Cold Bias Research Articles

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.

Southern Ocean Surface Temperatures and Cloud Biases in Climate Models Connected to the Representation of Glacial Deep Ocean Circulation

Abstract Simulating and reproducing the past Atlantic meridional overturning circulation (AMOC) with comprehensive climate models are essential to understanding past climate changes as well as to testing the ability of the models in simulating different climates. At the Last Glacial Maximum (LGM), reconstructions show a shoaling of the AMOC compared to modern climate. However, almost all state-of-the-art climate models simulate a deeper LGM AMOC. Here, it is shown that this paleodata–model discrepancy is partly related to the climate model biases in modern sea surface temperatures (SST) over the Southern Ocean (70°–45°S). Analysis of model outputs from three phases of the Paleoclimate Model Intercomparison Project shows that models with warm Southern Ocean SST biases tend to simulate a deepening of the LGM AMOC, while the opposite is observed in models with cold SST biases. As a result, a positive correlation of 0.41 is found between SST biases and LGM AMOC depth anomalies. Using sensitivity experiments with a climate model, we show, as an example, that changes in parameters associated with the fraction of cloud thermodynamic phase in a climate model reduce the biases in the warm SST over the modern Southern Ocean. The less biased versions of the model then reproduce a colder Southern Ocean at the LGM, which increases formation of Antarctic Bottom Water and causes shoaling of the LGM AMOC, without affecting the LGM climate in other regions. The results highlight the importance of sea surface conditions and clouds over the Southern Ocean in simulating past and future global climates. Significance Statement To test the ability of comprehensive climate models, simulations of the Last Glacial Maximum (LGM) have been conducted. However, most models simulated a deeper Atlantic meridional overturning circulation (AMOC) in the LGM, which contradicts paleodata suggesting a shallower AMOC. Here, using multimodel analysis and sensitivity experiments with a climate model, we show that this paleodata–model discrepancy is partly related to model biases in the modern Southern Ocean. Improvements in Southern Ocean surface temperatures and clouds reproduce a colder climate over the Southern Ocean at the LGM, which causes an intense shoaling of the AMOC due to increased formation of Antarctic Bottom Water. These results demonstrate the important effect of model biases over the Southern Ocean on simulating past climates.

Validation of Himawari-8 Sea Surface Temperature Retrievals Using Infrared SST Autonomous Radiometer Measurements

This study has evaluated five years (2016–2020) of Himawari-8 (H8) Sea Surface Temperature (SST) Level 2 Pre-processed (L2P) data produced by the Australian Bureau of Meteorology (Bureau) against shipborne radiometer SST measurements obtained from the Infrared SST Autonomous Radiometer (ISAR) onboard research vessel RV Investigator. Before being used, all data sets employed in this study have gone through careful quality control, and only the most trustworthy measurements are retained. With a large matchup database (31,871 collocations in total, including 16,418 during daytime and 15,453 during night-time), it is found that the Bureau H8 SST product is of good quality, with a mean bias ± standard deviation (SD) of −0.12 °C ± 0.47 °C for the daytime and −0.04 °C ± 0.37 °C for the night-time. The performance of the H8 data under different environmental conditions, determined by the observations obtained concurrently from RV Investigator, is examined. Daytime and night-time satellite data behave slightly differently. During the daytime, a cold bias can be seen under almost all environmental conditions, including for most values of wind speed, SST, and relative humidity. On the other hand, the performance of the night-time H8 SST product is consistently more stable under most meteorological conditions with the mean bias usually close to zero.

Evaluation of ERA5-Simulated Temperature and Its Extremes for Australia

Atmospheric reanalysis products offer high-resolution and long-term gridded datasets that can often be used as an alternative or a supplement to observational data. Although more accessible than typical observational data and deemed fit for climate change studies, reanalysis data can show biases resulting from data assimilation approaches. Thus, a thorough evaluation of the reanalysis product over the region and metric of study is critical. Here, we evaluate the performance of the latest generation of ECMWF reanalysis, ERA5, in simulating mean and extreme temperatures over Australia for 1979–2020 versus high-quality gridded observations. We find ERA5 generally simulates maximum and minimum temperatures reasonably well (mean bias ~1.5 °C), even though it underestimates/overestimates the daily maximum/minimum temperatures, leading to a cold bias for Tmax and a warm bias for Tmin. ERA5 also underestimates the decadal warming trend in both Tmax and Tmin compared to the observations. Furthermore, ERA5 struggles to simulate the temporal variability of Tmin, leading to a markedly worse skill in Tmin than Tmax. In terms of extreme indices, ERA5 is skilled at capturing the spatial and temporal patterns and trends of extremes, albeit with the presence of biases in each index. This can partially be attributed to the warm bias in the minimum temperature. Overall, ERA5 captures the mean and extreme temperature indices over the Australian continent reasonably well, warranting its potential to supplement observations in aiding climate change-related studies, downscaling for boundary conditions, and climate model evaluation.

Sensitivities of subgrid-scale physics schemes, meteorological forcing, and topographic radiation in atmosphere-through-bedrock integrated process models: a case study in the Upper Colorado River basin

Abstract. Mountain hydrology is controlled by interacting processes extending from the atmosphere through the bedrock. Integrated process models (IPMs), one of the main tools needed to interpret observations and refine conceptual models of the mountainous water cycle, require meteorological forcing that simulates the atmospheric process to predict hydroclimate then subsequently impacts surface–subsurface hydrology. Complex terrain and extreme spatial heterogeneity in mountainous environments drive uncertainty in several key considerations in IPM configurations and require further quantification and sensitivity analyses. Here, we present an IPM using the Weather Research and Forecasting (WRF) model which forces an integrated hydrologic model, ParFlow-CLM, implemented over a domain centered over the East River watershed (ERW), located in the Upper Colorado River basin (UCRB). The ERW is a heavily instrumented 300 km2 region in the headwaters of the UCRB near Crested Butte, CO, with a growing atmosphere-through-bedrock observation network. Through a series of experiments in the water year 2019 (WY19), we use four meteorological forcings derived from commonly used reanalysis datasets, three subgrid-scale physics scheme configurations in WRF, and two terrain shading options within WRF to test the relative importance of these experimental design choices for key hydrometeorological metrics including precipitation and snowpack, as well as evapotranspiration, groundwater storage, and discharge simulated by the ParFlow-CLM. Our hypothesis is that uncertainty from synoptic-scale forcings produces a much larger spread in surface–subsurface hydrologic fields than subgrid-scale physics scheme choice. Results reveal that the WRF subgrid-scale physics configuration leads to larger spatiotemporal variance in simulated hydrometeorological conditions, whereas variance across meteorological forcing with common subgrid-scale physics configurations is more spatiotemporally constrained. Despite reasonably simulating precipitation, a delay in simulated discharge peak is due to a systematic cold bias across WRF simulations, suggesting the need for bias correction. Discharge shows greater variance in response to the WRF simulations across subgrid-scale physics schemes (26 %) rather than meteorological forcing (6 %). The topographic radiation option has minor effects on the watershed-average hydrometeorological processes but adds profound spatial heterogeneity to local energy budgets (±30 W m−2 in shortwave radiation and 1 K air temperature differences in late summer). This is the first presentation of sensitivity analyses that provide support to help guide the scientific community to develop observational constraints on atmosphere-through-bedrock processes and their interactions.

Evaluating Arctic clouds modelled with the Unified Model and Integrated Forecasting System

Abstract. By synthesising remote-sensing measurements made in the central Arctic into a model-gridded Cloudnet cloud product, we evaluate how well the Met Office Unified Model (UM) and the European Centre for Medium-Range Weather Forecasting (ECMWF) Integrated Forecasting System (IFS) capture Arctic clouds and their associated interactions with the surface energy balance and the thermodynamic structure of the lower troposphere. This evaluation was conducted using a 4-week observation period from the Arctic Ocean 2018 expedition, where the transition from sea ice melting to freezing conditions was measured. Three different cloud schemes were tested within a nested limited-area model (LAM) configuration of the UM – two regionally operational single-moment schemes (UM_RA2M and UM_RA2T) and one novel double-moment scheme (UM_CASIM-100) – while one global simulation was conducted with the IFS, utilising its default cloud scheme (ECMWF_IFS). Consistent weaknesses were identified across both models, with both the UM and IFS overestimating cloud occurrence below 3 km. This overestimation was also consistent across the three cloud configurations used within the UM framework, with &gt;90 % mean cloud occurrence simulated between 0.15 and 1 km in all the model simulations. However, the cloud microphysical structure, on average, was modelled reasonably well in each simulation, with the cloud liquid water content (LWC) and ice water content (IWC) comparing well with observations over much of the vertical profile. The key microphysical discrepancy between the models and observations was in the LWC between 1 and 3 km, where most simulations (all except UM_RA2T) overestimated the observed LWC. Despite this reasonable performance in cloud physical structure, both models failed to adequately capture cloud-free episodes: this consistency in cloud cover likely contributes to the ever-present near-surface temperature bias in every simulation. Both models also consistently exhibited temperature and moisture biases below 3 km, with particularly strong cold biases coinciding with the overabundant modelled cloud layers. These biases are likely due to too much cloud-top radiative cooling from these persistent modelled cloud layers and were consistent across the three UM configurations tested, despite differences in their parameterisations of cloud on a sub-grid scale. Alarmingly, our findings suggest that these biases in the regional model were inherited from the global model, driving a cause–effect relationship between the excessive low-altitude cloudiness and the coincident cold bias. Using representative cloud condensation nuclei concentrations in our double-moment UM configuration while improving cloud microphysical structure does little to alleviate these biases; therefore, no matter how comprehensive we make the cloud physics in the nested LAM configuration used here, its cloud and thermodynamic structure will continue to be overwhelmingly biased by the meteorological conditions of its driving model.

Performance of the Canadian Arctic Prediction System during the YOPP Special Observing Periods

ABSTRACT As a contribution to the Year of Polar Prediction (YOPP), Environment and Climate Change Canada (ECCC) developed the Canadian Arctic Prediction System (CAPS), a high-resolution (3-km horizontal grid-spacing) deterministic Numerical Weather Prediction (NWP) system that ran in real-time from February 2018 to November 2021. During YOPP, ECCC was also running two other operational systems that cover the Arctic: the 10-km Regional Deterministic Prediction System (RDPS) and the 25-km Global Deterministic Prediction System (GDPS). The performance of these three systems over the Arctic was monitored and routinely compared during 2018, both subjectively and with objective verification scores. This work provides a description of CAPS and compares the surface variable objective verification for the Canadian deterministic NWP systems operational during YOPP, focusing on the Arctic winter and summer Special Observing Periods (Feb-March and July-Aug-Sept, 2018). CAPS outperforms RDPS and GDPS in predicting near-surface temperature, dew-point temperature, wind and precipitation, in both seasons and domains. All three systems exhibit a diurnal cycle in the near-surface temperature biases, with maxima at night and minima in day-time. In order to mitigate representativeness issues associated with complex topography, model tile temperatures are adjusted to the station elevation by applying a standard atmosphere lapse-rate: especially for the coarse-resolution models, the lapse-rate adjustment reduces the temperature cold biases characterising mountain terrains. Verification of winter precipitation is performed by adjusting solid precipitation measurement errors from the undercatch in windy conditions: the Canadian models’ systematic positive bias, which was artificially inflated by the undercatch, is reduced by the adjustment, to attain neutral bias. These YOPP dedicated intense verification activities have identified some strengths, weaknesses and systematic behaviours of the Canadian deterministic prediction systems at high latitudes: these results can serve as a benchmark, for comparison and further development. Moreover, this YOPP verification exercise has revealed some issues related to the verification of surface variables and has led to the development of better verification practices for the polar regions (and beyond).

Subsurface Thermohaline Biases in the Southern Tropical Pacific and the Roles of Wind Stress and Precipitation

Abstract Thermohaline structure and time evolution in the subsurface ocean play a critical role in climate variability and predictability. They are still poorly represented in ocean and climate models. Here, the characteristics of subsurface thermohaline biases in the southern tropical Pacific and their causes are investigated through CMIP-based analyses and model-based experiments. There exists a pronounced subsurface cold bias at 200-m depth over the southern tropical Pacific in CMIP6 simulations with an ensemble mean of about −4°C and an extreme close to −10°C. This cold bias is accompanied by a fresh subsurface bias of about −0.9 psu in the ensemble mean (−1.9 psu minimum). Similar subsurface thermohaline biases also exist in CMIP5 outputs, indicating that reduction of these biases remains a long-standing challenge for model developments. To understand the causes of these biases, attribution analyses and POP2-based sensitivity experiments are performed. It is found that the subsurface thermohaline biases are attributed to the model deficiencies in simulating wind stress curl and precipitation in the southern tropical Pacific. By conducting CESM2-based coupled experiments, a warm SST bias in the southeastern tropical Pacific is found to be responsible for the poor simulations in wind stress curl and precipitation. The consequences of these biases are also analyzed. The subsurface thermohaline biases cause the density field to increase substantially along 10°S, flattening the zonal isopycnal surface and reducing equatorward interior transport. In addition, the anomalously cold and fresh subsurface signals in the southern tropical Pacific are seen to propagate to the equator, leading to an overall spurious cooling in the equatorial subsurface. Significance Statement Subsurface biases severely degrade the credibility of climate models in their predictions and projections; hence, it is important to understand the causes of these subsurface biases. Our study analyzes the characteristics of subsurface thermohaline biases in the southern tropical Pacific and investigates their causes. A pronounced subsurface cold bias is found over the southern tropical Pacific, accompanied by an obvious subsurface fresh bias. By performing attribution analyses and numerical experiments, it is found that the subsurface thermohaline biases are attributed to the model deficiencies in simulating wind stress and precipitation, which are further attributed to the warm SST bias in the southeastern tropical Pacific. These results provide a guide for improving climate model performances.

Improved Performance of CLM5.0 Model in Frozen Soil Simulation Over Tibetan Plateau by Implementing the Vegetation Emissivity and Gravel Hydrothermal Schemes

AbstractFrozen soil is widely distributed over the Tibetan Plateau (TP) and has significant impacts on the regional climate and ecosystem. However, the Community Land Model version 5 (CLM5.0) produces evident cold bias in the frozen soil simulation over TP. In this study, an improved vegetation emissivity scheme and a gravel hydrothermal scheme have been implemented into CLM5.0 model, and their synergistic influences on the frozen soil simulation over TP have been systematically addressed and revealed. Results show that adopting the vegetation emissivity scheme, the gravel hydrothermal scheme, and both can remarkably reduce the cold bias in the frozen soil simulated by the original CLM5.0 model, and the column mean root‐mean‐square error (RMSE) can be reduced by 12.88%, 20.68%, and 31.11% at Arou site and 25.03%, 10.15%, and 36.87% at Maqu site, respectively. The reductions of column mean RMSE for the modeled soil temperature regionally averaged over TP induced by the adoption of vegetation emissivity scheme, the gravel hydrothermal scheme, and both are 32.34%, 6.75%, and 30.18%, respectively. The underlying physical mechanisms are related to the improved representation of the soil hydrothermal processes above or in the soil. The improved vegetation emissivity scheme improves the soil surface long‐wave radiation heat transfer process, while the gravel hydrothermal scheme improves the hydrothermal properties within the soil. Overall, improving the surface long‐wave radiation heat transfer and the internal soil hydrothermal properties can obviously enhance the performance of CLM5.0 model in simulating the soil freeze–thaw processes.

UKESM1.1: development and evaluation of an updated configuration of the UK Earth System Model

Abstract. Many Coupled Model Intercomparison Project phase 6 (CMIP6) models have exhibited a substantial cold bias in the global mean surface temperature (GMST) in the latter part of the 20th century. An overly strong negative aerosol forcing has been suggested as a leading contributor to this bias. An updated configuration of UK Earth System Model (UKESM) version 1, UKESM1.1, has been developed with the aim of reducing the historical cold bias in this model. Changes implemented include an improved representation of SO2 dry deposition, along with several other smaller modifications to the aerosol scheme and a retuning of some uncertain parameters of the fully coupled Earth system model. The Diagnostic, Evaluation and Characterization of Klima (DECK) experiments, a six-member historical ensemble and a subset of future scenario simulations are completed. In addition, the total anthropogenic effective radiative forcing (ERF), its components and the effective and transient climate sensitivities are also computed. The UKESM1.1 preindustrial climate is warmer than UKESM1 by up to 0.75 K, and a significant improvement in the historical GMST record is simulated, with the magnitude of the cold bias reduced by over 50 %. The warmer climate increases ocean heat uptake in the Northern Hemisphere oceans and reduces Arctic sea ice, which is in better agreement with observations. Changes to the aerosol and related cloud properties are a driver of the improved GMST simulation despite only a modest reduction in the magnitude of the negative aerosol ERF (which increases by +0.08 W m−2). The total anthropogenic ERF increases from 1.76 W m−2 in UKESM1 to 1.84 W m−2 in UKESM1.1. The effective climate sensitivity (5.27 K) and transient climate response (2.64 K) remain largely unchanged from UKESM1 (5.36 and 2.76 K respectively).

An Application of the Maximum Entropy Production Method in the WRF Noah Land Surface Model

AbstractThe calculation of surface sensible heat (SH) and latent heat (LE) fluxes using the bulk transfer models for complex terrains, tall vegetation regions, and morning and evening transition periods remains a challenging problem in numerical weather and climate models. The maximum entropy production (MEP) model, a new method of calculating surface heat fluxes, is coupled with the Noah land surface model (LSM) in the Weather Research and Forecasting (WRF) model. Surface heat fluxes and meteorological variables including air temperature, relative humidity, soil temperature, and precipitation data at nine intensive observation stations and 453 routine meteorological operational observation stations in the Tibetan Plateau (TP) from 1 June to 31 August 2015 are used to evaluate the coupled model. The results show that the MEP method improves the nonclosure of the surface energy balance in the WRF (with an energy residual from 24.3 to 1.9 W m−2), reducing the overestimations of SH and LE and the underestimation of the surface ground heat flux over the complex terrain of TP (with the bias decreases of 50.6% and 117.1% for SH and LE in turn), especially during the daytime. The improved SH and LE reduce the cold and wet biases in the TP by 29.4% and 51.7%, respectively. The MEP model also reduces the overestimated soil temperature by 63%. Moreover, the overestimated daily precipitation is reduced by 3% over the TP. The successful application of the MEP method demonstrates its advantage over the bulk transfer method, providing a new approach for reducing the overestimation of surface heat fluxes and wet and cold biases in numerical forecast models in complex terrains.

On the use of Infrared Atmospheric Sounding Interferometer (IASI) spectrally resolved radiances to test the EC-Earth climate model (v3.3.3) in clear-sky conditions

Abstract. The long-term comparison between simulated and observed spectrally resolved outgoing longwave radiation (OLR) can represent a stringent test for the direct verification and improvement of general circulation models (GCMs), which are regularly tuned by adjusting parameters related to subgrid processes not explicitly represented in the model to constrain the integrated OLR energy fluxes to observed values. However, a good agreement between simulated and observed integrated OLR fluxes may be obtained from the cancellation of opposite-in-sign systematic errors localized in specific spectral ranges. Since the mid-2000s, stable hyperspectral observations of the mid-infrared region (667 to 2750 cm−1) of the Earth emission spectrum have been provided by different sensors (e.g. AIRS, IASI and CrIS). Furthermore, the FORUM (Far-infrared Outgoing Radiation Understanding and Monitoring) mission, selected to be the ninth ESA Earth Explorer, will measure, starting from 2027, the terrestrial radiation emitted to space at the top of the atmosphere (TOA) from 100 to 1600 cm−1, filling the observational gap in the far-infrared (FIR) region, from 100 to 667 cm−1. In this work, in anticipation of FORUM measurements, we compare Infrared Atmospheric Sounding Interferometer (IASI) Metop-A observations to radiances simulated on the basis of the atmospheric fields predicted by the EC-Earth Global Climate Model (version 3.3.3) in clear-sky conditions. To simulate spectra based on the atmospheric and surface state provided by the climate model, the radiative transfer model σ-IASI has been integrated in the Cloud Feedback Model Intercomparison Project (COSP) package. Therefore, online simulations, provided by the EC-Earth model equipped with the new COSP–σ-IASI module, have been performed in clear-sky conditions with prescribed sea surface temperature and sea ice concentration, every 6 h, over a time frame consistent with the availability of IASI data. Systematic comparisons between observed and simulated brightness temperature (BT) have been performed in 10 cm−1 spectral intervals, on a global scale over the ocean, with a specific focus on the latitudinal belt between 30∘ S and 30∘ N. The analysis has shown a warm BT bias of about 3.5 K in the core of the CO2 absorption band and a cold BT bias of approximately 1 K in the wing of the CO2 band, due to a positive temperature bias in the stratosphere and a negative temperature bias in the middle troposphere of the climate model, respectively. Finally, considering a warm BT bias in the rotational–vibrational water vapour band, we have highlighted a dry bias of the water vapour concentration in the upper troposphere of the model.

Assessment of AIRS Version 7 Temperature Profiles and Low-Level Inversions with GRUAN Radiosonde Observations in the Arctic

The extensive global climate observing system (GCOS) reference upper-air network (GRUAN) datasets provide a chance to validate newly released Atmospheric Infrared Sounder (AIRS) version 7 (v7) products over the Arctic. This manuscript reports on the analysis performed to evaluate errors from AIRS version 6 (v6) and v7 temperature profiles and to characterize the derived low-level temperature inversion (LLI) representativeness in the Arctic region. The AIRS averaging kernel, representing the AIRS measurement sensitivity, is applied to reduce the vertical resolution of the radiosonde profiles for comparison. Due to improved retrieval algorithms, v7 produces smaller biases in the troposphere and suppresses the cold bias in v6. Nevertheless, the profile-averaged root mean square error (RMSE) increased by over 30% in v7, particularly in the winter half-year when v7 showed a larger RMSE below 800 hPa. The AIRS temperature retrieval accuracy is primarily sensitive to surface type and cloud fraction. Compared to v6, v7 has less bias over frozen land and sea ice in different cloud fraction conditions. However, the RMSEs of v7 are more sensitive to the effective cloud fraction (ECF) and are highly influenced by a more significant contribution from nonfrozen land samples. Compared to the kernel-averaged radiosonde profiles, more than 80% of the temperature profiles from v6 and v7 accurately detect LLIs. The discreteness of the AIRS’s predefined pressure level results is consistent with the radiosondes only 65% of the time for LLI depth calculation. In contrast, the AIRS can obtain LLI intensity with a relatively high correlation (&gt;0.9). With the AIRS temperature retrieval in the boundary layer further improved, it has the potential to be used as an independent LLI detector in the Arctic region.

Investigating the Diurnal Radiative, Turbulent, and Biophysical Processes in the Amazonian Canopy‐Atmosphere Interface by Combining LES Simulations and Observations

AbstractWe investigate the atmospheric diurnal variability inside and above the Amazonian rainforest for a representative day during the dry season. To this end, we combine high‐resolution large‐eddy simulations that are constrained and evaluated against a comprehensive observation set, including CO2 concentrations, gathered during GoAmazon2014/15. We design systematic numerical experiments to quantify whether a multilayer approach in solving the explicit canopy improves our canopy‐atmosphere representation. We particularly focus on the relationship between photosynthesis and plant transpiration, and their distribution at leaf and canopy scales. We found the variability of photosynthesis drivers like vapor pressure deficit and leaf temperature to be about 3 times larger for sunlit leaves compared to shaded leaves. This leads to a large spread on leaf stomatal conductance values with minimum and maximum values varying more than 100%. Regarding the turbulent structure, we find wind‐driven stripe‐like shapes at the canopy top and structures resembling convective cells at the canopy. Wind‐related variables provide the best spatiotemporal agreement between model and observations. The potential temperature and heat flux profiles agree with an observed decoupling near the canopy top interface, although with less variability and cold biases of up to 3 K. The increasing complexity on the biophysical processes leads to the largest disagreements for evaporation, CO2 plant assimilation and soil efflux. The model is able to capture the correct dependences and trends with the magnitudes still differing. We finally discuss the need to revise leaf and soil models and to complete the observations at leaf and canopy levels.

Numerical simulation of thermal stratification in Lake Qiandaohu using an improved WRF-Lake model

Lake thermal stratification is important for regulating lake environments and ecosystems and is sensitive to climate change and human activity. However, numerical simulation of coupled hydrodynamics and heat transfer processes in deep lakes using one-dimensional lake models remains challenging because of the insufficient representation of key parameters. In this study, Lake Qiandaohu, a deep and warm monomictic reservoir, was used as an example to investigate thermal stratification via an improved parameterization scheme of the Weather Research and Forecast (WRF)-Lake. A comparison with in situ observations demonstrated that the default WRF-Lake model was able to simulate well the seasonal variation of the lake thermal structure. However, the simulations exhibited cold biases in lake surface water temperature (LSWT) throughout the year while generating weaker stratification in summer, thereby leading to an earlier cooling period in autumn. With an improved parameterization (i.e., via determination of initial lake water temperature profiles, light extinction coefficients, eddy diffusion coefficients and surface roughness lengths), the modified WRF-Lake model was able to better simulate LSWT and thermal stratification. Critically, employing realistic initial conditions for lake water temperature is essential for producing realistic hypolimnetic water temperatures. The use of time-dependent light extinction coefficients resulted in a deep thermocline and warm LSWT. Enlarging eddy diffusivity led to stronger mixing in summer and further influenced autumn cooling. The parameterized surface roughness lengths mitigated the excessive turbulent heat loss at the lake surface, improved the model performance in simulating LSWT, and generated a warm mixed layer. This study provides guidance on model parameterization for simulating the thermal structure of deep lakes and advances our understanding of the strength and revolution of lake thermal stratification under seasonal changes.

Evaluation of a cloudy cold-air pool in the Columbia River basin in different versions of the High-Resolution Rapid Refresh (HRRR) model

Abstract. The accurate forecast of persistent orographic cold-air pools in numerical weather prediction models is essential for the optimal integration of wind energy into the electrical grid during these events. Model development efforts during the second Wind Forecast Improvement Project (WFIP2) aimed to address the challenges related to this. We evaluated three versions of the National Oceanic and Atmospheric Administration (NOAA) High-Resolution Rapid Refresh model with two different horizontal grid spacings against in situ and remote sensing observations to investigate how developments in physical parameterizations and numerical methods targeted during WFIP2 impacted the simulation of a persistent cold-air pool in the Columbia River basin. Differences amongst model versions were most apparent in simulated temperature and low-level cloud fields during the persistent phase of the cold-air pool. The model developments led to an enhanced low-level cloud cover, resulting in better agreement with the observations. This removed a diurnal cycle in the near-surface temperature bias at stations throughout the basin by reducing a cold bias during the night and a warm bias during the day. However, low-level clouds did not clear sufficiently during daytime in the newest model version, which leaves room for further model developments. The model developments also led to a better representation of the decay of the cold-air pool by slowing down its erosion.