Cold Bias Research Articles

Performance and process-based evaluation of the BARPA-R Australasian regional climate model version 1

Abstract. Anthropogenic climate change is changing the Earth system processes that control the characteristics of natural hazards both globally and across Australia. Model projections of hazards under future climate change are necessary for effective adaptation. This paper presents BARPA-R (the Bureau of Meteorology Atmospheric Regional Projections for Australia), a regional climate model designed to downscale climate projections over the Australasian region with the purpose of investigating future hazards. BARPA-R, a limited-area model, has a 17 km horizontal grid spacing and makes use of the Met Office Unified Model (MetUM) atmospheric model and the Joint UK Land Environment Simulator (JULES) land surface model. To establish credibility and in compliance with the Coordinated Regional Climate Downscaling Experiment (CORDEX) experiment design, the BARPA-R framework has been used to downscale ERA5 reanalysis. Here, an assessment of this evaluation experiment is provided. Performance-based evaluation results are benchmarked against ERA5, with comparable performance between the free-running BARPA-R simulations and observationally constrained reanalysis interpreted as a good result. First, an examination of BARPA-R's representation of Australia's surface air temperature, precipitation, and 10 m winds finds good performance overall, with biases including a 1 ∘C cold bias in daily maximum temperatures, reduced diurnal temperature range, and wet biases up to 25 mm per month in inland Australia. Recent trends in daily maximum temperatures are consistent with observational products, while trends in minimum temperatures show overestimated warming and trends in precipitation show underestimated wetting in northern Australia. Precipitation and temperature teleconnections are effectively represented in BARPA-R when present in the driving boundary conditions, while 10 m winds are improved over ERA5 in six out of eight of the Australian regions considered. Secondly, the paper considers the representation of large-scale atmospheric circulation features and weather systems. While generally well represented, convection-related features such as tropical cyclones, the South Pacific Convergence Zone (SPCZ), the Northwest Cloudband, and the monsoon westerlies show more divergence from observations and internal interannual variability than mid-latitude phenomena such as the westerly jets and extratropical cyclones. Having simulated a realistic Australasian climate, the BARPA-R framework will be used to downscale two climate change scenarios from seven CMIP6 global climate models (GCMs).

On the importance of the humidity flux for the surface mass balance in the accumulation zone of the Greenland Ice Sheet

Abstract. It is highly uncertain how the humidity flux between the snow surface and the atmosphere contributes to the surface mass balance (SMB) of the interior Greenland Ice Sheet (GrIS). Due to sparse observations, evaluations of the simulated humidity flux are limited. Model-based estimates of the humidity flux contribution to the SMB are, therefore, unconstrained and even disagree in magnitude and sign. In this study, we evaluate the regional climate model MAR at the EGRIP (East Greenland Ice-Core Project) site in the accumulation zone of the GrIS. We use a combined dataset of continuous one-level bulk estimates of the humidity flux covering the period of May 2016–August 2019 and eddy-resolving eddy-covariance humidity flux measurements from all four summer seasons. In summer, we document a bias of too little sublimation (−1.3 W m−2, −1.65 mm w.e.) caused by a cold bias in both air and surface temperature, leading to a reduced humidity gradient. In winter, MAR overestimates vapor deposition by about 1 order of magnitude. This is a consequence of an overestimated temperature gradient in too stable atmospheric conditions compared to observations. Both systematic errors cause a large discrepancy in the annual net humidity flux between the model and observations of −9 mm w.e. yr−1. Remarkably, the simulated net annual humidity flux contributes positively to the SMB, contrary to observations documenting a net sublimation flux. We correct the systematic errors by applying a simple but effective correction function to the simulated latent heat flux. Using this correction, we find that 5.1 % of the annual mass gain at the EGRIP site sublimates again, and 4.3 % of the total mass gain is deposited vapor from the near-surface air. The estimated net humidity flux contribution to the annual SMB is about −1 % (net sublimation) compared to +5.6 % for the uncorrected simulation. In summer, the corrected MAR simulation shows that vapor deposition accounts for 9.6 % of the total mass gain and that 31 % of the total mass gain at the EGRIP site sublimates again. The net fluxes contribute to −32 % of the summer SMB. These results demonstrate that the humidity flux is a major driver of the summer SMB in the accumulation zone of the GrIS and highlight that even small changes could increase its importance for the annual SMB in a warming climate.

Causes of Diverse Impacts of ENSO on the Southeast Asian Summer Monsoon among CMIP6 Models

Abstract This study examines the fidelity of 47 models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in representing the influence of El Niño–Southern Oscillation (ENSO) on the Southeast Asian summer monsoon (SEASM) during the ENSO decaying summer. The response of the SEASM to ENSO shows a large model spread among the models, some of which even simulate opposite signs of SEASM anomalies compared to the observed values. The bad-performance models (BPMs) are therefore selected to be compared with both the good-performance models (GPMs) and observations to explore the possible causes of the deficiency. Results show that in the BPMs, the ENSO-related warm sea surface temperature (SST) anomalies extend too far westward in the western equatorial Pacific (WEP) and they do not dissipate in the El Niño decaying summer in comparison with those in the GPMs and observations, interfering with the effect of ENSO on the SEASM. The slow decay of WEP SST anomalies from the El Niño mature winter to the decaying summer in the BPMs is mainly caused by a weak negative shortwave radiation feedback due to a low sensitivity of convection to local SST anomalies, which is related to the cold bias in climatological SST over this region. On the other hand, from the mature winter to the decaying summer of El Niño, the El Niño–related anomalous eastward current does not reverse to a westward current in the BPMs, which also contributes to the slow decay of WEP SST anomalies via inducing excessively persistent warm zonal advection. Significance Statement We investigate the possible causes of the diverse impacts of El Niño–Southern Oscillation (ENSO) on the Southeast Asian summer monsoon (SEASM) among 47 CMIP6 models. We find that a plausible reason for the deficiency of some models in simulating the influence of ENSO on the monsoon is that the sea surface temperature (SST) anomalies associated with ENSO are unrealistic in the western equatorial Pacific (WEP) in these models. Further diagnoses indicate that the unrealistic WEP SST anomalies are related to the cold bias of the climatological SST, which could lead to a weak negative shortwave radiation feedback and excessively persistent warm zonal advection. The information provided in this study is useful for improving the skill of the climate models in representing the ENSO–SEASM relationship.

Surface ocean warming near the core of hurricane Sam and its representation in forecast models

On September 30, 2021, a saildrone uncrewed surface vehicle intercepted Hurricane Sam in the northwestern tropical Atlantic and provided continuous observations near the eyewall. Measured surface ocean temperature unexpectedly increased during the first half of the storm. Saildrone current shear and upper-ocean structure from the nearest Argo profiles show an initial trapping of wind momentum by a strong halocline in the upper 30 m, followed by deeper mixing and entrainment of warmer subsurface water into the mixed layer. The ocean initial conditions provided to operational forecast models failed to capture the observed upper-ocean structure. The forecast models failed to simulate the warming and developed a surface cold bias of ~0.5°C by the time peak winds were observed, resulting in a 12-17% underestimation of surface enthalpy flux near the eyewall. Results imply that enhanced upper-ocean observations and, critically, improved assimilation into the hurricane forecast systems, could directly benefit hurricane intensity forecasts.

Impact of Uncertainties in Land Surface Processes on Subseasonal Predictability of Heat Waves Onset Over the Yangtze River Valley

AbstractLand surface processes are strongly associated with heat waves (HWs). However, how the uncertainties in land surface processes owing to inaccurate physical parameters influence subseasonal HW predictions has rarely been explored. To examine the impact of parameter errors of land surface processes on the uncertainty of subseasonal HW predictions, five strong and long‐lasting HW events over the middle and lower reaches of the Yangtze River are investigated. Based on the Weather Research and Forecasting model, the conditional nonlinear optimal perturbation related to parameters (CNOP‐P) approach is employed to address the aforementioned issues. Numerical results demonstrate that the CNOP‐P type errors of physical parameters cause large prediction errors for five HW event onsets. Two types of CNOP‐Ps are obtained for HW events, called the type‐1 CNOP‐P and the type‐2 CNOP‐P. The type‐1 (type‐2) CNOP‐P causes an approximately 3°C (2°C) warm (cold) bias during the HW period. Surface sensible and latent heat flux errors, especially flux exchange between vegetation canopy and canopy air, provide considerable uncertainty in subseasonal HW predictions. The type‐1 (type‐2) CNOP‐P exhibits an underestimation (overestimation) of transpiration. Furthermore, it should be noted that the type‐1 CNOP‐P results in a substantial difference in soil moisture, a phenomenon that is demonstrated to be challenging to observe in the type‐2 CNOP‐P. The results indicate that understanding vegetation‐atmosphere dynamics is crucial for improving subseasonal HW predictions. Jointly lowering soil‒atmosphere and vegetation‐atmosphere uncertainty can notably improve subseasonal HW prediction skills.

How Well Do CMIP6 Models Simulate Salinity Barrier Layers in the North Indian Ocean?

Abstract Previous studies have hypothesized that climatologically thick salinity-stratified barrier layers (BLs) in the north Indian Ocean (NIO) influence the upper ocean heat budget, sea surface temperature (SST), and monsoons. Here, we investigate how state-of-the-art Coupled Model Intercomparison Project phase 6 (CMIP6) climate models simulate the NIO barrier layer thickness (BLT). CMIP6 models generally reproduce the BLT seasonal cycle and spatial distribution, but with shallow November–February (NDJF) biases in regions with thick observed BLT: the eastern equatorial Indian Ocean (EEIO), Bay of Bengal (BoB), and southeastern Arabian Sea (SEAS). We show that the intensity of the CMIP6 equatorial easterly wind bias controls the EEIO shallow isothermal layer depth (ILD) and BLT biases. It also controls the BoB shallow BLT bias, both through the propagation of the EEIO shallow ILD bias into the NIO coastal waveguide and because it is linked to the BoB dry and cold bias through the Bjerknes feedback, hence also controlling the mixed layer depth (MLD) deep bias there. Finally, the SEAS shallow BLT bias is due to a too-deep MLD, in response to subdued monsoonal currents around India, which do not bring enough BoB low-salinity water. The BL insulating effect mentioned in literature does not seem to dominate in CMIP6. Rather, the CMIP6 salinity-related deep MLD biases diminish the BoB cooling rate by winter upward surface heat fluxes, reducing cold SST biases. This suggests that salinity effects alleviate the easterly equatorial wind, cold, and dry BoB biases that develop through the positive Bjerknes feedback loop in CMIP6.

Local and Remote Causes of the Equatorial Pacific Cold Sea Surface Temperature Bias in the Kiel Climate Model

Abstract We investigate the origin of the equatorial Pacific cold sea surface temperature (SST) bias and its link to wind biases, local and remote, in the Kiel Climate Model (KCM). The cold bias is common in climate models participating in phases 5 and 6 of the Coupled Model Intercomparison Project. In the coupled experiments with the KCM, the interannually varying NCEP/CFSR wind stress is prescribed over four spatial domains: globally, over the equatorial Pacific (EP), the northern Pacific (NP), and the southern Pacific (SP). The corresponding EP SST bias is reduced by 100%, 52%, 12%, and 23%, respectively. Thus, the EP SST bias is mainly attributed to the local wind bias, with small but not negligible contributions from the extratropical regions. Erroneous ocean circulation driven by overly strong winds causes the cold SST bias, while the surface heat flux counteracts it. Extratropical Pacific SST biases contribute to the EP cold bias via the oceanic subtropical gyres, which is further enhanced by dynamical coupling in the equatorial region. The origin of the wind biases is examined by forcing the atmospheric component of the KCM in a stand-alone mode with observed SSTs and simulated SSTs from the coupled experiments. Wind biases over the EP, NP, and SP regions originate in the atmosphere model. The cold EP SST bias substantially enhances the wind biases over all three regions, while the NP and SP SST biases support local amplification of the wind bias. This study suggests that improving surface wind stress, at and off the equator, is a key to improve mean-state equatorial Pacific SST in climate models.

Benefit of incorporating GLASS remote sensing vegetation products in improving Noah-MP land surface temperature simulations on the Tibetan Plateau

Land Surface Temperature (LST) is important for diagnosing surface energy balance in land surface models (LSMs). However, LST simulation in current LSMs tends to show large cold biases, partially due to the reason that the model's prescribed vegetation parameters (e.g., Leaf Area Index (LAI) and Fraction of Vegetation Cover (FVC)) are misrepresented, especially in regions with complex topography and climate such as Tibetan Plateau. Recent advancements in remote sensing technologies provide a unique opportunity to improve the model's vegetation parameters at large scales. In this study, we practice two experiments to improve LST simulations in Noah-MP LSM by (1) incorporating LAI and FVC from the Global Land Surface Satellite (GLASS) remote sensing product (exp_RS); and (2) incorporating an empirical LAI and FVC parameterization scheme based on the soil temperature stress factor (exp_RL02). Results show that the effect of vegetation on simulated LST is the most significant in summer season when the model-satellite LAI and FVC differences are the largest. Compared to the default experiment that uses static LAI and FVC values from the model's look-up table (exp_CTL), the results in exp_RS and exp_RL02 show domain-wide improvement of the simulated LST. The LAI and FVC effect on LST are also well reflected in model's energy budget components (i.e., longwave emissivity, sensible and latent heat fluxes, etc). Validation of the model simulated soil temperature with in-situ observations further demonstrate the model improvements. Our study underscores the important role of vegetation in regulating surface energy transfer processes. Our study also highlights the feasibility and benefit of incorporating remote sensing data in improving land surface model simulations.

Difference in boreal winter predictability between two dynamical cores of Community Atmosphere Model 5

This study investigates the sensitivity of the boreal winter prediction skill of Community Atmosphere Model 5 to the choice of the dynamical core. Both finite volume (FV) and spectral element (SE) dynamical cores are tested. An additional FV with the SE topography (FVSE) is also conducted to isolate the possible influence of the topography. The three dynamical core experiments, which ran from 2001/2002–2017/2018, are validated using Japanese 55 year reanalysis data. It turns out that the SE (−4.27 °C) has a smaller cold bias in boreal-winter surface air temperature (SAT) than the FV (−5.17 °C) and FVSE (−5.29 °C), particularly in North America, East Asia, and Southern Europe/Northern Africa. Significant North Atlantic Oscillation-like biases are also identified in the mid-troposphere. These biases affect seasonal prediction skills. Although the overall prediction skills of boreal-winter SAT, quantified by the anomaly correlation coefficient (ACC), and root-mean-square error (RMSE), are reasonably good (ACC = 0.40 and RMSE = 0.47 in the mean values of SE, FV, and FVSE), they significantly differ from one region to another, depending on the choice of dynamical cores. For North America and Southern Europe/Northern Africa, SE shows better skills than FVSE and FV. Conversely, in East Asia, FV and FVSE outperform SE. These results suggest that the appropriate choice of the dynamical cores and the bottom boundary conditions could improve the boreal-winter seasonal prediction on a regional scale.

Simulation of Temperature Extremes Over West Africa With MPAS

AbstractA large ensemble of 51 simulations with the Model for Prediction Across Scales (MPAS) has been applied to assess its ability to reproduce extreme temperatures and heat waves in the area of West Africa. With its global approach the model avoids transition errors influencing the performance of limited area climate models. The MPAS simulations were driven with sea surface temperature (SST) and sea ice extent as the only boundary condition. The results reveal moderate cold biases in the range from −0.6° to −0.9°C for the daily mean temperature and −1.2° to −2.0°C for the area mean of the daily maximum temperature. The bias in the number of tropical nights ranges from +3 to −10 days. An underestimation by up to 50% is also present regarding the number of summer days. The heat wave duration index is underestimated regionally by 10%–60%. MPAS simulations are generally closer to the reanalysis results than they are to the observational reference. The results from long term runs and from short term runs with selected SST years are similar. Shortcomings in the reproduction of the temperature and precipitation indices found in the present investigation indicate that the global MPAS approach does provide a fidelity similar to that of the regional climate models.

Improving climate model skill over High Mountain Asia by adapting snow cover parameterization to complex-topography areas

Abstract. This study investigates the impact of topography on five snow cover fraction (SCF) parameterizations developed for global climate models (GCMs), including two novel ones. The parameterization skill is first assessed with the High Mountain Asia Snow Reanalysis (HMASR), and three of them are implemented in the ORCHIDEE land surface model (LSM) and tested in global land–atmosphere coupled simulations. HMASR includes snow depth (SD) uncertainties, which may be due to the elevation differences between in situ stations and HMASR grid cells. Nevertheless, the SCF–SD relationship varies greatly between mountainous and flat areas in HMASR, especially during the snow-melting period. The new parameterizations that include a dependency on the subgrid topography allow a significant SCF bias reduction, reaching 5 % to 10 % on average in the global simulations over mountainous areas, which in turn leads to a reduction of the surface cold bias from −1.8 ∘C to about −1 ∘C in High Mountain Asia (HMA). Furthermore, the seasonal hysteresis between SCF and SD found in HMASR is better captured in the parameterizations that split the accumulation and the depletion curves or that include a dependency on the snow density. The deep-learning SCF parameterization is promising but exhibits more resolution-dependent and region-dependent features. Persistent snow cover biases remain in global land–atmosphere experiments. This suggests that other model biases may be intertwined with the snow biases and points out the need to continue improving snow models and their calibration. Increasing the model resolution does not consistently reduce the simulated SCF biases, although biases get narrower around mountain areas. This study highlights the complexity of calibrating SCF parameterizations since they affect various land–atmosphere feedbacks. In summary, this research spots the importance of considering topography in SCF parameterizations and the challenges in accurately representing snow cover in mountainous regions. It calls for further efforts to improve the representation of subgrid-scale processes affecting snowpack in climate models.

Overcoming cold start and sensor bias: A deep learning-based framework for IoT-enabled monitoring applications

Target localization is an essential aspect of smart environmental monitoring, which has gained prominence thanks to the Internet of Things (IoT) paradigm. Localization is the process of determining the location of an unknown target within an area of interest (AoI) based on data gathered by IoT sensors. Existing target localization works assume full knowledge about the sensors without considering the cold start problem, which may arise when new sensors join the network. This is exacerbated by the problem of bootstrapping in IoT sensors, where the system needs to identify and register unknown sensors. Additionally, the variability in sensors characteristics is inevitable due to the numerous factors that may alter the sensors behavior, raising the problem of sensor bias. Existing works dealing with the aforementioned problems are inadequate, since they involve a pre-processing step and assume the bias to be static over time, resulting in inaccurate localization systems. Therefore, this work proposes a robust and dynamic, holistic localization system that can find the location of an unknown emitting target regardless of the sensors characteristics. The proposed system comprises of two phases: 1) characterization and localization phase; where a deep learning model is utilized to characterize the sensors based on their locations and readings only, whereas Bayesian algorithm is employed to determine the unknown target location by leveraging the predicted sensors characteristics, and 2) correction phase, where an update metric is devised to refine the predicted sensors characteristics based on the estimated target location. The validation of the proposed approach was conducted using real-life and synthetic datasets and compared against an existing benchmark, where it shows an improvement of ∼79%, in terms of both localization and prediction errors. Thus, showcasing the effectiveness and potential advantages of the proposed approach in localizing unknown target(s).

Regional and Teleconnected Impacts of Solar Radiation‐Topography Interaction Over the Tibetan Plateau

AbstractSolar radiation‐topography interaction plays an important role in surface energy balance over the Tibetan Plateau (TP). However, the impacts of such interaction over the TP on climate locally and in the Asian regions remain unclear. This study uses the Energy Exascale Earth System Model (E3SM) to evaluate the regional and teleconnected impacts of solar radiation‐topography interaction over the TP. Land‐atmosphere coupled experiments show that topography regulates the surface energy balance, snow processes, and surface climate over the TP across seasons. Accounting for solar radiation‐topography interaction improves E3SM simulation of surface climate. The winter cold bias in air temperature decreases from −4.57 to −3.79 K, and the wet bias in summer precipitation is mitigated in southern TP. The TP's solar radiation‐topography interaction further reduces the South and East Asian summer precipitation biases. Our results demonstrate the topographic roles in regional climate over the TP and highlight its teleconnected climate impacts.

Regional and Seasonal Biases in Convection-Allowing Model Forecasts of Near-Surface Temperature and Moisture

Abstract This study investigates regional, seasonal biases in convection-allowing model forecasts of near-surface temperature and dewpoint in areas of particular importance to forecasts of severe local storms. One method compares model forecasts with objective analyses of observed conditions in the inflow sectors of reported tornadoes. A second method captures a broader sample of environments, comparing model forecasts with surface observations under certain warm-sector criteria. Both methods reveal a cold bias across all models tested in Southeast U.S. cool-season warm sectors. This is an operationally important bias given the thermodynamic sensitivity of instability-limited severe weather that is common in the Southeast cool season. There is not a clear bias across models in the Great Plains warm season, but instead more varied behavior with differing model physics. Significance Statement The severity of thunderstorms and the types of hazards they produce depend in part on the low-level temperature and moisture in the near-storm environment. It is important for numerical forecast models to accurately represent these fields in forecasts of severe weather events. We show that the most widely used short-term, high-resolution forecast models have a consistent cold bias of about 1 K (up to 2 K in certain cases) in storm environments in the southeastern U.S. cool season. Human forecasters must recognize and adjust for this bias, and future model development should aim to improve it.

A Global-Ocean-Data Assimilation for Operational Oceanography

In this study, a global-ocean-data-assimilation system based on the three-dimensional variational (3DVAR) scheme is built for operational oceanography. The available observations include satellite altimetry; the satellite-measured sea-surface temperature (SST); and T/S profiles from Argo floats, which are assimilated to provide the initial condition of the global-ocean forecasting. The statistical analysis methods are designed to assess the performance of the data-assimilation scheme, and the results show that the analysis SST fields agree well with OSTIA and MGDSST, and the corresponding root-mean-square errors are, respectively, 0.523 and 0.548 °C. Moreover, the analysis sea-surface-height fields are well represented at the middle and low latitudes and have a slightly greater difference in the regions with strong mesoscale eddies. The variations in the vertical distribution of the forecasting temperature profiles resemble those of the GTS buoy observation. The forecasting salinity profiles correspond well to GTS observations, except with a weaker cold bias between the depths 100 and 200 m (about 0.2 PSU) at buoy station 2901494. Overall, our 3DVAR assimilation system plays a significant role in improving the accuracy of analysis and forecasting fields for operational oceanography.

Ocean Dynamics Causes the Equatorial Pacific SST Bias in CAS‐ESM2‐0

AbstractCoupled ocean‐atmosphere models still suffer from systematic biases in simulating the sea‐surface temperature (SST) in the equatorial Pacific. Like in many current generations of climate models, the Chinese Academy of Sciences (CAS) Earth System Model (CAS‐ESM2‐0) simulated warmer SST in the eastern Pacific and colder SST in the central to western Pacific at the equator. We analyzed the physical processes causing these biases. We find that model biases in surface heat flux are a consequence, not the cause, of the SST bias. Ocean currents are implicated as the cause. The biases in ocean currents come from both the ocean model itself and the bias in surface winds in the atmospheric model. We show that the bias of ocean currents in the standalone ocean model causes warming in the region of positive precipitation bias in the standalone atmospheric model. When the two models are coupled, the precipitation bias is greatly amplified. Anomalous equatorial winds as atmospheric Rossby‐gravity wave response to precipitation induce additional bias in ocean currents that transport heat in the upper ocean. The cold equatorial SST bias in the central and western Pacific is found to be due to temperature advection by the anomalous zonal currents, while the warm bias in the eastern Pacific is due to advection by the anomalous zonal and meridional currents as well as downwelling. Results from the analysis offer insights into the causes of the SST biases in coupled models in the equatorial Pacific.

Challenging the universality of heatwave definitions: gridded temperature discrepancies across climate regions

As global studies of climate change depict increasingly dire outcomes of extreme heat, there is an urgent need to understand the appropriateness of heatwave definitions and temperature datasets in different parts of the world. We carry out an intercomparison of the CHIRTS gridded station-satellite temperature dataset with three reanalysis products, ERA5, NCEP-DOE Reanalysis 2, and MERRA2, to assess biases in the absolute value of extreme heat events and the distribution of extreme events. We find close agreement between all four datasets in the magnitude and distribution of extreme temperatures, with a cold bias in the reanalyses over mountainous areas. However, there is little to no agreement between datasets on the timing of extreme heat events in the tropics, and the datasets do not even agree on which month is the hottest month climatologically in these regions. Second, we compare how the four datasets represent the frequency and timing of extreme heat events, using two different types of heatwave definitions: 5-day duration-based extremes and extreme temperature-humidity combinations (heat index). In the case of 5-day heatwaves, there are almost zero events recorded historically in tropical regions. In contrast, high absolute values of the heat index are most common in dry climates, likely due to the dominance of high temperature spikes in these regions, and high heat index events also occur in temperate and tropical regions. There is little agreement between datasets, however, on when these extreme heat index events have happened historically in the tropics. Given these results, we highlight the need for locally developed heatwave metrics for different parts of the world, and we urge against the use of a single heatwave definition in global studies. We also recommend that any studies assessing heat-health relationships in tropical regions beware of the lack of agreement between observational and reanalysis datasets and compare results from different gridded dataset products to estimate uncertainty in heat-health relationships.

A station‐based evaluation of near‐surface south foehn evolution in COSMO‐1

AbstractThis study investigates the skill of the Consortium for Small‐scale Modeling (COSMO) model (v5.7) at 1.1 km horizontal grid size in simulating the near‐surface foehn properties and evolution for five south foehn events and a five‐year‐long climatology. A significant near‐surface cold bias is found during foehn, with an average bias of K in the Rhine Valley in the five foehn cases and K in the major northern foehn valleys in the five‐year foehn climatology. The cold bias tends to be larger in the stronger and moister deep foehn events. Sensitivity experiments are carried out to examine the possible causes of the cold bias, including changes to the parameterization of the land–atmosphere interaction, 1D turbulence parameterization, and horizontal grid spacing. Most sensitivity experiments have only a very minor impact on the cold bias, except for the model run with a horizontal grid spacing of 550 m. The 550‐m COSMO run shows a reduced cold bias during foehn hours and also an improvement in the simulated foehn duration and northward foehn extent. By inspecting the vertical dimension, we found that the near‐surface cold bias downstream might partly originate upstream. A further contribution to the downstream cold bias is likely due to insufficient vertical mixing in the foehn flow. The latter is possibly enhanced in the 550‐m model run, leading to a less stably stratified atmosphere in the lower few hundred meters of the atmosphere and a reduction of the reported model cold bias.

Improvements in the Canadian Earth System Model (CanESM) through systematic model analysis: CanESM5.0 and CanESM5.1

Abstract. The Canadian Earth System Model version 5.0 (CanESM5.0), the most recent major version of the global climate model developed at the Canadian Centre for Climate Modelling and Analysis (CCCma) at Environment and Climate Change Canada (ECCC), has been used extensively in climate research and for providing future climate projections in the context of climate services. Previous studies have shown that CanESM5.0 performs well compared to other models and have revealed several model biases. To address these biases, the CCCma has recently initiated the “Analysis for Development” (A4D) activity, a coordinated analysis activity in support of CanESM development. Here we describe the goals and organization of this effort and introduce two variants (“p1” and “p2”) of a new CanESM version, CanESM5.1, which features important improvements as a result of the A4D activity. These improvements include the elimination of spurious stratospheric temperature spikes and an improved simulation of tropospheric dust. Other climate aspects of the p1 variant of CanESM5.1 are similar to those of CanESM5.0, while the p2 variant of CanESM5.1 features reduced equilibrium climate sensitivity and improved El Niño–Southern Oscillation (ENSO) variability as a result of intentional tuning of the atmospheric component. The A4D activity has also led to the improved understanding of other notable CanESM5.0 and CanESM5.1 biases, including the overestimation of North Atlantic sea ice, a cold bias over sea ice, biases in the stratospheric circulation and a cold bias over the Himalayas. It provides a potential framework for the broader climate community to contribute to CanESM development, which will facilitate further model improvements and ultimately lead to improved climate change information.

Temperature variation in the South Orkney Islands, maritime Antarctic

AbstractMeteorological records at Signy Station in the South Orkney Islands (SOIs) have recently been digitized to cover the period of 1947–1995. This study compares the newly available near‐surface air temperatures at Signy with those from a nearby station, Orcadas and with reanalysis datasets to provide a more comprehensive picture of the weather and climate variability in the SOIs. Temperatures from both stations show a higher degree of variability in winter than summer, but the variability differs in terms of its relationship to the dominant wind directions and sea ice influences. The two stations differ markedly in terms of their respective warm temperature events, largely due to orography‐induced föhn winds at Signy Station as northwesterlies flow over Coronation Island. ERA5 reproduces the monthly to annual averages exceedingly well but underestimates both cold and warm tails of station temperatures. Temperature trends in the SOIs are also considered in terms of changes in large‐scale circulation and the sea surface temperature over the Brazil‐Falkland Confluence. However, caution is required in interpreting the long‐term temperature trends estimated from reanalysis data as most of the reanalyses show a cold bias before 1979, which is most likely caused by misrepresentation of the sea ice.