Near-surface Air Temperature Research Articles

Energy and environmental impacts of air-to-air heat pumps in a mid-latitude city

Heat pumps (HPs) have emerged as a key technology for reducing energy use and greenhouse gas emissions. This study evaluates the potential switch to air-to-air HPs (AAHPs) in Toulouse, France, where conventional space heating is split between electric and gas sources. In this context, we find that AAHPs reduce heating energy consumption by 57% to 76%, with electric heating energy consumption decreasing by 6% to 47%, resulting in virtually no local heating-related CO2 emissions. We observe a slight reduction in near-surface air temperature of up to 0.5 °C during cold spells, attributable to a reduction in sensible heat flux, which is unlikely to compromise AAHPs operational efficiency. While Toulouse’s heating energy mix facilitates large energy savings, electric energy consumption may increase in cities where gas or other fossil fuel sources prevail. Furthermore, as AAHPs efficiency varies with internal and external conditions, their impact on the electrical grid is more complex than conventional heating systems. The results underscore the importance of matching heating system transitions with sustainable electricity generation to maximize environmental benefits. The study highlights the intricate balance between technological advancements in heating and their broader environmental and policy implications, offering key insights for urban energy policy and sustainability efforts.

Aerosols and black carbon variability using OMI and MERRA-2 and their relationship to near-surface air temperature.

An extinction of incoming solar radiation is taking place by absorption and scattering by dust, water droplets, and gaseous molecules. Such phenomena are responsible for altering meteorological variables. In the present study, temporal analysis of the aerosol optical thickness (AOT) and black carbon (BC) surface mass concentration was undertaken using an ozone monitoring instrument (OMI) and modern-era retrospective analysis for research and applications, version 2 (MERRA-2) satellite from the year 2018 to 2022. The study was mainly focused on the western states of India which are Rajasthan, Gujarat, and Maharashtra. The correlation of AOT and BC surface mass concentration with near-surface temperature (2m above ground level) was analyzed. BC and temperature shows strong negative correlation as BC is known for its absorption of radiation. It accumulates in the atmosphere and contributes to atmospheric warming while simultaneously bringing down the near-surface air temperature due to the reduced sunlight reaching the ground. Also, seasonal analysis was conducted for winter, summer, monsoon, and post-monsoon, which shows the higher values of AOT in monsoon; however, seasonal average BC surface mass concentration was found high in winter in each year for all three states. AERONET data from Jaipur, Rajasthan, and Pune, Maharashtra for the year 2021 was used to further evaluate the AOT generated from OMI. The results demonstrated a significant connection, with R2 values of 0.62 and 0.69, respectively. The temperature retrieved from MERRA-2 was also validated with ground truth data of the Continuous Ambient Air Quality Monitoring Station (CAAQMS) at both stations showing high agreement with R2 > 0.70.

Environmental impacts of aerosol radiative effect and urbanization and their interactions over the Beijing-Tianjin-Hebei City cluster

With fast economic development, urban expansion and air pollution are two main eco-environmental challenges confronting China. Though both are associated with intensified human activities, their impacts on urban environment are investigated separately, and the synthetic effect and intricate interaction of the two have not been fully understood. Here, based on in-situ measurements and online-coupled meteorology-chemistry modelling, the impacts of anthropogenic aerosol and urbanization are investigated simultaneously at Beijing-Tianjin-Hebei city cluster during a typical pollution season. The two processes exhibit opposite influences on meteorology and air quality. Aerosol via its radiative effect tends to cause net loss of solar energy with 3 °C cooling at the ground surface and 1 °C warming in the upper boundary layer. In contrast, urbanization yields large increment of net surface solar radiation as well as sensible heat flux, leading to over 1 °C warming in the lower atmosphere. Such modifications of boundary layer structure alter the accumulation and formation of air pollution. Urbanization effect dominated in the modification of near-surface air temperature and air pollution, making the synthetic effect a linear combination of two processes. The study highlights a comprehensive understanding of air pollution and urbanization and their synergy effect on urban environment over megacities.

The Added Value of Statistical Seasonal Forecasts

Seasonal climate predictions can assist with timely preparations for extreme episodes, such as dry or wet periods that have associated additional risks of droughts, fires and challenges for water management. Timely warnings for extreme warm summers or cold winters can aid in preparing for increased energy demand. We analyse seasonal forecasts produced by three different methods: (1) a multi-linear statistical forecasting system based on observations only; (2) a non-linear random forest model based on observations only; and (3) process-based dynamical forecast models. The statistical model is an empirical system based on multiple linear regression that is extended to include the trend over the previous 3 months in the predictors, and overfitting is further reduced by using an intermediate multiple linear regression model. This results in a significantly improved El Niño forecast skill, specifically in spring. Also, the Indian Ocean dipole (IOD) index forecast skill shows improvements, specifically in the summer and autumn months. A hybrid multi-model ensemble is constructed by combining the three forecasting methods. The different methods are used to produce seasonal forecasts (three-month means) for near-surface air temperature and monthly accumulated precipitation seasonal forecast with a lead time of one month. We find numerous regions with added value compared with multi-model ensembles based on dynamical models only. For instance, for June, July and August temperatures, added value is observed in extensive parts of both Northern and Southern America, as well as Europe.

Investigating long-term changes in surface water temperature of Dongting Lake using Landsat imagery, China.

Lake surface water temperature (LSWT) plays a crucial role in assessing the health of aquatic ecosystems. Variations in LSWT can significantly impact the physical, chemical, and biological processes within lakes. This study investigates the long-term changes in surface water temperature of the Dongting Lake, China. The LSWT is retrieved using Landsat thermal infrared imageries from 1988 to 2022 and validated with in situ observations, and the change characteristics of LSWT and near-surface air temperature (NSAT) as well as the spatial distribution characteristics of LSWT are analyzed. Additionally, the contribution rates of different meteorological factors to LSWT are quantified. The results show that the accuracy assessment of satellite-derived temperatures indicates a Nash-Sutcliffe efficiency coefficient (NSE) of 0.961, suggesting an accurate retrieval of water temperature. From 1988 to 2022, both the annual average LSWT and NSAT of Dongting Lake exhibit an increasing trend, with similar rates of warming. They both undergo a mutation in 1997 and have the main periods on the 11-year and 4-year time scales. The changes in NSAT emerge as one of the important factors contributing to variations in LSWT. Among the multiple meteorological factors, NSAT exhibits a significant correlation with LSWT (R = 0.822, α < 0.01). Furthermore, NSAT accounts for the highest contribution rate to LSWT, amounting to 67.5%. The distribution of LSWT within Dongting Lake exhibits spatial variations, with higher LSWT observed on the west part compared to the east part during summer, while lower LSWT occurs on the west part during winter. The findings of this study can provide a scientific understanding for the long-term thermal regimes of lakes and help advance sustainable lake management.

The Atmospheric Heating Mechanism over the Tharsis Bulge of Mars and the Impact of Global Dust Storms

Mars atmospheric dynamics are crucial for understanding its climate and weather patterns, especially over plateaus. Previous studies have explored localized atmospheric heating mechanisms over Mars plateaus only to a little extent. The local atmospheric heating dynamics over the Tharsis plateau, especially during global dust storms (GDSs), have not been quantitatively analyzed before. Based on reanalysis datasets, our analysis reveals that the central highlands of Tharsis experience ~130 K diurnal temperature fluctuations, driven by intense daytime convective activity. Surface temperature and near-surface air temperatures show fluctuations approximately 25 K and 20 K higher than those at similar latitudes, respectively. We quantify a super-adiabatic lapse rate around noon that suggests strong atmospheric instability, previously unquantified in this region. By dusk, the atmosphere stabilizes, presenting a homogenized condition. At aphelion, sensible heating and adiabatic terms control the atmospheric heating, while, at perihelion, radiative and sensible heating predominate. Notably, the onset of GDS significantly alters this dynamic, reducing the ground–air temperature gap from 17 K to 5 K and enhancing diabatic heating (adiabatic cooling) in the mid-to-lower (mid-to-upper) troposphere, with increases in radiative components up to 60 W/m2.

Simple process-led algorithms for simulating habitats (SPLASH v.2.0): robust calculations of water and energy fluxes

Abstract. The current representation of key processes in land surface models (LSMs) for estimating water and energy balances still relies heavily on empirical equations that require calibration oriented to site-specific characteristics. When multiple parameters are used, different combinations of parameter values can produce equally acceptable results, leading to a risk of obtaining “the right answers for the wrong reasons”, compromising the reproducibility of the simulations and limiting the ecological interpretability of the results. To address this problem and reduce the need for free parameters, here we present novel formulations based on first principles to calculate key components of water and energy balances, extending the already parsimonious SPLASH model v.1.0 (Davis et al., 2017, GMD). We found analytical solutions for many processes, enabling us to increase spatial resolution and include the terrain effects directly in the calculations without unreasonably inflating computational demands. This calibration-free model estimates quantities such as net radiation, evapotranspiration, condensation, soil water content, surface runoff, subsurface lateral flow, and snow-water equivalent. These quantities are derived from readily available meteorological data such as near-surface air temperature, precipitation, and solar radiation, as well as soil physical properties. Whenever empirical formulations were required, e.g., pedotransfer functions and albedo–snow cover relationships, we selected and optimized the best-performing equations through a combination of remote sensing and globally distributed terrestrial observational datasets. Simulations at global scales at different resolutions were run to evaluate spatial patterns, while simulations with point-based observations were run to evaluate seasonal patterns using data from hundreds of stations and comparisons with the VIC-3L model, demonstrating improved performance based on statistical tests and observational comparisons. In summary, our model offers a more robust, reproducible, and ecologically interpretable solution compared to more complex LSMs.

Using clustering to understand intra-city warming in heatwaves: insights into Paris, Montreal, and Zurich

We introduce a novel methodological advancement by clustering paired near-surface air temperature with the planetary boundary layer height to characterize intra-city clusters for analytics. To illustrate this approach, we analyze three heatwaves (HWs): the 2019 HW in Paris, the 2018 HW in Montreal, and the 2017 HW in Zurich. We assess cluster-based characteristics before, during, and after heatwave events. While the urban clusters identified by this clustering align well with built-up areas obtained from the Moderate Resolution Imaging Spectroradiometer (MODIS) land cover data, additional local hot spots spanning several kilometers can also be recognized, extending outside the built-up areas. Using the objective hysteresis model, we further determine the overall strength coefficient of the hysteresis loop between ground storage flux and all-wave downward radiative flux, ranging from 0.414 to 0.457 for urban clusters and from 0.126 to 0.157 for rural clusters during the heatwave periods. Across all cities, we observe a consistent refueling-restoration mode in the cumulative ground heat flux as the heatwaves progress. Future developments of this proposed two-component clustering approach, with the integration of more influential physics and advances in spatial and temporal resolutions, will offer a more comprehensive characterization of cities for urban climate analytics.

Amplification of the discrepancy between simplified and physics-based wet-bulb globe temperatures in a warmer climate

The Simplified Wet Bulb Globe Temperature (sWBGT) is widely used in heat stress assessments for climate-change studies, but its limitations have not been thoroughly explored. Building on recent critiques of sWBGT's use for current climate on global scale, this study examines sWBGT's biases using dynamically-downscaled sub-daily climate projections under multiple future emission scenarios. The analysis is aimed at understanding caveats in the application of sWBGT and the uncertainties in existing climate change analysis dependent on sWBGT. Results indicate sWBGT's biases are heavily influenced by local near-surface air temperature, with overestimation of heat stress in East Asia regions, particularly hot and humid areas, due to static assumptions of radiation and wind speed. This overestimation is amplified in warmer climates, leading to exaggerated projected heat stress increases in future. In contrast, underestimations are found for heat stress levels attributed to low wind speeds and strong radiations, such as over the Tibetan Plateau and certain extreme events. Additionally, sWBGT underestimates variability in extreme heatwave events compared to WBGT in both current and future climates, irrespective of overestimation in absolute heatwave intensities. This study emphasizes the limitations of sWBGT, especially in future warmer climates. Importance of sub-daily data for capturing daily maximum heat stress level and reflecting diurnal variations in different components is also discussed. In conclusion, we recommend using Liljegren's model (i.e., physics-based calculation) with high-resolution sub-daily climate data for more accurate outdoor heat stress assessments in climate change studies.

Atmospheric blocking and temperatures in the Antarctic Peninsula

The Antarctic Peninsula (AP) has displayed a propensity for persistent blocking ridges and anticyclonic conditions, particularly during recent summertime extreme weather events. This study investigates atmospheric blocking patterns over the AP through historical (1981–2010) and future (2071–2100, SSP5–8.5) periods using ERA5 reanalysis and six CMIP6 models, including multi-member realizations from two models totaling ten simulations. We focus particularly on 500 hPa geopotential height (Z500) and near-surface air temperature (T2m) anomalies. The historical analysis highlights significant differences between the CMIP6 models and ERA5 reanalysis, especially in the austral winter, with EC-Earth3 and INM-CM4 models matching closest with the ERA5. Future projections show that while the northern AP and the Drake Passage largely do not exhibit a clear trend towards increased blocking, there are exceptions. The EC-Earth3 model predicts more blocking–like conditions northwest of the AP in summer and a pronounced ridge over the Bellingshausen Sea in winter, indicating a potential increase in blocking events. The INM-CM4 model projects a minor increase in summer Z500 heights off the western and southern AP, without clear blocking patterns over the AP, and negligible winter changes. Localized intensification is noted in the northern parts of the blocking domain and southern AP during extreme blocking conditions. These variations are mirrored in T2m anomalies, suggesting warming in the northern and southern sections of AP but little change elsewhere. The results of this study underscore the need to more accurately capture complex blocking mechanisms and their impacts on regional climate patterns around the AP. We also suggest employing refined blocking definitions and incorporating a broader range of climate models to enhance our understanding of blocking patterns and their impacts in a changing climate.

Anthropogenic influence on seasonal extreme temperatures in eastern China at century scale

Due to the scarcity of observational data in the early 20th century, very limited research has explored the impact of human activities on temperature extremes at the regional scale. Here we used a newly developed homogenized near-surface air temperature dataset from the beginning of the 20th century to estimate the frequency and intensity of extreme temperatures in eastern China and evaluate their anthropogenic influence based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found clear increases in warm extremes and decreases in cold extremes since 1901 for both annual and seasonal mean temperatures, with more pronounced changes in recent decades. The most significant warming occurred in spring and winter, approximately double the smallest warming observed in autumn. The CMIP6 models generally replicated the century-scale warming in annual and seasonal temperature extremes, showing increases in the frequency and intensity of warm extremes and corresponding decreases in cold extremes. The optimal fingerprinting detections suggest that the century-scale warming can be clearly attributed to anthropogenic forcing, including changes in seasonal extreme temperatures. Most observed changes in extreme temperatures were attributable to anthropogenic greenhouse gas emissions, partially offset by a smaller negative impact from anthropogenic aerosol forcing, whereas natural forcing has played a minor role. These results provide important information for accurately projecting future changes in temperature extremes.

Interchangeability of multi-decade skin and surface air temperature trends over land in models

Satellite land surface temperature (Ts ) records have now reached 20+ year length, but their trends may differ from historical records built from in-situ measurements of near-surface air temperature (Tas ). In the ERA5 reanalysis, 60° S–60° N land Ts and Tas trends can differ by up to ±0.06 °C decade−1 over 20 years, depending on the period, or more on smaller spatial scales. Here I use 1979–1998 outputs from ACCESS1-0 climate model simulations with prescribed land Ts to understand changes in Ts and Tas . CO2’s effective radiative forcing causes adjustments that warm Tas relative to Ts . In ACCESS1-0, vegetation enhances the adjustments to CO2 over land. Meanwhile, feedbacks in ACCESS1-0 oppose the adjustments, resulting in small long-term net effects on global temperature estimates. In coupled simulations from other models, there is no agreement on whether Ts or Tas warms more and the most extreme case shows global long-term differences of just 5% between land Ts or land Tas trends. The results contrast with over-ocean behavior where adjustments and feedbacks reinforce each other, and drive larger long-term Tas warming relative to Ts across all models.

Boost to early spring compound heatwaves over South Asia from anomalous Tibetan Plateau atmospheric heat source

In the early spring of 2022, South Asia (SA) experienced an unprecedented and prolonged episode of compound extreme heatwaves (EHWs), which pose significant risks to both human society and ecosystems. This exceptional event broke historical records for both frequency and intensity anomalies since 1981 and could be attributed to a barotropic atmospheric regime characterized by an anomalous anticyclone. This study ascribed this record-breaking event to an exceptionally warm Tibetan Plateau (TP) atmospheric apparent heat source (TPAHS). Both the observations and the numerical experiments conducted with a linear baroclinic model revealed that the unusual TPAHS acted as an “air pump”, driving the vigorous lower-level convergence and ascending motion around the TP. Abnormal TPAHS led to an increase in air temperature, subsequently elevating the height of the tropopause. This, in turn, induced a cooler upper-level and warmer lower-level thermal structure over the TP, resulting in a negative potential vorticity (PV) source near 200 hPa. As the TPAHS continued to intensify, the anomalous anticyclone strengthened and extended westward, facilitating the advection of negative PV anomalies from the TP to upstream regions. This resulted in negative PV anomalies and increased geopotential height over the SA. Furthermore, the triggered zonal circulation significantly enhanced descending motion and reduced relative vorticity, thereby strengthening the barotropic atmospheric regime over the SA. Consequently, clear skies prevailed, amplifying the solar shortwave radiation reaching the ground. This increase in surface thermal radiation warmed the near-surface air temperatures, ultimately resulting in compound EHWs on an interannual timescale.

Persistent urban heat.

Urban surface and near-surface air temperatures are known to be often higher than their rural counterparts, a phenomenon now labeled as the urban heat island effect. However, whether the elevated urban temperatures are more persistent than rural temperatures at timescales commensurate to heat waves has not been addressed despite its importance for human health. Combining numerical simulations by a global climate model with a surface energy balance theory, it is demonstrated here that urban surface and near-surface air temperatures are significantly more persistent than their rural counterparts in cities dominated by impervious materials with large thermal inertia. Further use of these materials will result in even stronger urban temperature persistence, especially for tropical cities. The present findings help pinpoint mitigation strategies that can simultaneously ameliorate the larger magnitude and stronger persistence of urban temperatures.

Public health: a forgotten piece of the adaptation law puzzle

This paper uses the problem of extreme heat to illustrate the inadequacy of laws for protecting public health under climate change. Climate change is already having serious effects on public health. The Intergovernmental Panel on Climate Change (IPCC) Sixth Assessment Report highlights significant adaptation gaps for human health protection, urging that public health adaptation must be ‘proactive, timely and effective’. The law can be a powerful tool for advancing adaptation to protect public health, but there has been very little scholarly analysis of its potential, or whether in some circumstances it may promote maladaptation. For example, legal regimes for land use planning typically respect existing uses of property and make retrofitting for climate-proofing hard to mandate. These regimes can take many years to amend so new infrastructure continues to comply with outdated approaches, such as relying on air conditioning for cooling and offering limited shading. Laws also promote a focus on crisis management during a heat event but fail to promote the preventive action necessary to foster resilience. We present a case study of how the law exacerbates public health risks from extreme heat and falls short of facilitating adaptation in the Greater Western Sydney region of Australia, an area with a population of 2.6 million. In 2019, this area experienced a record near-surface air temperature of 52°C (125.6°F) causing significant adverse physical and mental health impacts. The public health impacts of extreme temperatures in this region are well documented, as are the increasing strains on emergency and health services. This case study demonstrates that laws could help to control heat in the landscape and secure the safety of vulnerable populations, but to do so they must prioritize adaptation to the health impacts of climate change.

Comparing ML Methods for Downscaling Near-Surface Air Temperature over the Eastern Mediterranean

Near-surface air temperature (Ta) is a key variable in global climate studies. Global climate models such as ERA5 and CMIP6 predict various parameters at coarse spatial resolution (&gt;9 km). As a result, local phenomena such as the urban heat islands are not reflected in the model’s outputs. In this study, we address this limitation by downscaling the resolution of ERA5 (9 km) and CMIP6 (27 km) Ta to 1 km, employing two different machine learning algorithms (XGBoost and Deep Learning). Our models leverage a diverse set of features, including data from satellites (land surface temperature and normalized difference vegetation index), from ERA5 and CMIP6 climate models (e.g., solar and thermal radiation, wind), and from digital elevation models to develop accurate machine learning prediction models. These models were rigorously validated against observations from 98 meteorological stations in the East Mediterranean (Israel) using a standard cross-validation technique as well as a leave-one-group-out on the station ID evaluation methodology to avoid overfitting and dependence on geographic location. We demonstrate the sensitivity of the downscaled Ta to local land cover and topography, which is missing in the climate models. Our results demonstrate impressive accuracy with the Deep Learning-based models, obtaining Root Mean Squared Error (RMSE) values of 0.98 °C (ERA5) and 1.86 °C (CMIP6) for daily Ta and 2.20 °C (ERA5) for hourly Ta. Additionally, we explore the impact of the various input features and offer an extended application for future climate predictions. Finally, we propose an enhanced evaluation framework, which addresses the problem of model overfitting. This work provides practical tools and insights for building and evaluating Ta downscaling models. The code and data are publicly shared online.

Representing natural climate variability in an event attribution context: Indo-Pakistani heatwave of 2022

Attribution of extreme climate events to global climate change as a result of anthropogenic greenhouse gas emissions has become increasingly important. Extreme climate events arise at the intersection of natural climate variability and a forced response of the Earth system to anthropogenic greenhouse gas emissions, which may alter the frequency and severity of such events. Accounting for the effects of both natural climate variability and the forced response to anthropogenic climate change is thus central for the attribution. Here, we investigate the reproducibility of probabilistic extreme event attribution results under more explicit representations of natural climate variability. We employ well-established methodologies deployed in statistical Earth System Model emulators to represent natural climate variability as informed from its spatio-temporal covariance structures. Two approaches towards representing natural climate variability are investigated: (1) where natural climate variability is treated as a single component; and (2) where natural climate variability is disentangled into its annual and seasonal components. We showcase our approaches by attributing the 2022 Indo-Pakistani heatwave to human-induced climate change. We find that explicit representation of annual and seasonal natural climate variability increases the overall uncertainty in attribution results considerably compared to established approaches such as the World Weather Attribution Initiative. The increase in likelihood of such an event occurring as a result of global warming differs slightly between the approaches, mainly due to different assessments of the pre-industrial return periods. Our approach that explicitly resolves annual and seasonal natural climate variability indicates a median increase in likelihood by a factor of 41 (95% range: 6-603). We find a robust signal of increased likelihood and intensification of the event with increasing global warming levels across all approaches. Compared to its present likelihood, under 1.5 °C (2 °C) of global near-surface air temperature increase relative to pre-industrial temperatures, the likelihood of the event would be between 2.2 to 2.5 times (8 to 9 times) higher. We note that regardless of the different statistical approaches to represent natural variability, the outcomes on the conducted event attribution are similar, with minor differences mainly in the uncertainty ranges. Possible reasons for differences are evaluated, including limitations of the proposed approach for this type of application, as well as the specific aspects in which it can provide complementary information to established approaches.

Chasing the heat: Unraveling urban hyperlocal air temperature mapping with mobile sensing and machine learning

Many cities face unprecedented high temperatures with increasing extreme events. Heatwaves pose significant health risks, including cardiovascular diseases, heatstroke, and dehydration. Mapping urban near-surface air temperature (Tair) is crucial for understanding thermal exposure and addressing climate change. Previous studies relied on satellite-derived land surface temperature (LST) and stationary monitoring, but high spaio-temporal Tair mapping is still a challenge. This study optimized a mobile sensing scheme using an electric bicycle platform with environmental and image sensors, and deep learning captured local-scale urban factors. A spatio-temporal data fusion model that consisted of three parts, temporal trend extraction, locality analysis, and neighborhood effect analysis, generated hyperlocal Tair maps. The Results from Beijing demonstrated the effectiveness of the framework, achieving the lowest MAE of 0.02 °C. Optimized data collection and the new model achieved accurate temperature predictions and thermal exposure assessment. Efficiency enhanced sensing strategy was also proposed. The study highlights local-scale factors and spatio-temporal dependencies in addressing heatwaves and climate change impacts in urban areas.

The influence of Urmia Lake desiccation on an extreme snowfall event: A case study using the WRF-Lake model

Urmia Lake in the northwest Iran has undergone significant desiccation in recent decades, shrinking to a much smaller size than its original. This study implements the WRF-Lake coupled modeling system to examine the impacts of the observed physical changes over the Lake during a heavy snowfall event occurred February 1–2, 2017. Modifications are introduced to represent the shrunken, salt-encrusted lake surface and surrounding arid environment through updating land use, soil characteristics, lake size and depth, and albedo data. Experiments incorporating lake-atmosphere coupling and land surface refinements (Lake_CTL, Lake_LULC, Lake_AL) outperform simulations without a lake (WRF_noLake) in reproducing observed precipitation patterns at stations surrounding the Urmia Lake. All simulations underestimate the extreme 290 mm snowfall recorded at the Urmia station; however, the Lake_LULC and Lake_AL improve snowfall representation at other stations around the lake. Regarding snow cover validation, Lake_LULC and Lake_AL exhibit a higher correlation with satellite observations and show a significant improvement in snow detection near Urmia Lake. They have a Probability of Detection (POD) of 0.94 and a Critical Success Index (CSI) of 0.91, compared to a POD of 0.79–0.80 and a CSIs of ∼0.77 for the default lake model (Lake_CTL) and the WRF_noLake experiments, respectively. Additionally, adjusting the initial lake surface temperature to match observations substantially reduces cold biases in near-surface air temperatures over Urmia Lake. The lack of lake temperature updating in WRF-Lake poses ongoing challenges in the model, though. In summary, this study demonstrates that refining the representations of desiccated lakes and their surroundings in high-resolution coupled models would improve simulations of meteorological processes influenced by lake-atmosphere interactions.

Cooler Arctic surface temperatures simulated by climate models are closer to satellite-based data than the ERA5 reanalysis

Many climate models simulate near-surface air temperatures that are too low in the Arctic compared to the observation-based ERA5 reanalysis data, a bias that was noted in the Inter-governmental Panel on Climate Change Sixth Assessment Report (IPCC AR6). Here we present a high-resolution, satellite-derived dataset of near-surface air temperatures for the Arctic sea-ice region (1982–2020). We use it as a benchmark to reevaluate climate reanalyses and model simulations in CMIP6 (Coupled Model Intercomparison Project 6). We find that the CMIP6 simulations in the central Arctic, with generally thicker ice and snow, align well with satellite observations, with an annual mean bias of less than ± 1 °C over sea ice. By contrast, climate reanalyses like ERA5 exhibit widespread warm biases exceeding 2 °C in the same region. We conclude that reliance on ERA5 reanalysis as a reference may have led to an underestimation of climate model reliability in the Arctic region.