIP3, CMIP5 and CMIP6 future climate projections portrayed the recently observed warming

Despite the dire conclusions of the Intergovernmental Panel on Climate Change (IPCC) Assessment Reports in terms of global warming and its impacts on Earth’s climate, ecosystems and human society, a skepticism claiming that the projected global warming is alarmist or, at least, overestimated, still persists. Given the years passed since the future climate projections that served as basis for the IPCC 4th, 5th and 6th Assessment Reports were released, it is now possible to answer this fundamental question if the projected global warming has been over or underestimated. This study presents a comparison between CMIP3, CMIP5 and CMIP6 future temperature projections and observations. The results show that the global warming projected by all CMIPs and future climate scenarios here analyzed project a global warming slightly lower than the observed one. The observed warming is closer to the upper level of the projected ones, revealing that CMIPs future climate scenarios with higher GHG emissions appear to be the most realistic ones. These results show that CMIPs future warming projections have been slightly conservative up to 2020, which could suggest a similar cold bias in their warming projections up to the end of the current century. However, given the short future periods here analyzed, inferences about warming at longer timescales cannot be done with confidence, since the models internal variability can play a relevant role on timescales of 20 years and less.

Snow depth and snow cover over the Tibetan Plateau observed from space in against ERA5: matters of scale

There has been a statement that the satellite and reanalyses significantly overestimate snow depths (SDs) as compared with in-situ observations over the Tibetan Plateau (TP). The inconsistency may be partly due to representations related to different spatial resolutions. To further clarify matters of scale, this work estimates and compares the fractional snow cover (FSC) from MODIS (500 m), SDs from Sentinel-1 C-band SAR (1 km), ERA5-Land (9 km) and ERA5 (31 km), together with ground observations from GHCN-D and at 6 newly-established stations in the Namco watershed. SD and FSC from MERRA-2 (50 km) are also discussed in comparison with ERA5s. Results indicate that SD from fine resolution reanalysis has better consistency to in-situ observations over the TP. SD of ERA5-Land matches in-situ measurements better than ERA5 and MERRA-2. Overestimates of SDs in reanalyses are likely for shallow snowpack. However, underestimates are found for deep snow particularly late in the snow season. Improvements are displayed in ERA5-Land, while increased SD is attributed by altitude in fine resolution. Overall SDs of ERA5 and ERA5-Land have similar spatial distributions and annual cycle patterns over the TP, consistent with satellite-based datasets. A notable defect of ERA5s is related to delayed ablation of deep snowpack during spring and early summer. It causes cold temperature biases at surface that may impact on the land-atmosphere interaction. Snow analysis that combines the information of FSC into SD has been demonstrated in MERRA-2 data. Snow analysis has the potential to improve SDs of ERA5s over the TP.

SSP 시나리오 기반 CORDEX-동아시아 2단계 다중 기후모델의 동아시아 지역 모의 성능평가 및 미래 전망 분석

Recently, the CORDEX-East Asia team completed climate change simulations based on five regional climate models (RCM) under four Shared Socioeconomic Pathways (SSP) scenarios. In this study, the performance of five RCMs and their ensemble averages for present-day climate simulations are evaluated. All RCMs have relatively high performance for climatology patterns for Korea, East China and Japan, while relatively low performance is observed for the Tibetan plateau. Many RCMs share cold and wet biases. Future climate changes under four SSP scenarios are analyzed for the end of 21st century (2081 ~ 2100) compared to present day (1995 ~ 2014). East Asia is expected to experience temperature increases of 2.4℃ to 6.2℃ and precipitation increases of 6.7% to 12.6%, with stronger changes in higher-emission scenarios. Among the five RCMs, HadGEM3-RA projects the largest increase in temperature while GRIMs is characterized by the strongest increase in precipitation. In line with mean warming rates, warm extreme days (TX90p) are projected to increase by 35.7 ~ 93.3 days and cold extreme days (TN10p) are projected to decrease by 23.4 ~ 35.2 days. The results of this study can be used as a reference for future detailed analyses of East Asian climate change and its impacts as well as for emphasizing the importance of carbon neutrality.

Characteristics of High-Latitude Climate and Cloud Simulation in Community Atmospheric Model Version 6 (CAM6)

Many global climate models (GCMs) have difficulty in simulating climate variabilities over high northern latitudes. One of the main reasons is the inability of GCMs to simulate proper cloud fraction and the amount of liquid-containing cloud over the region. This study assessed the impact of cloud simulation in high latitudes by comparing the long-term parallel simulations of Community Atmosphere Model version 6 (CAM6) and CAM5, the previous version. The results show that the CAM6 simulation exhibits a considerable improvement in the Arctic, especially by reducing the cold bias of CAM5 throughout the year. Over the sub-Arctic region, however, CAM6 produces an excessive cold bias in summer and a warm bias in winter compared to the observation, which is closely related to the overestimation of cloud fraction and the amount of cloud liquid. In summer, the overestimation of the cloud in CAM6 tends to alleviate the cold bias compared to CAM5 due to an increase in downward longwave radiation over the high latitudes, while causing the excessive cold bias by blocking downward shortwave radiation over the sub-Arctic land area. In winter, when there is little incidence of shortwave radiation, the overestimation of the cloud in CAM6 increases the downward longwave radiation, which alleviates the cold bias in CAM5 over the Arctic but induces an excessive warm bias over the sub-Arctic land. The excessive cloudiness in CAM6 could weaken the high-latitude internal variability, exacerbating the deteriorating climate variability and long-term trend simulations in the region.

The role of land surface schemes in non‐hydrostatic RegCM on the simulation of Indian summer monsoon

AbstractThis study evaluates the performance of a non‐hydrostatic Regional Climate Model (RegCM) with two land surface parameterizations, namely Biosphere‐Atmosphere Transfer (BAT) scheme and Community Land Model (CLM) scheme on the simulation of summer monsoon over India. The initial and boundary conditions are taken from ERA‐Interim (ERA‐I) reanalysis available at a 0.75° spatial grid. The RegCM is designed with 25 km horizontal resolution and 23 vertical levels, and it integrated for the period 1st January 1982 to 30th September 2016. We used the NOAA Optimum Interpolation Weekly sea surface temperatures, the topography and land‐use data from the United States Geological Survey, and Global Land Cover Characterization. Our results reveal that the summer monsoon precipitation anomaly is reasonably well simulated with BAT and CLM schemes compared to those of India Meteorological Department (IMD) observations. The spatial distributions of temperature anomaly over India with BAT compared to CRU shows cold bias over most of the Indian sub‐continent, whereas the CLM shows large warm bias over central India and monsoon trough region. The low‐level and upper‐level winds are in good agreement with ERA‐I. Further, the analysis of extreme monsoon events (i.e., excess and deficit seasons) reveals that CLM depicts less bias in simulating the excess and deficit precipitation regions as compared to those of BAT. Conversely, BAT simulates temperature well over India in both excess and deficit monsoon seasons. The Taylor's diagram analysis, Added Value Index based on Mean Square Error, and Modified Brier Skill Score are also applied for the extreme rainfall seasons. Our quantitative analysis demonstrates that the CLM have resolved better rainfall features with more skills with respect to that of BAT. The results suggest that CLM have added value in Indian Summer Monsoon (ISM) regional climate simulation than the BAT, particularly on simulation of the rainfall characteristics during extreme rainfall seasons.

The Equatorial Pacific Cold Tongue Bias in CESM1 and Its Influence on ENSO Forecasts

Abstract The mean-state bias and the associated forecast errors of the El Niño–Southern Oscillation (ENSO) are investigated in a suite of 2-yr-lead retrospective forecasts conducted with the Community Earth System Model, version 1, for 1954–2015. The equatorial Pacific cold tongue in the forecasts is too strong and extends excessively westward due to a combination of the model’s inherent climatological bias, initialization imbalance, and errors in initial ocean data. The forecasts show a stronger cold tongue bias in the first year than that inherent to the model due to the imbalance between initial subsurface oceanic states and model dynamics. The cold tongue bias affects not only the pattern and amplitude but also the duration of ENSO in the forecasts by altering ocean–atmosphere feedbacks. The predicted sea surface temperature anomalies related to ENSO extend to the far western equatorial Pacific during boreal summer when the cold tongue bias is strong, and the predicted ENSO anomalies are too weak in the central-eastern equatorial Pacific. The forecast errors of pattern and amplitude subsequently lead to errors in ENSO phase transition by affecting the amplitude of the negative thermocline feedback in the equatorial Pacific and tropical interbasin adjustments during the mature phase of ENSO. These ENSO forecast errors further degrade the predictions of wintertime atmospheric teleconnections, land surface air temperature, and rainfall anomalies over the Northern Hemisphere. These mean-state and ENSO forecast biases are more pronounced in forecasts initialized in boreal spring–summer than other seasons due to the seasonal intensification of the Bjerknes feedback.

Summer regional climate simulations over Tibetan Plateau: from gray zone to convection permitting scale

The Tibetan Plateau (TP) is often referred to as ‘the Third Pole’ and plays an essential role in the global climate. However, it remains challenging for most global and regional models to realistically simulate the characteristics of climate over the TP. In this study, two Weather Research and Forecasting model (WRF) experiments using spectral nudging with gray-zone (GZ9) and convection-permitting (CP3) resolution are conducted for summers from 2009 to 2018. The surface air temperature (T2m) and precipitation from the two simulations and the global reanalysis ERA5 are evaluated against in-situ observations. The results show that ERA5 has a general cold bias over southern TP, especially in maximum T2m (Tmax), and wet bias over whole TP. Both experiments can successfully capture the spatial pattern and daily variation of T2m and precipitation, though cold bias for temperature and dry bias for precipitation exist especially over the regions south of 35° N. Compared with ERA5, the added value of the two WRF experiments is mainly reflected in the reduced cold bias especially for Tmax with more improvement found in CP3 and the reduced wet bias. However, the ability of the convection-permitting WRF experiment in improving the simulation of precipitation seems limited when compared to the gray-zone WRF experiment, which may be related to the biases in physical parameterization and lack of representativeness of station observation. Further investigation into surface radiation budget reveals that the underestimation of net shortwave radiation contributes a lot to the cold bias of T2m over the southeastern TP in GZ9 which is improved in CP3. Compared with GZ9, CP3 shows that larger specific humidity at low-level (mid-high level) coexists with more precipitation (clouds) over the southern TP. This improvement is achieved by better depiction of topographic details, underlying surface and atmospheric processes, land–atmosphere interactions and so on, leading to stronger northward water vapor transport (WVT) in CP3, providing more water vapor for precipitation at surface and much wetter condition in the mid-high level.

Intercomparison of Vaisala RS92 and RS41 Radiosonde Temperature Sensors under Controlled Laboratory Conditions

Radiosoundings are essential for weather and climate applications, as well as for calibration and validation of remote sensing observations. Vaisala RS92 radiosondes have been widely used on a global scale until 2016; although in the fall of 2013, Vaisala introduced the RS41 model to progressively replace the RS92. To ensure the highest quality and homogeneity of measurements following the transition from RS92 to RS41, intercomparisons of the two radiosonde models are needed. A methodology was introduced to simultaneously test and compare the two radiosonde models inside climatic chambers, in terms of noise, calibration accuracy, and bias in temperature measurements. A pair of RS41 and RS92 radiosondes has been tested at ambient pressure under very different temperature and humidity conditions, reproducing the atmospheric conditions that a radiosonde can meet at the ground before launch. The radiosondes have also been tested before and after fast (within ≈ 10 s) temperature changes of about ±20 °C, simulating a scenario similar to steep thermal changes that radiosondes can meet when passing from indoor to outdoor environment during the pre-launch phase. The results show that the temperature sensor of RS41 is less affected by noise and more accurate than that of RS92, with noise values less than 0.06 °C for RS41 and less than 0.1 °C for RS92. The deviation from the reference value, referred to as calibration error, is within ±0.1 °C for RS41 and the related uncertainty (hereafter with coverage factor k = 1) is less than 0.06 °C, while RS92 is affected by a cold bias in the calibration, which ranges from 0.1 °C up to a few tenths of a degree, with a calibration uncertainty less than 0.1 °C. The temperature bias between RS41 and RS92 is within ±0.1 °C, while its uncertainty is less than 0.1 °C. The fast and steep thermal changes that radiosondes can meet during the pre-launch phase might lead to a noise increase in temperature sensors during radiosoundings, up to 0.1 °C for RS41 and up to 0.3 °C for RS92, with a similar increase in their calibration uncertainty, as well as an increase in the uncertainty of their bias up to 0.3 °C.

Potential and limitations of convection‐permitting CNRM‐AROME climate modelling in the French Alps

AbstractConvection‐permitting climate modelling is a promising avenue for climate change research and services especially in mountainous regions. Work is required to evaluate the results of high‐resolution simulations against relevant observations, and put them in a broader context against coarser resolution modelling frameworks. Here we evaluate numerical simulations with the convection‐permitting regional climate model CNRM‐AROME ran at 2.5 km horizontal resolution over a large pan‐Alpine domain in the European Alps, using either the ERA‐Interim or climate model output as boundary conditions. This study analyses annual and seasonal characteristics of 2 m temperature, total precipitation, solid fraction of precipitation and snow depth at the scale of the French Alps under past and future climate conditions. The results are compared with the local reanalysis S2M, and raw or adjusted, with the ADAMONT method, simulations of the regional climate model CNRM‐ALADIN driven either by the ERA‐Interim reanalysis or the CNRM‐CM5 global climate model. The study highlights generally similar differences in past and future climate between the datasets, as well as obstacles to the use of some CNRM‐AROME outputs as they stand. These consist of excessive accumulation of snow on the ground above 1,800 m a.s.l., as well as lower temperature values at same elevations than the S2M reanalysis and the ADAMONT‐adjusted outputs. Besides these obstacles, CNRM‐AROME simulations present several advantages compared to the raw CNRM‐ALADIN outputs. Among them, a significantly smaller cold bias, more realistic values of accumulated precipitations, as well as a better representation of the spatial variability of the different variables investigated, which always stand closer to the reference data than in the CNRM‐ALADIN outputs. As suggested by many studies, CNRM‐AROME could even produce more realistic accumulated precipitations at high elevation than the S2M reanalysis taken as our reference and consequently than the ADAMONT‐adjusted projections, but the lack of a reliable set of high‐resolution observations at high elevation remains an obstacle to their evaluation.

Assessment of the WRF Model as a Guidance Tool Into Cloud Seeding Operations in the United Arab Emirates

AbstractWith the projected expansion of arid/semi‐arid regions in a warming world, precipitation enhancement activities such as cloud seeding will become increasingly popular and relied upon. Due to the inherent costs, a successful planning is crucial, which involves accurate model predictions. In this study, the usefulness of the Weather Research and Forecasting (WRF) model forecasts for guidance into seeding operations in the United Arab Emirates, where seeding activities have been conducted for more than two decades, is assessed. The WRF predictions are compared with ground‐based, satellite‐derived and radar reflectivity data, and in‐situ observations onboard the airplanes used to perform the seeding operations. WRF is found to have higher skill in simulating the observed cloud top pressure/temperature than the cloud fraction, with the model vertical velocity predictions also more skillful than those of the radar reflectivity. A stronger Arabian Heat Low (AHL) in the model leads to drier conditions which, together with a surface cold bias, limits the spatial extent and vertical depth of the simulated convective clouds. Development of convective rolls in the boundary layer is reported in both observations and simulations and their interaction with cold pools from convective clouds promote the development of secondary convection. Sensitivity to the choice of the Planetary Boundary Layer (PBL) scheme is also noticed, with the Yonsei University PBL scheme giving the best performance. When considering the two factors needed for a successful seeding operation that is, the presence of an updraft and clouds, the model‐predicted seeding regions largely match the areas where precipitation was observed. As the proposed WRF set up can be used operationally, the model forecasts will bring added value to the seeding activities in the country.