Cold Bias Research Articles

Dynamical downscaling projections of extreme temperature for the major river basins in China under shared socioeconomic pathway scenarios

AbstractThis study presents an analysis of the changes in extreme temperatures over major river basins in China under different shared socioeconomic pathway (SSP) scenarios based on a set of dynamical downscaling over CORDEX East Asia, performed with the regional climate model RegCM4 at a resolution of 25 km, driven by the global climate model FGOALS‐g3. The results indicated that the dynamical downscaling tended to reduce the warm (cold) biases of the present‐day daily maximum (TXx) and minimum temperatures (TNn) simulated by FGOALS‐g3. Under the SSP scenarios, substantial increases in TXx and TNn and the number of warm days and warm nights throughout China were projected by both models. The average increase in TXx in most of the river basins ranged from 1 to 2°C under the SSP1‐2.6 scenario, from 2 to 3°C under the SSP2‐4.5 scenario, and from 3 to 5°C under the SSP5‐8.5 scenario. Reductions in the number of cold days and cold nights were projected across China, but these reductions were smaller than the increases in the number of warm days and warm nights. The interregional variability in the increases in TXx and TNn was more pronounced in the SSP5‐8.5 scenario and the RegCM4 model than in the other SSP scenarios and the FGOALS‐g3 model, respectively. Larger percentages of the land area and population of China would be affected by greater increases in extreme high temperatures and TNn. Under the SSP2‐4.5 (SSP1‐2.6) scenario, 20–65% (50–95%) of the impacts of extreme temperatures under the SSP5‐8.5 scenario could be avoided across most regions in China. The avoided impacts of warm days and warm nights were greater than those of the other extreme temperature indices. More of the impacts of extreme temperatures could be avoided in the Huai River basin, Yellow River basin, Hai River basin, and Northwest Interior River basin than in the other river basins in China.

Characterising extratropical near‐tropopause analysis humidity biases and their radiative effects on temperature forecasts

AbstractA cold bias in the extratropical lowermost stratosphere in forecasts is one of the most prominent systematic temperature errors in numerical weather prediction models. Hypothesized causes of this bias include radiative effects from a collocated moist bias in model analyses. Such biases would be expected to affect extratropical dynamics and result in the misrepresentation of wave propagation at tropopause level. Here the extent to which these humidity and temperature biases are connected is quantified. Observations from radiosondes are compared to operational analyses and forecasts from the European Centre for Medium‐Range Weather Forecasts (ECMWF) Integrated Forecasting System (IFS) and Met Office Unified Model (MetUM) to determine the magnitude and vertical structure of these biases. Both operational models over‐estimate lowermost stratospheric specific humidity, with a maximum moist bias around 1 km above the tropopause where humidities are around of the observed values on average. This moist bias is already present in the initial conditions and changes little in forecasts over the first five days. Though temperatures are represented well in the analyses, the IFS forecasts anomalously cool in the lower stratosphere, relative to verifying radiosonde observations, by 0.2 K day. The IFS single column model is used to show this temperature change can be attributed to increased long‐wave radiative cooling due to the lowermost stratospheric moist bias in the initial conditions. However, the MetUM temperature biases cannot be entirely attributed to the moist bias, and another significant factor must be present. These results highlight the importance of improving the humidity analysis to reduce the extratropical lowermost stratospheric cold bias in forecast models and the need to understand and mitigate the causes of the moist bias in these models.

Future changes in sub‐seasonal variability of Indian winter precipitation

AbstractThe winter precipitation over north India is due to the passage of synoptic systems known as Western Disturbances (WDs), which are embedded in large‐scale subtropical westerly jet (SWJ). The winter (December, January, February [DJF]) precipitation is vital to maintaining glaciers, sustenance of rivers, replenishing downstream springs, and agriculture. Any change in winter precipitation has a profound impact on agriculture, socio‐economic status, and water security of the nation. Thus, a close understanding of future changes in winter precipitation is critical to the country. The present study evaluates the future changes in sub‐seasonal variability of winter precipitation over north India using ICHEC‐EC‐EARTH‐SMHI‐RCA4 CORDEX‐SA experiment. Wet bias in precipitation and cold bias in temperature are observed in model. Almost all days in the winter season witnessed a shift towards a warm‐wet climate in a future warming scenario. A large amount of precipitation is observed in February in model as well as observation. A 10‐day interval scale spanning during DJF is considered to evaluate these changes. Future changes in winter precipitation are analysed under RCP4.5 and 8.5 scenarios. The percentage change in winter precipitation shows a maximum increase during the first fortnight of December and the first week of February. Precipitation intensity weakens during January. Two hundred hectopascal wind, 850 hPa wind, and moisture flux influence the increase in precipitation over the region.

The Cause of the Large Cold Bias in the Northwestern Pacific Ocean

AbstractCommon biases of climate models in simulating sea surface temperature (SST) greatly limit our understanding of future climate change. The cold SST bias in the northwestern Pacific (NWP), one of the most distinct and persistent biases in all generations of climate models, was previously attributed to the local and remote influences of the Pacific and Atlantic Oceans, but has never been related to the Indian Ocean. By analyzing simulations from 52 state‐of‐the‐art CMIP6 models and a series of model experiments, we find the strong cold bias in the NWP is primarily caused by the weak cold bias in the tropical Indian Ocean (TIO). Eliminating the TIO bias induces a prominent pressure high over the NWP, which favors a SST warming there through the enhancement of warm oceanic Ekman advection. Our results indicate that the large NWP cold bias could be reduced by ∼66% once the weak TIO bias is corrected.

Improved parameterization of snow albedo in Noah coupled with Weather Research and Forecasting: applicability to snow estimates for the Tibetan Plateau

Abstract. Snow albedo is important to the land surface energy balance and to the water cycle. During snowfall and subsequent snowmelt, snow albedo is usually parameterized as functions of snow-related variables in land surface models. However, the default snow albedo scheme in the widely used Noah land surface model shows evident shortcomings in land–atmosphere interaction estimates during snow events on the Tibetan Plateau. Here, we demonstrate that our improved snow albedo scheme performs well after including snow depth as an additional factor. By coupling the Weather Research and Forecasting (WRF) and Noah models, this study comprehensively evaluates the performance of the improved snow albedo scheme in simulating eight snow events on the Tibetan Plateau. The modeling results are compared with WRF run with the default Noah scheme and in situ observations. The improved snow albedo scheme significantly outperforms the default Noah scheme in relation to air temperature, albedo and sensible heat flux estimates by alleviating cold bias estimates, albedo overestimates and sensible heat flux underestimates, respectively. This in turn contributes to more accurate reproductions of snow event evolution. The averaged root mean square error (RMSE) relative reductions (and relative increase in correlation coefficients) for air temperature, albedo, sensible heat flux and snow depth reach 27 % (5 %), 32 % (69 %), 13 % (17 %) and 21 % (108 %), respectively. These results demonstrate the strong potential of our improved snow albedo parameterization scheme for snow event simulations on the Tibetan Plateau. Our study provides a theoretical reference for researchers committed to further improving the snow albedo parameterization scheme.

Evaluation of Climate in CMIP6 Models over Two Third Pole Subregions with Contrasting Circulation Systems

AbstractReliable simulations of historical and future climate are critical to assessing ecological and hydrological responses over the Third Pole (TP). In this study, we evaluate the historical and future temperature and precipitation simulations of 18 models from the Coupled Model Intercomparison Project Phase 6 (CMIP6) in southeastern TP (SETP) and the upstream of the Amu Darya and Syr Darya (UAS) regions, two typical TP subregions dominated by the Indian summer monsoon system and westerlies, respectively. Comparison against station observations suggests that CMIP6 models generally capture the intra-annual variability and spatial pattern of historical climate over both subregions. However, the wetting and cold biases observed in CMIP5 still persist in CMIP6; annual temperature is underestimated by most models and annual precipitation is overestimated by all models. Multi-model average cold biases in SETP and UAS are 1.18°C and 0.32°C, respectively, and wet biases in SETP and UAS are 119% and 46%, respectively. We further analyze climate projections under SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios. Both SETP and UAS subregions are projected to experience significant warming in 2015–2100, with warming trends 34%–42% and 40%–50% higher than the global trend, respectively. Model projections suggest that the warming trend will slow down under SSP1-2.6 and SSP2-4.5 but further intensify under SSP5-8.5 in 2050–2100. Monsoon-dominated SETP is projected to experience a significant wetting trend stronger than UAS over the entire future period, especially in summer (cf. winter in westerlies-dominated UAS). Concurrently, a significant drying trend in summer is found in UAS during 2050–2100 under SSP5-8.5, suggesting the intensified uneven distributions of seasonal precipitation based on projections.

Evaluation of a new 12\xa0km regional perturbed parameter ensemble over Europe

We evaluate a 12-member perturbed parameter ensemble of regional climate simulations over Europe at 12 km resolution, carried out as part of the UK Climate Projections (UKCP) project. This ensemble is formed by varying uncertain parameters within the model physics, allowing uncertainty in future projections due to climate modelling uncertainty to be explored in a systematic way. We focus on present day performance both compared to observations, and consistency with the driving global ensemble. Daily and seasonal temperature and precipitation are evaluated as two variables commonly used in impacts assessments. For precipitation we find that downscaling, even whilst within the convection-parameterised regime, generally improves daily precipitation, but not everywhere. In summer, the underestimation of dry day frequency is worse in the regional ensemble than in the driving simulations. For temperature we find that the regional ensemble inherits a large wintertime cold bias from the global model, however downscaling reduces this bias. The largest bias reduction is in daily winter cold temperature extremes. In summer the regional ensemble is cooler and wetter than the driving global models, and we examine cloud and radiation diagnostics to understand the causes of the differences. We also use a low-resolution regional simulation to determine whether the differences are a consequence of resolution, or due to other configuration differences, with the predominant configuration difference being the treatment of aerosols. We find that use of the EasyAerosol scheme in the regional model, which aims to approximate the aerosol effects in the driving model, causes reduced temperatures by around 0.5 K over Eastern Europe in Summer, and warming of a similar magnitude over France and Germany in Winter, relative to the impact of interactive aerosol in the global runs. Precipitation is also increased in these regions. Overall, we find that the regional model is consistent with the global model, but with a typically better representation of daily extremes and consequently we have higher confidence in its projections of their future change.

The Origin of Systematic Forecast Errors of Extreme 2020 East Asian Summer Monsoon Rainfall in GloSea5

AbstractThis study examined the origin of the systematic underestimation of rainfall anomalies over East Asia during July–August 2020 in operational forecasts. Through partial nudging experiments, we found that the East Asian rainfall anomalies were successfully predicted in GloSea5 with corrected tropical sea surface temperature (SST) forcing. Once the observed SST is applied over the Indian Ocean and tropical central‐eastern Pacific, a low‐level anticyclonic anomaly over the subtropical western Pacific, which transports warm‐moist air from the tropics to increase the East Asian precipitation, is well reproduced as observed. By further separating the SST into climatological and anomalous components, we revealed that the cold and dry mean state bias over the Indian Ocean and central‐eastern Pacific is responsible for the weak anomalous atmospheric teleconnection patterns from the tropics to East Asia. This implies that correcting the model mean climatological fields can directly impact the operational seasonal forecast skill.

Evaluating Variable‐Resolution CESM Over China and Western United States for Use in Water‐Energy Nexus and Impacts Modeling

AbstractClimate models are critical tools to study earth system processes and are often further downscaled to provide refined‐resolution data sets for regional water‐energy impact analyses. In this study, the Variable‐Resolution Community Earth System Model (VR‐CESM), a new dynamical downscaling method, is employed to generate 1/8° simulations for China and the western United States over 1970–2006. Precipitation, temperature, snowpack, radiation, and wind speed are compared with two 1° global climate models, regional climate model ensemble, and reference data sets to evaluate their fidelity. Simulated precipitation skill is dependent upon the reference data sets used, a slight (<+10%) and spatially consistent wet bias in China during summer and the western United States during winter. Precipitation bias in VR‐CESM is the smallest among models and demonstrates the added value provided by resolution refinement. However, temperature biases are generally greater than precipitation due to more complicated interactions among processes. VR‐CESM simulates +2°C warm bias in the low‐elevation regions in China but −2°C cold bias in mountains. However, DJF cold bias (−2.23°C to−3.37°C) and JJA warm bias (+1.13°C to +1.87°C) exist in the western United States. The modeling skills of precipitation and temperature at various topographies are similar in the two evaluated regions. The cold bias was likely affected by downward shortwave radiation deficit (−15%) and slow bias of surface wind speeds (−16% to −29%). Despite the wet and cold bias, snow water equivalent and snow cover are underestimated in mountains. We conclude that VR‐CESM outperforms other models evaluated and provides the best estimation of downscaled climate model data for water‐energy studies.

Cold Season Performance of the NU-WRF Regional Climate Model in the Great Lakes Region

AbstractAs Earth’s largest collection of fresh water, the Laurentian Great Lakes have enormous ecological and socio-economic value. Their basin has become a regional hotspot of climatic and limnological change, potentially threatening its vital natural resources. Consequentially, there is a need to assess the current state of climate models regarding their performance across the Great Lakes region and develop the next generation of high-resolution regional climate models to address complex limnological processes and lake-atmosphere interactions. In response to this need, the current paper focuses on the generation and analysis of a 20-member ensemble of 3-km National Aeronautics and Space Administration (NASA)-Unified Weather Research and Forecasting (NU-WRF) simulations for the 2014-2015 cold season. The study aims to identify the model’s strengths and weaknesses; optimal configuration for the region; and the impacts of different physics parameterizations, coupling to a 1D lake model, time-variant lake-surface temperatures, and spectral nudging. Several key biases are identified in the cold-season simulations for the Great Lakes region, including an atmospheric cold bias that is amplified by coupling to a 1D lake model but diminished by applying the Community Atmosphere Model radiation scheme and Morrison microphysics scheme; an excess precipitation bias; anomalously early initiation of fall lake turnover and subsequent cold lake bias; excessive and overly persistent lake ice cover; and insufficient evaporation over Lakes Superior and Huron. The research team is currently addressing these key limitations by coupling NU-WRF to a 3D lake model in support of the next generation of regional climate models for the critical Great Lakes Basin.

Position, Magnitude, and Size of Warm‐Pool El Niño: Variability, Seasonal Predictability, and Climate Impacts

AbstractThis study surveys the variability and predictability of the position, magnitude, and size of warm‐pool El Niño (WP El Niño), as well as its impacts on the climate. A new detection method for WP El Niño that is flexible regarding its position, magnitude, and size is developed. Thirteen WP El Niño events are found from 1950 to 2018 using this detector. These events have a mean (standard deviation) center position of 31.7 (12.4)° longitude, a mean peak sea surface temperature anomaly (SSTA) of 1.87 (0.62) K, and a zonal size parameter of 19.99 (5.13)° longitude. Also, stronger WP El Niño events tend to be larger and take place farther east. Based on these distributions, a set of model experiments is conducted to evaluate climate sensitivity to these three features. The wintertime North American temperature dipole due to WP El Niño is chosen as an example. Its bearing is linearly controlled by the position of the SSTA field of WP El Niño, and this control is stronger when the SSTA field is stronger or smaller. Predictability of position, magnitude, and size of WP El Niño on seasonal scale by the North American Multi‐Model Ensemble is examined. With a 2‐month lead‐time, the position and magnitude of WP El Niño are predicted accurately, but size tends to be large‐biased. Increasing the lead‐time generally leads to more eastward bias, cold bias, and large bias. Precision of the predictions is also reduced with lead‐time.

Evaluation of 2-m Air Temperature and Surface Temperature from ERA5 and ERA-I Using Buoy Observations in the Arctic during 2010–2020

In data-sparse regions such as the Arctic, atmospheric reanalysis is one of the key tools for understanding rapid climate change at the regional and global scales. The utility of reanalysis datasets based on data assimilation is affected by their accuracy and biases. Therefore, it is important to evaluate their performance. Here, we conduct inter-comparisons of two temperature variables, namely, the 2-m air temperature (Ta) and the surface temperature (Ts), from the widely used ERA-I and ERA5 reanalysis datasets provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) against in situ observations from three international buoy programs (i.e., the International Arctic Buoy Programme (IABP), the Multidisciplinary Drifting Observatory for the Study of Arctic Climate (MOSAiC), and the Cold Regions Research and Engineering Laboratory (CRREL)) during 2010–2020 in the Arctic. Overall, the results show that both the ERA-I and ERA5 were well correlated with the buoy observations, with the highest correlation coefficient reaching 0.98. There were generally warm Ta biases for both ERA-I (2.27 ± 3.33 °C) and ERA5 (2.34 ± 3.22 °C) when compared with more than 3000 matching pairs of daily buoy observations. The warm Ta biases of both reanalysis datasets exhibited seasonal variations, reaching the maximum of 3.73 ± 2.84 °C in April and the minimum of 1.36 ± 2.51 °C in September. For Ts, both ERA-I and ERA5 exhibited good consistencies with the buoy observations, but have higher amplitude biases compared with those for Ta, with generally negative biases of −4.79 ± 4.86 °C for ERA-I and −4.11 ± 3.92 °C for ERA5. For both reanalysis datasets, the largest bias of Ts (−11.18 ± 3.08 °C) occurred in December, while the biases were rather small (less than −3 °C) in the warmer months (April to October). The cold Ts biases for ERA-I and ERA5 were probably overestimated due to the location of the surface temperature sensors on the buoys, which may have been affected by snow cover. Both the Ta and Ts biases varied for different buoy programs and different sea ice concentration conditions, yet they exhibited similar trends.

Simulations of the East Asian Winter Monsoon on Subseasonal to Seasonal Time Scales Using the Model for Prediction Across Scales

The Model for Prediction Across Scales (MPAS) is used to simulate the East Asian winter monsoon (EAWM) over the 2011–2020 winter. The 45 day hindcasts are made with 30 km horizontal resolution and constructed to a time-lagged ensemble system. The climatology, the major modes of EAWM variability, and the blocking activities are examined. The evaluation results reveal that MPAS can simulate the climatologic characteristics of EAWM reasonably, with a surface cold bias of 4% and a positive rainfall bias of 9% over East Asia. MPAS can perform skillfully in the forecasts of surface temperature probability of East Asia and is more reliable in detecting below normal and above normal events. The features that influence the EAWM variability are also analyzed. MPAS simulates reasonably in the occurrence frequency of blocking high in both locations and duration time. The empirical orthogonal function analysis also shows that MPAS can capture the two major modes of the surface temperature of EAWM. On the other hand, it is also found that a biased sea surface temperature may modify the circulations over the Western Pacific and affect the simulated occurrence frequency of cold events near Taiwan during winter.

The ENEA-REG system (v1.0), a multi-component regional Earth system model: sensitivity to different atmospheric components over the Med-CORDEX (Coordinated Regional Climate Downscaling Experiment) region

Abstract. In this study, a new regional Earth system model is developed and applied to the Med-CORDEX (Coordinated Regional Climate Downscaling Experiment) region. The ENEA-REG system is made up of two interchangeable regional climate models as atmospheric components (RegCM, REGional Climate Model, and WRF, Weather Research and Forecasting), a river model (Hydrological Discharge, HD), and an ocean model (Massachusetts Institute of Technology General Circulation Model, MITgcm); processes taking place at the land surface are represented within the atmospheric models with the possibility to use several land surface schemes of different complexity. The coupling between these components is performed through the RegESM driver. Here, we present and describe our regional Earth system model and evaluate its components using a multidecadal hindcast simulation over the period 1980–2013 driven by ERA-Interim reanalysis. We show that the atmospheric components correctly reproduce both large-scale and local features of the Euro-Mediterranean climate, although we found some remarkable biases: in particular, WRF has a significant cold bias during winter over the northeastern bound of the domain and a warm bias in the whole continental Europe during summer, while RegCM overestimates the wind speed over the Mediterranean Sea. Similarly, the ocean component correctly reproduces the analyzed ocean properties with performances comparable to the state-of-art coupled regional models contributing to the Med-CORDEX initiative. Our regional Earth system model allows studying the Euro-Mediterranean climate system and can be applied to both hindcast and scenario simulations.

Comparison of kilometre and sub‐kilometre scale simulations of a foehn wind event over the Larsen C Ice Shelf, Antarctic Peninsula using the Met Office Unified Model (MetUM)

AbstractA foehn event on 27 January 2011 over the Larsen C Ice Shelf (LCIS), Antarctic Peninsula and its interaction with an exisiting ground‐based cold‐air pool is simulated using the MetUM atmospheric model at kilometre and sub‐kilometre scale grid spacing. Atmospheric model simulations at kilometre grid scales are an important tool for understanding the detailed circulation and temperature structure over the LCIS, especially the occurrence of foehn‐induced surface melting, erosion of cold‐air pools, and low‐level wind jets (so‐called foehn jets). But whether there is an improvement/convergence in the model representation of these features at sub‐kilometre grid scales has yet to be established. The foehn event was simulated at grid spacings of 4, 1.5 and 0.5 km, with the results compared to automatic weather station and radiosonde measurements. The features commonly associated with foehn, such as a leeside hydraulic jump and enhanced leeside warming, were comparatively insensitive to resolution in the 4 to 0.5 km range, although the 0.5 km simulation shows a slightly sharper and larger hydraulic jump. By contrast, during the event the simulation of fine‐scale foehn jets above the cold‐air pool showed considerable dependence on grid spacing, although no evidence of convergence at higher resolution. During the foehn event, the MetUM model is characterised by a nocturnal cold bias of around 8 °C and an underestimate of the near‐surface stability of the cold‐air pool, neither of which improved with increased resolution. This finding identifies a key model limitation, at both kilometre and sub‐kilometre scales, to realistically capture the vertical mixing in the boundary layer and its impact on thermodynamics, through either daytime heating from below or the downward penetration of foehn jet winds from above. Detailed model‐resolved foehn jet dynamics thus plays a crucial role in controlling the near‐surface temperature structure over the LCIS, as well as sub‐grid turbulent mixing.

Arctic Warming Revealed by Multiple CMIP6 Models: Evaluation of Historical Simulations and Quantification of Future Projection Uncertainties

AbstractThe Arctic has experienced a warming rate higher than the global mean in the past decades, but previous studies show that there are large uncertainties associated with future Arctic temperature projections. In this study, near-surface mean temperatures in the Arctic are analyzed from 22 models participating in phase 6 of the Coupled Model Intercomparison Project (CMIP6). Compared with the ERA5 reanalysis, most CMIP6 models underestimate the observed mean temperature in the Arctic during 1979–2014. The largest cold biases are found over the Greenland Sea the Barents Sea, and the Kara Sea. Under the SSP1-2.6, SSP2-4.5, and SSP5-8.5 scenarios, the multimodel ensemble mean of 22 CMIP6 models exhibits significant Arctic warming in the future and the warming rate is more than twice that of the global/Northern Hemisphere mean. Model spread is the largest contributor to the overall uncertainty in projections, which accounts for 55.4% of the total uncertainty at the start of projections in 2015 and remains at 32.9% at the end of projections in 2095. Internal variability uncertainty accounts for 39.3% of the total uncertainty at the start of projections but decreases to 6.5% at the end of the twenty-first century, while scenario uncertainty rapidly increases from 5.3% to 60.7% over the period from 2015 to 2095. It is found that the largest model uncertainties are consistent cold bias in the oceanic regions in the models, which is connected with excessive sea ice area caused by the weak Atlantic poleward heat transport. These results suggest that large intermodel spread and uncertainties exist in the CMIP6 models’ simulation and projection of the Arctic near-surface temperature and that there are different responses over the ocean and land in the Arctic to greenhouse gas forcing. Future research needs to pay more attention to the different characteristics and mechanisms of Arctic Ocean and land warming to reduce the spread.

Late Spring and Summer Subseasonal forecasts in the Northern Hemisphere midlatitudes: biases and skill in the ECMWF model

AbstractSubseasonal forecasts lie between medium-range and seasonal time scales with an emerging attention due to the relevance in society and by the scientific challenges involved. This study aims to (i) evaluate the development of systematic errors with lead time in the European Centre for Medium-Range Weather Forecasts (ECMWF) ensemble forecasts of surface-related variables during late spring and summer, and (ii) investigate potential relationships between the systematic errors and predictive skill. The evaluation is performed over the northern hemisphere midlatitudes, focusing on several regions with different climate characteristics. The results indicate five key bias patterns: (i) cold bias of daily maximum temperature (mx2t) in the April-May forecasts at all lead times in most regions; (ii) central North America with a warm bias mostly in the daily minimum temperature (mn2t); (iii) East of Caspian Sea region with a warm and dry bias; (iv) Western and Mediterranean Europe with a cold bias in mn2t mainly in April-May forecasts and (v) continental Europe with a cold bias in the mx2t and warm bias of mn2t in the June-July forecasts. We also found substantial deviations of soil moisture and terrestrial water storage variation in most regions compared to ECMWF ERA5 reanalysis. Despite the large differences in the systematic error characteristics among the different regions, there is little relation to the skill of the subseasonal forecasts. The systematic temperature biases require further attention from model developers as diurnal cycle improvements could enhance some of the potential predictability coming from the long-memory effect of soil moisture.

An Evaluation of Temperature-Based Agricultural Indices Over Korea From the High-Resolution WRF Simulation

This study evaluates the performance of dynamical downscaling of global prediction generated from the NOAA Climate Forecast System (CFSv2) at subseasonal time-scale against densein-situobservational data in Korea. The Weather Research and Forecasting (WRF) double-nested modeling system customized over Korea is adopted to produce very high resolution simulation that presumably better resolves geographically diverse climate features. Two ensemble members of CFSv2 starting with different initial conditions are downscaled for the summer season (June-July-August) during past 10-year (2011–2020). The comparison of simulations from the nested domain (5 km resolution) of WRF and driving CFSv2 (0.5°) clearly demonstrates the manner in which dynamical downscaling can drastically improve daily mean temperature (Tmean) and daily maximum temperature (Tmax) in both quantitative and qualitative aspects. The downscaled temperature not only better resolves the regional variability strongly tied with topographical elevation, but also substantially lowers the systematic cold bias seen in CFSv2. The added value from the nested domain over CFSv2 is far more evident in Tmax than in Tmean, which indicates a skillful performance in capturing the extreme events. Accordingly, downscaled results show a reasonable performance in simulating the plant heat stress index that counts the number of days with Tmax above 30°C and extreme degree days that accumulate temperature exceeding 30°C using hourly temperature. The WRF simulations also show the potential to capture the variation of Tmean-based index that represents the accumulation of heat stress in reproductive growth for the mid-late maturing rice cultivars in Korea. As the likelihood of extreme hot temperatures is projected to increase in Korea, the modeling skill to predict the ago-meteorological indices measuring the effect of extreme heat on crop could have significant implications for agriculture management practice.

Development and evaluation of spectral nudging strategy for the simulation of summer precipitation over the Tibetan Plateau using WRF (v4.0)

Abstract. Precipitation is the key component determining the water budget and climate change of the Tibetan Plateau (TP) under a warming climate. This high-latitude region is regarded as “the Third Pole” of the Earth and the “Asian Water Tower” and influences the eco-economy of downstream regions. However, the intensity and diurnal cycle of precipitation are inadequately depicted by current reanalysis products and regional climate models (RCMs). Spectral nudging is an effective dynamical downscaling method used to improve precipitation simulations of RCMs by preventing simulated fields from drifting away from large-scale reference fields, but the most effective manner of applying spectral nudging over the TP is unclear. In this paper, the effects of spectral nudging parameters (e.g., nudging variables, strengths, and levels) on summer precipitation simulations and associated meteorological variables were evaluated over the TP. The results show that using a conventional continuous integration method with a single initialization is likely to result in the over-forecasting of precipitation events and the over-forecasting of horizontal wind speeds over the TP. In particular, model simulations show clear improvements in their representations of downscaled precipitation intensity and its diurnal variations, atmospheric temperature, and water vapor when spectral nudging is applied towards the horizontal wind and geopotential height rather than towards the potential temperature and water vapor mixing ratio. This altering of the spectral nudging method not only reduces the wet bias of water vapor in the lower troposphere of the ERA-Interim reanalysis (when it is used as the driving field) but also alleviates the cold bias of atmospheric temperatures in the upper troposphere, while maintaining the accuracy of horizontal wind features for the regional model field. The conclusions of this study imply how driving field errors affect model simulations, and these results may improve the reliability of RCM results used to study the long-term regional climate change.

Estimating near-surface climatology of multi-reanalyses over the Greenland Ice Sheet

This study uses meteorological records from Automatic Weather Stations (AWSs) to estimate the performance of global reanalysis products for monthly air temperature, relative humidity and wind speed over the Greenland Ice Sheet (GrIS). These products include the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA5), ECMWF Interim Reanalysis (ERAI), Modern-Era Retrospective Analysis for Research and Applications, Version 2 (MERRA-2), Climate Forecast System Reanalysis Version 1/Climate Forecast System Version 2 (CFSRv1/CFSRv2), and Japanese 55-year Reanalysis (JRA-55). The global reanalysis products generally perform better in summer than in winter, and their qualities vary by glaciological regime. No reanalysis is clearly identified as the optimal dataset for all meteorological parameters, seasons and regions. For all reanalyses, warm biases are observed in the accumulation zone, but cold biases are observed in the ablation area of the GrIS. ERAI, ERA5 and JRA-55 underestimate relative humidity during any month. While MERRA-2 overestimates wind speeds, underestimates are found for the other reanalyses excluding JRA-55 during all months. Despite the robust agreement between the AWS time series for all three variables and each reanalysis product averaged over the ice sheet, sudden jumps occur in annual mean wind speed in CFSR, and in annual relative humidity in JRA-55 from 2010 to 2011.