Cold Bias Research Articles

Understanding Surface Air Temperature Cold Bias Over China in CMIP6 Models

AbstractThe systematic simulated cold biases in models for surface air temperature (SAT) over China have been under discussion for a long time, but the related attribution is still unclear. In this study, we investigate the main contributors and relevant physical processes of SAT biases over China based on 31 models participating in the sixth phase of the Coupled Model Intercomparison Project from a surface energy budget perspective. On annual and seasonal scales, less downward clear‐sky longwave radiation in the models, which is due to the underestimated tropospheric temperature and water vapor amount, is the main cause of cold biases over the Tibetan Plateau, Tarim Basin, Sichuan Basin, and most of eastern China. Meanwhile, the simulation biases in surface albedo and sensible heat flux induce cold biases depending on regions and seasons. In winter and spring, the overestimated surface albedo in association with more snow cover leads to prevalent cold biases over the western Tibetan Plateau and Northeast China. Excessive sensible heat release caused by the overestimated ground–air temperature difference facilitates SAT underestimation in the Sichuan Basin during summer and autumn. In addition, the cold biases in winter and spring are larger than those in summer and autumn, due to the stronger snow cover overestimation and tropospheric temperature underestimation.

Atmospheric response to cold wintertime Tibetan Plateau conditions over eastern Asia in climate models

Abstract. Central Asian orography (namely the Tibetan and Mongolian plateaux) sets important features of the winter climate over eastern Asia and the Pacific. By deflecting the mid-latitude jet polewards it contributes to the formation of the Siberian high and, on the lee side, to the advection of dry cold continental air over the eastern Asian coast and the Pacific Ocean, where atmospheric instability and cyclogenesis thrive. While the mechanic forcing by the orography is assessed in a number of modelling studies, it is still not clear how near-surface temperature over the two most prominent orographic barriers of the central Asian continent – the Tibetan and Mongolian plateaux – influences the winter climate. The problem is particularly relevant in view of a well-known cold bias in state-of-the-art climate models in proximity to the Tibetan Plateau, likely related to the modelling of land processes and land–atmosphere interaction over complex orography. Here we take advantage of the large spread in near-surface temperature over the central Asian plateaux within the Coupled Model Intercomparison Project Phase 6 (CMIP6) to study how colder-than-average Asian plateau temperatures impact the atmospheric circulation. Based on composites of the CMIP6 models' climatologies showing the coldest Tibetan Plateau conditions, we find that such negative temperature anomalies appear to amplify the atmospheric response to orography, with an intensification of the eastern Asian winter monsoon and of the equatorward flank of the Pacific jet. The results of the CMIP6 composite analysis are supported by experiments run with an intermediate-complexity atmospheric model, forced by a similar pattern of cold surface temperatures over the central Asian plateaux. Within this setting, the relative influence of the Tibetan and the Mongolian Plateau surface conditions is analysed. Based on the results reported in this work we project that advances in the modelling of the land energy budget over the elevated regions of central Asia could improve the simulation of the climate in the Asian–Pacific sector climate, together with the reliability of climate projections and the performance of shorter-term forecasts.

Weakening of springtime Arctic ozone depletion with climate change

Abstract. In the Arctic stratosphere, the combination of chemical ozone depletion by halogenated ozone-depleting substances (hODSs) and dynamic fluctuations can lead to severe ozone minima. These Arctic ozone minima are of great societal concern due to their health and climate impacts. Owing to the success of the Montreal Protocol, hODSs in the stratosphere are gradually declining, resulting in a recovery of the ozone layer. On the other hand, continued greenhouse gas (GHG) emissions cool the stratosphere, possibly enhancing the formation of polar stratospheric clouds (PSCs) and, thus, enabling more efficient chemical ozone destruction. Other processes, such as the acceleration of the Brewer–Dobson circulation, also affect stratospheric temperatures, further complicating the picture. Therefore, it is currently unclear whether major Arctic ozone minima will still occur at the end of the 21st century despite decreasing hODSs. We have examined this question for different emission pathways using simulations conducted within the Chemistry-Climate Model Initiative (CCMI-1 and CCMI-2022) and found large differences in the models' ability to simulate the magnitude of ozone minima in the present-day climate. Models with a generally too-cold polar stratosphere (cold bias) produce pronounced ozone minima under present-day climate conditions because they simulate more PSCs and, thus, high concentrations of active chlorine species (ClOx). These models predict the largest decrease in ozone minima in the future. Conversely, models with a warm polar stratosphere (warm bias) have the smallest sensitivity of ozone minima to future changes in hODS and GHG concentrations. As a result, the scatter among models in terms of the magnitude of Arctic spring ozone minima will decrease in the future. Overall, these results suggest that Arctic ozone minima will become weaker over the next decades, largely due to the decline in hODS abundances. We note that none of the models analysed here project a notable increase of ozone minima in the future. Stratospheric cooling caused by increasing GHG concentrations is expected to play a secondary role as its effect in the Arctic stratosphere is weakened by opposing radiative and dynamical mechanisms.

Evaluating the impact of enhanced horizontal resolution over the Antarctic domain using a variable-resolution Earth system model

Abstract. Earth system models are essential tools for understanding the impacts of a warming world, particularly on the contribution of polar ice sheets to sea level change. However, current models lack full coupling of the ice sheets to the ocean and are typically run at a coarse resolution (1∘ grid spacing or coarser). Coarse spatial resolution is particularly a problem over Antarctica, where sub-grid-scale orography is well-known to influence precipitation fields, and glacier models require high-resolution atmospheric inputs. This resolution limitation has been partially addressed by regional climate models (RCMs), which must be forced at their lateral and ocean surface boundaries by (usually coarser) global atmospheric datasets, However, RCMs fail to capture the two-way coupling between the regional domain and the global climate system. Conversely, running high-spatial-resolution models globally is computationally expensive and can produce vast amounts of data. Alternatively, variable-resolution grids can retain the benefits of high resolution over a specified domain without the computational costs of running at a high resolution globally. Here we evaluate a historical simulation of the Community Earth System Model version 2 (CESM2) implementing the spectral element (SE) numerical dynamical core (VR-CESM2) with an enhanced-horizontal-resolution (0.25∘) grid over the Antarctic Ice Sheet and the surrounding Southern Ocean; the rest of the global domain is on the standard 1∘ grid. We compare it to 1∘ model runs of CESM2 using both the SE dynamical core and the standard finite-volume (FV) dynamical core, both with identical physics and forcing, including prescribed sea surface temperatures (SSTs) and sea ice concentrations from observations. Our evaluation reveals both improvements and degradations in VR-CESM2 performance relative to the 1∘ CESM2. Surface mass balance estimates are slightly higher but within 1 standard deviation of the ensemble mean, except for over the Antarctic Peninsula, which is impacted by better-resolved surface topography. Temperature and wind estimates are improved over both the near surface and aloft, although the overall correction of a cold bias (within the 1∘ CESM2 runs) has resulted in temperatures which are too high over the interior of the ice sheet. The major degradations include the enhancement of surface melt as well as excessive cloud liquid water over the ocean, with resultant impacts on the surface radiation budget. Despite these changes, VR-CESM2 is a valuable tool for the analysis of precipitation and surface mass balance and thus constraining estimates of sea level rise associated with the Antarctic Ice Sheet.

Assessing high resolution thermal monitoring of complex intertidal environments from space: The case of ECOSTRESS at Rias Baixas, NW Iberia

Currently, thermal sensors on satellites and other orbital platforms with high spatial resolution designed to monitor the oceans are often insufficient to assess the surface temperature of small bodies of water or within the coastal boundary layer. As the accuracy and sensitivity of remote sensing satellites improve, the demand for more accurate and up-to-date basic data sets for calibration increases. The quality of the thermal data collected by ECOSTRESS on the International Space Station allows the characterization of thermal stress levels in coastal ecosystems with a high spatial resolution of 70 m and a return time from hours to 5 days. This study focused on the calibration of ECOSTRESS estimates with in situ surface temperature data from sensors installed at 3 cm depth in the sediment on the intertidal muddy sandflats of three of the Rías Baixas in Galicia, NW Iberian Peninsula, from 2019 to 2021. A final number of 45 ECOSTRESS temperature images were analyzed. From these, 20% contained substantial georeferencing errors which had to be corrected manually with GIS software tools. We applied the Fourier's law of Heat Conduction to derive surface estimates from loggers sub-surface measurements that could be directly compared with ECOSTRESS data. Overall, a good calibration which explained more than 80% of ECOSTRESS temperature estimates for the whole dataset was obtained, but with an intrinsic cold bias around 1.39 °C. When temperatures at a depth of 3 cm were used, the linear fit became worse and the negative bias increased to 1.49 °C. Closer inspection revealed that night measurements were responsible for this larger deviation, as ECOSTRESS estimates became much colder compared to within sediment measurements because of the combined effect of the instrument intrinsic bias and nocturnal surface cooling. The best calibration was obtained when surface estimates were calculated just for the nighttime, as the cold bias decreased to 0.93 °C. More importantly, during hot daytime conditions in emersion above 20 °C, ECOSTRESS data matched surface temperature estimates, probably because of a better performance of ECOSTRESS algorithm at dry surfaces with lower emissivity. Thus, during the most ecologically relevant periods when high temperatures could drive thermal stress in many commercially-important bivalve species, ECOSTRESS provides accurate surface estimates that can be used to derive sub surface temperatures at the depths at which the different burrowing organisms live. We thus conclude that this instrument constitutes an important global tool to examine thermal stress at an unprecedented spatial scale for complex sea-land boundary systems.

Quantifying the Rightward Bias Extent of Tropical Cyclones' Cold Wakes

AbstractAlthough the cold wake of tropical cyclones (TCs) is recognized to be typically rightward biased relative to TC track (in the North Hemisphere), it remains unclear to what extent the rightward bias should be for specified TCs. Based on numerical simulations and observational statistics, this study shows that the distance of cold wake rightward bias is closely related to latitude, translation speed and radius of maximum wind (RMW) of a TC, but is independent from maximum surface wind and radial wind profile outside the RMW, namely TC intensity and size. A semi‐empirical equation is derived to represent the rightward bias extent of cold wakes. For TCs with slower translation speed, smaller RMW, or at higher latitudes, the cold wakes are less asymmetric relative to the TC track. Atmospheric simulations also show that the TC tends to be weaker and more asymmetric as the cold wake is closer to TC center.

Reducing the Cold Bias of the WRF Model Over the Tibetan Plateau by Implementing a Snow Coverage‐Topography Relationship and a Fresh Snow Albedo Scheme

AbstractMost climate models show systematic cold biases during snow‐covered period over the Tibetan Plateau (TP), which is associated with snow and surface albedo overestimations. In this work, a snow cover fraction (SCF) scheme and a recently developed albedo scheme for shallow snow are implemented in the Noah‐MP land surface model coupled with the Weather Research and Forecasting (WRF) model. The SCF scheme introduces subgrid orographic variability to reduce the SCF, and the shallow‐snow albedo scheme parameterizes the fresh‐snow albedo as a function of the snow depth (SD). Evaluations by remote sensing data show that both schemes can effectively alleviate the overestimation of the simulated surface albedo, SCF, snow water equivalent, and SD over the TP. The reductions in the modeled SCF and snow albedo directly lead to lower surface albedo values and thus more surface solar radiation absorption, which accelerates snow melting and causes surface warming effects. Further comparisons with Moderate Resolution Imaging Spectroradiometer data and station observations show that both schemes can significantly reduce the cold biases in the surface skin temperature (from −4.39°C to 0.19°C for the TP mean) and 2‐m air temperature (from −4.48°C to −1.05°C for the station mean) during the cold season (October to May of next year) in the study region. This work provides guidance for advancing the snow‐related physics in climate models and the improved WRF model could facilitate weather forecasting and climate prediction for the plateau region.

Future heat extremes and impacts in a convection-permitting climate ensemble over Germany

Abstract. Heat extremes and associated impacts are considered the most pressing issue for German regional governments with respect to climate adaptation. We explore the potential of a unique high-resolution, convection-permitting (2.8 m), multi-GCM (global climate model) ensemble with COSMO-CLM (Consortium for Small-scale Modeling Climate Limited-area Modelling) regional simulations (1971–2100) over Germany regarding heat extremes and related impacts. We find a systematically reduced cold bias especially in summer in the convection-permitting simulations compared to the driving simulations with a grid size of 7 km and parametrized convection. The projected increase in temperature and its variance favors the development of longer and hotter heat waves, especially in late summer and early autumn. In a 2 ∘C (3 ∘C) warmer world, a 26 % (100 %) increase in the heat wave magnitude index is anticipated. Human heat stress (universal thermal climate index (UTCI) > 32 ∘C) and region-specific parameters tailored to climate adaptation revealed a dependency on the major landscapes, resulting in significantly higher heat exposure in flat regions such as the Rhine Valley, accompanied by the strongest absolute increase. A nonlinear, exponential increase is anticipated for parameters characterizing strong heat stress (UTCI > 32 ∘C, tropical nights, very hot days). Providing region-specific and tailored climate information, we demonstrate the potential of convection-permitting simulations to facilitate improved impact studies and narrow the gap between climate modeling and stakeholder requirements for climate adaptation.

Regional vs. global temperature calibrations for lacustrine BrGDGTs in the North American (sub)tropics: Implications for their application in paleotemperature reconstructions

Branched glycerol dialkyl glycerol tetraethers (brGDGTs) have shown great promise in lacustrine temperature reconstructions across different continents. While brGDGTs have been reported from many different regions and global brGDGT-temperature calibrations have been developed with various methods, southern North America remains an understudied area with little available data. In this study, we analyzed 101 lake surface sediment samples across Mexico and Central America and compared their distributions with those in other lacustrine systems. Nine major brGDGTs were found in all samples. We investigated the relationships between the distribution of the fractional abundances of the nine major brGDGTs and temperature and developed regional calibrations for Mean Annual Temperature using three different approaches, including a novel machine learning method – Ridge Regression. All the regional calibrations provide similar results with very close error ranges (RMSE = 3.1 °C). The majority of global brGDGT-temperature calibrations tend to reconstruct lower temperatures when it is below 15 °C. Interestingly, regional brGDGT calibrations appear to reduce the “cold bias”, but the various global and regional calibrations tested here are not significantly different in their predictive capability.

Performance of the WRF Model at the Convection‐Permitting Scale in Simulating Snowfall and Lake‐Effect Snow Over the Tibetan Plateau

AbstractThis study investigated the performance of the Weather Research Forecasting (WRF) model at 4‐km horizontal grid spacing in simulating precipitation, 2 m air temperature (T2), snowfall, and lake‐effect snow (October 4–8, 2018) over the Tibetan Plateau (TP). Multiple simulations with different physical parameterization schemes (PPSs), including two planetary boundary layer schemes (Yonsei University and Mellor–Yamada–Janjic), no cumulus and multi‐scale Kain‐Fritsch, two land surface models (Noah and Noah‐MP), and two microphysics schemes (Thompson and Milbrandt), were conducted and compared. Compared with gauge observations, all PPSs simulate mean daily precipitation with mean relative errors (MREs) of 27.7%–53.6%. Besides, spatial correlation coefficients (SCCs) between simulated and observed mean daily precipitation range from 0.56 to 0.71. For simulations of T2, all PPSs perform similarly well, even though the mean cold biases are up to about 3°C. Meanwhile, all PPSs exhibit acceptable performance in simulating spatial distributions of snow depth, snow cover, and snowfall amount, with SCCs of 0.37–0.65 between simulations and observations. However, the WRF simulations significantly overestimate snow depth (∼0.4 cm mean error) and snowfall amount (MREs >372%). The Milbrandt scheme slightly outperforms the other PPSs in simulating snow‐related variable magnitudes. Due to their inaccurate temperature and airflow modeling over the lake surface and its surroundings, none of the WRF simulations well reproduce the characteristics that more snow occurs over the lake and downwind area. Overall, this study provides a useful reference for future convection‐permitting climate modeling of snow or other extreme events when using the WRF model in the TP and other alpine regions.

Improving the Forecasts of Surface Latent Heat Fluxes and Surface Air Temperature in the GRAPES Global Forecast System

The GRAPES (Global/Regional Assimilation and Prediction System) global medium-range forecast system (GRAPES_GFS) is a new generation numerical weather forecast model developed by the China Meteorological Administration (CMA). However, the forecasts of surface latent heat fluxes and surface air temperature have systematic biases, which affect the forecasts of atmospheric dynamics by modifying the lower boundary conditions and degrading the application of GRAPES_GFS since the 2 m air temperature is one of the key components of weather forecast products. Here, we add a soil resistance term to reduce soil evaporation, which ultimately reduces the positive forecast bias of the land surface latent heat flux. We also reduce the positive forecast bias of the ocean surface latent heat flux by considering the effect of salinity in the calculation of the ocean surface vapor pressure and by adjusting the parameterizations of roughness length for the exchanges in momentum, heat, and moisture between the ocean surface and atmosphere. Moreover, we modify the parameterization of the roughness length for the exchanges in heat and moisture between the land surface and atmosphere to reduce the cold bias of the nighttime 2 m air temperature forecast over areas with lower vegetation height. We also consider the supercooled soil water to reduce the warm forecast bias of the 2 m air temperature over northern China during winter. These modified parameterizations are incorporated into the GRAPES_GFS and show good performance based on a set of evaluation experiments. This paper highlights the importance of the representations of the land/ocean surface and boundary layer processes in the forecasting of surface heat fluxes and 2 m air temperature.

Sr/Ca-temperature calibration for the coral Montastraea cavernosa

The Sr/Ca ratio in coral skeletons is an important proxy for the reconstruction of sea surface temperatures (SST) of the environment and helps our knowledge about SST variability in the tropics. In the western tropical South Atlantic where the species Montastraea cavernosa is one of the most important reef-builders, only a few species have been evaluated as coral-based archives for environmental changes. Here we evaluate the Sr/Ca ratio recorded in the thecal wall and other skeletal structures of the species M. cavernosa and its implications for paleothermometry investigations. Sampling along the thecal wall generated a record of Sr/Ca ratios with well-defined annual cycles, but also with noisy intervals. A linear regression of the most recent 8 years of well-defined Sr/Ca ratios and SST results in the calibration equation Sr/Ca (mmol/mol) = −0.040 (±0.005) * SST (°C) + 10.15 (±0.15). This is the first calibration equation for this species. The skeletal elements of M. cavernosa were characterized by heterogeneous Sr/Ca ratios, with higher values for the columellae and septum, which could potentially generate a cold bias in reconstructed SST of up to 7.5 °C. The variability of Sr/Ca ratios in these structures overwhelms the annual seasonality of Sr/Ca ratios in the thecal wall, and the difference in Sr/Ca ratios observed from distinct skeletal structures may be related to biological or vital effects influencing the uptake of Sr into different coral structures. Furthermore, improving sampling techniques for M. cavernosa should be considered in future studies for the successful recovery of robust geochemical signals, eventually expanding the number of species used as natural archives in the western tropical South Atlantic.

Evaluation of Reanalysis Estimates of Precipitation, Radiation, and Temperature over Benin (West Africa)

Abstract In West Africa, climatic data issues, especially availability and quality, remain a significant constraint to the development and application of distributed hydrological modeling. As alternatives to ground-based observations, reanalysis products have received increasing attention in recent years. This study aims to evaluate three reanalysis products, namely, ERA5, Water and Global Change (WATCH) Forcing Data (WFD) ERA5 (WFDE5), and MERRA-2, from 1981 to 2019 to determine their ability to represent four hydrological climates variables over a range of space and time scales in Benin. The variables from the reanalysis products are compared with point station databased metrics Kling–Gupta efficiency (KGE), mean absolute error (MAE), correlation, and relative error in precipitation annual (REPA). The results show that ERA5 presents a better correlation for annual mean temperature (between 0.74 and 0.90) than do WFDE5 (0.63–0.78) and MERRA-2 (0.25–0.65). Both ERA5 and WFDE5 are able to reproduce the observed upward trend of temperature (0.2°C decade−1) in the region. We noted a systematic cold bias of ∼1.3°C in all reanalyses except WFDE5 (∼0.1°C). On the monthly time scale, the temperature of the region is better reproduced by ERA5 and WFDE5 (KGE ≥ 0.80) than by MERRA-2 (KGE < 0.5). At all time scales, WFDE5 produces the best MAE scores for longwave (LW) and shortwave (SW) radiation, followed by ERA5. WFDE5 also provides the best estimates for the annual precipitation (REPA ∈ ]−25, 25[ and KGE ≥ 50% at most stations). ERA5 produces similar results, but MERRA-2 performs poorly in all the metrics. In addition, ERA5 and WFDE5 reproduce the bimodal rainfall regime in southern Benin, unlike MERRA-2, but all products have too many small rainfall events.

Evaluating ENSO's Spatiotemporal Complexity in the CWB CFS 1‐Tiered Model Hindcasts

AbstractUsing hindcasts produced by a coupled climate model, this study evaluates whether the model can forecast the observed spatiotemporal complexity in the El Niño−Southern Oscillation (ENSO) during the period 1982−2011: the eastern Pacific (EP), central Pacific‐I (CP‐I) and ‐II (CP‐II) types of El Niño, and the multi‐year evolution events of El Niño occurred in 1986–1988 (i.e., 1986/87/88 El Niño) and La Niña occurred in 1998–2000 (i.e., 1998/99/00 La Niña). With regard to the spatial complexity, it is found that the CP‐I type of El Niño is the easiest to hindcast, the CP‐II is second, and the EP is most difficult to hindcast as its amplitude is significantly underestimated in the model used here. The model deficiency in hindcasting the EP El Niño is related to a warm bias in climatological sea surface temperatures (SSTs) in the tropical eastern Pacific. This warm bias is related to model biases in the strengths of the Pacific Walker circulation and South Pacific high, both of which are notably weaker than observed. As for the temporal complexity, the model successfully hindcasts the multi‐year evolution of the 1998/99/00 La Niña but fails to accurately hindcast the 1986/87/88 El Niño. This contrasting model performance in hindcasting multi‐year events is found to be related to a cold bias in climatological SSTs in the tropical central Pacific. This cold bias result enables the model La Niña, but not El Niño, to activate intrabasin tropical‒subtropical interactions associated with the Pacific Meridional Mode that produce the multi‐year evolution pattern.

Diagnosing the Radiation Biases in Global Climate Models Using Radiative Kernels

AbstractRadiation energy balance at the top of the atmosphere (TOA) is a critical boundary condition for the Earth climate. It is essential to validate it in the global climate models (GCM) on both global and regional scales. However, the comparison of overall radiation field is known to conceal compensating errors. Here we use a new set of radiative kernels to diagnose the radiation biases caused by different geophysical variables in the GCMs of the Coupled Model Intercomparison Project. We find although clouds remain a primary cause of radiation biases, the radiation biases caused by non‐cloud variables are of comparable magnitudes. Many GCMs have a cold air temperature bias and a moist tropospheric humidity bias, which lead to considerable biases in TOA radiation budget but are compensated by cloud biases. These findings signify the importance of validating the GCM simulations in terms of both the overall and component radiation biases.

Assessment and improvement of RegCM 4.6 coupled with CLM4.5 in simulation of land surface temperature in mainland China

Land surface temperature(LST) is an important indicator to study climate change and test the performance of regional climate model simulation. RegCM4.6 is the representative version of regional climate model RegCM, which is coupled with advanced third-generation land surface model NCAR CLM4.5. Currently, RegCM4.6 has become an important tool to study regional climate change in China. However, its ability to simulate land surface temperature in mainland China and the reasons for its deviation have not been systematically studied, and targeted improvement work is lacking. The present study is the first to employ LST data collected from 809 Chinese meteorological stations from the last 30 years to comprehensively assess the ability of CLM4.5 to simulate LST. Sensitivity tests of soil thermal conductivity (STC) were carried out to improve the model. Although the coupled regional climate model could accurately simulate the temporal and spatial variation of LST, a cold bias of 2~8 °C existed for all of mainland China, which was larger in seasons with more precipitation and greater soil moisture than other seasons. Deviation increased from southeast to northwest. which was caused by the incoming long-wave radiation, sensible heat, and latent heat simulated. There was a significant linear relationship between the observed and simulated LSTs, with correlation coefficients for all the stations ranged from 0.75 to 0.9 (P < 0.001). The observed LST increased at a rate of 0.58 °C/decade, but the simulated LST increased at a lower rate. Assessment of three different STC schemes showed that the Lu-Ren scheme was the most suitable for LST simulation in mainland China. Developing a new STC scheme that considers the role of water vapor can effectively improve the model when used in mainland China.

A performance evaluation of various physics schemes on the predictions of precipitation and temperature over the Tibet Autonomous Region of China

The WRF model has become an important tool for numerical weather prediction. However, the mechanism of different physics schemes in WRF for air temperature and precipitation predictions over the Tibet Autonomous Region of China is still uncertain. In this study, WRF experiments with distinct physics schemes are conducted on a rainfall event that occurred during the last two days of June 2021 in Tibet. The predictions of air temperature and precipitation from WRF are compared against observational data and the ECMWF forecast, and explained by the land-atmosphere interaction. Results confirm that WRF produces more accurate temperature and precipitation predictions than the widely used ECMWF forecast product. These analyses also reveal large differences in WRF performance in predicting the intensity and spatial patterns of rainfall and diurnal variations of temperature due to the use of different physics schemes. Among the selected physics schemes, the RRTMG scheme yields the highest accuracy forecasts for light rain to rainstorms. The other schemes give an ambiguous performance in relation to predicting various rainfall intensities. In addition, WRF applying the Noah-MP, Mellor-Yamada-Janjic, and RRTMG schemes contributes to alleviating the predicted cold air bias from the ECMWF and WRF control experiment, which is closely related to the surface net radiation overprediction, near-surface energy accumulation and the relatively accurate estimates of ground temperature. The RRTMG scheme shows the strongest capacity for predictions of diurnal variation of temperature, with the lowest RMSE of 3.18 °C and mean error of 2.05 °C. These values are 1.69 °C and 2.03 °C lower, respectively, compared to the ECMWF. The capacity of different schemes to relieve the mode's cold bias is ranked from high to low as RRTMG > Mellor-Yamada-Janjic > Grell 3D/Noah-MP.

Tropical Indian Ocean Mixed Layer Bias in CMIP6 CGCMs Primarily Attributed to the AGCM Surface Wind Bias

Abstract The relatively weak sea surface temperature bias in the tropical Indian Ocean (TIO) simulated in the coupled general circulation model (CGCM) from the recently released CMIP6 has been found to be important in model simulations of regional and global climate. However, the cause of the bias is debated because the bias is strongly model dependent and shows marked seasonality. In this study, we separate the bias in CGCMs into bias arising from oceanic GCMs (OGCMs) and bias that is independent of OGCMs using a set of CMIP6 and OMIP6 models. We found that OGCMs contribute little to mixed layer bias in the CGCMs. The OGCM-independent bias exhibits a large-scale cold mixed layer bias in the TIO throughout the year, with an unexpectedly high degree of model consistency. By conducting a set of OGCM experiments, we show that the OGCM-independent mixed layer bias is caused mainly by surface wind bias in the utilized CGCMs. About 89% of surface wind bias in the CGCMs is due to the inability of atmospheric GCMs (AGCMs), whereas atmosphere–ocean coupling in the CGCMs has only a minor influence on surface wind bias. The bias in surface wind is also found to be the cause of subsurface temperature bias besides the ocean dynamics such as vertical mixing and vertical shear in currents. Our results indicate that correcting TIO mixed layer bias in CGCMs requires improvement in the capability of AGCM in simulating the climatological surface winds. Significance Statement We aimed to discover the cause of temperature bias in the Indian Ocean in CMIP6 models. The bias was separated into oceanic model and ocean-model-independent bias to correspond exactly to bias caused by the oceanic model and by the atmospheric model and coupling, respectively. Oceanic bias contributes little to bias in CMIP6, but ocean-model-independent bias explains the CMIP6 bias throughout the year. We ran oceanic model experiments to show that surface wind bias causes ocean-model-independent temperature bias in the entire TIO and subsurface temperature bias in some areas of the Indian Ocean. We further found that 89% of surface wind bias originates from the atmospheric model. The results improve our understanding of the cause of the bias in the Indian Ocean and show that our method of bias separation is effective for attributing the source of bias to different proposed mechanisms.

Evaluation of Vertical Profiles and Atmospheric Boundary Layer Structure Using the Regional Climate Model CCLM during MOSAiC

Regional climate models are a valuable tool for the study of the climate processes and climate change in polar regions, but the performance of the models has to be evaluated using experimental data. The regional climate model CCLM was used for simulations for the MOSAiC period with a horizontal resolution of 14 km (whole Arctic). CCLM was used in a forecast mode (nested in ERA5) and used a thermodynamic sea ice model. Sea ice concentration was taken from AMSR2 data (C15 run) and from a high-resolution data set (1 km) derived from MODIS data (C15MOD0 run). The model was evaluated using radiosonde data and data of different profiling systems with a focus on the winter period (November–April). The comparison with radiosonde data showed very good agreement for temperature, humidity, and wind. A cold bias was present in the ABL for November and December, which was smaller for the C15MOD0 run. In contrast, there was a warm bias for lower levels in March and April, which was smaller for the C15 run. The effects of different sea ice parameterizations were limited to heights below 300 m. High-resolution lidar and radar wind profiles as well as temperature and integrated water vapor (IWV) data from microwave radiometers were used for the comparison with CCLM for case studies, which included low-level jets. LIDAR wind profiles have many gaps, but represent a valuable data set for model evaluation. Comparisons with IWV and temperature data of microwave radiometers show very good agreement.

Assessment of climate simulation over the Tibetan Plateau based on high-resolution multi-RCM within CORDEX-EA-II

The climate simulation over the Tibetan Plateau (TP) remains a challenge for climate models, limiting the reliability of future climate projections over there. This study focuses on the performance of five regional climate models (RCMs) under the Coordinated Regional Climate Downscaling Experiment-East Asia (CORDEX-EA-II) in reproducing the climate over the TP for the period 1981–2015. All simulations are driven by ERA-Interim reanalysis at a resolution of 25 km (0.22°), and are evaluated against the two observation datasets (CN05 and ERA5) as well as inter-compared to identify their weaknesses and differences among them. The results show that all RCMs can capture the observed spatial distribution and annual cycle of temperature and precipitation over the TP, with overall cold and wet biases. The cold biases exhibit a clear altitude dependence, while there is no obvious relationship between precipitation biases and altitude, and most of the RCMs advance the time with the maximum monthly precipitation by 1 month over the northwestern TP. The distribution of temperature trends and the signal of elevation-dependent warming in the observation are well reproduced by most of the RCMs. Inconsistent precipitation trends are found between the two observational datasets during summer and autumn, and the simulated precipitation trends are more consistent with that in ERA5 than in CN05. Further analysis suggests that the downward shortwave radiation is the main contributor to the diverse temperature simulations among the RCMs for all seasons, and the inability of RCMs to reproduce the precipitation trends may be related to the misrepresentation of convection processes. Moreover, some of the RCMs perform better than ERA5 over the TP, demonstrating the importance of dynamical downscaling over the TP.