Simulated Surface Temperature Research Articles

Depth Matters: Lake Bathymetry Selection in Numerical Weather Prediction Systems

AbstractLake surface conditions are critical for representing lake‐atmosphere interactions in numerical weather prediction. The Community Land Model's 1‐D lake component (CLM‐lake) is part of NOAA's High‐Resolution Rapid Refresh (HRRR) 3‐km weather/earth‐system model, which assumes that virtually all the two thousand lakes represented in CONUS have distinct (for each lake) but spatially uniform depth. To test the sensitivity of CLM‐lake to bathymetry, we ran CLM‐lake as a stand‐alone model for all of 2019 with two bathymetry data sets for 23 selected lakes: the first had default (uniform within each lake) bathymetry while the second used a new, spatially varying bathymetry. We validated simulated lake surface temperature (LST) with both remote and in situ observations to evaluate the skill of both runs and also intercompared modeled ice cover and evaporation. Though model skill varied considerably from lake to lake, using the new bathymetry resulted in marginal improvement over the default. The more important finding is the influence bathymetry has on modeled LST (i.e., differences between model simulations) where lake‐wide LST deviated as much as 10°C between simulations and individual grid cells experienced even greater departures. This demonstrates the sensitivity of surface conditions in atmospheric models to lake bathymetry. The new bathymetry also improved lake depths over the (often too deep) previous value assumed for unknown‐depth lakes. These results have significant implications for numerical weather prediction, especially in regions near large lakes where lake surface conditions often influence the state of the atmosphere via thermal regulation and lake effect precipitation.

Temperature Increases Threaten the Congo Basin Rainforest in the Twenty-First Century More than Precipitation Changes

Abstract Western and central equatorial Africa—the Congo basin region—is one of the most convectively active regions on the planet. It supports a large, diverse tropical forest and sequesters carbon. Despite its importance, the region is understudied and poorly observed. Seasonality in the Congo basin region is defined by variations in precipitation, not temperature. The equinoctial seasons produce the highest rainfall rates. Summers are also wet, while winters are dry. A number of studies have analyzed precipitation trends in observations, but a clear signal has not emerged. GCMs do not represent the regional climate with high accuracy, and their projections disagree. Analyses of the forest health, however, document a decline. Here, convective-permitting model simulations with a 3-km resolution are used to predict greenhouse gas–forced changes in the climate of the Congo basin. Implications of the simulated changes for the health of the Congolese rainforest are evaluated. Simulated surface temperatures in the Congo basin region increase by 4.0–5.4 K by the end of the century, depending on region and season. Increases in atmospheric water vapor and changes in clouds provide important feedbacks to the radiative forcing. No statistically significant changes in regional-scale precipitation occur, but convective storms intensify locally. Since estimates based on optimal temperatures for photosynthesis suggest 4-K warming causes a decline in tropical forests, these results suggest that temperature increases pose a greater threat to the health of the Congolese rainforest than precipitation changes in the twenty-first century.

Transient Thermal Simulation of Lithium‐Ion Batteries for Hybrid/Electric Vehicles

This paper focuses on the development of a plug‐in hybrid vehicle (PHEV) full‐vehicle transient thermal model in thermal modelling software to predict the battery surface temperature at various locations. The full‐vehicle thermal model consists of a full exhaust piping system, a high‐voltage lithium‐ion battery pack system, and a battery liquid coolant system. All modes of heat transfer including conduction, forced and natural convection, radiation from the exhaust system, battery cooling, and battery internal heat generation are considered in the model. The full‐vehicle model is simulated under various vehicle conditions to represent four standard customer drive cycles. The simulated battery surface temperature at specified points along the battery module surfaces is compared to experimental vehicle test‐cell data to provide model validation. Using the results from the transient thermal simulations, prediction of the battery thermal degradation is performed throughout the entire vehicle lifecycle. The thermal degradation is estimated using thermal goals and equivalent exposure times.

Sensitivity of Fine‐Resolution Urban Heat Island Simulations to Soil Moisture Parameterization

ABSTRACTUrban areas experience the impact of natural disasters, such as heatwaves and flash floods, disparately in different neighbourhoods across a city. The demand for precise urban hydrometeorological and hydroclimatological modelling to examine this disparity, and the interacting challenges posed by climate change and urbanisation, has thus surged. The Weather Research and Forecasting (WRF) model has served such operational and research purposes for decades. Recent advancements in WRF, including enhanced numerical schemes and sophisticated urban atmospheric‐hydrological parameterizations, have empowered the simulation of urban geophysical processes at high resolution (~1 km), but even this resolution misses significant urban microclimate variability. This study applies the large‐eddy simulations (LES) mode within WRF, coupled with single‐layer urban canopy models (SLUCM), to enable even finer‐scale modelling (150 m) of the Urban Heat Island (UHI) effect in the Baltimore metropolitan area. We run nine scenarios to evaluate various methods of initializing soil moisture and various spinup lead times, and to assess the impact of WRF's Mosaic approach in depicting subgrid‐scale processes. We evaluate the scenarios by comparing the WRF simulated land surface temperature (LST) against Landsat LST and the WRF simulated hourly 2‐m air temperatures (AT) with observations from eight weather stations across the domain. Results underscore the paramount influence of the lead spinup time on the spatiotemporal distribution of simulated soil moisture, consequently shaping WRF's efficacy in predicting the UHI. Furthermore, interpolating soil moisture‐related parameters from the parent for child domain initialization yields a notable reduction in mean and root‐mean‐squared errors. This improvement was particularly evident in simulations with the longest spinup time, affirming the importance of carefully designing the initialization of soil moisture for improved urban temperature predictions.

Quantifying the Cooling Effect of Urban Greening Driven by Ecological Restoration Projects in China.

Urban greening (UG) affects local climate by altering surface energy balance, while long-term UG cooling potential, patterns, and contribution to curbing urban warming remain unclear. Here, we designed an novel statistical model to evaluate the cooling potential of UG (CPUG) and created the first CPUG map for China. By exploring the trends in observed and simulated urban surface temperatures (UST), we quantified the CPUG of 0.20 K over the past two decades, which slowed down the warming trend by 14.17% in Chinese cities. We found that the CPUG varied significantly between the urban core and sprawl areas. Specifically, the CPUG in the urban core was approximately 1.01 K, and it contributed to curbing urban warming by 56.08%, which was more than 7.2 times higher than in the sprawl areas, where the CPUG was only 0.14 K and contributed to curbing urban warming by 9.93%. We further revealed that urbanization and major ecological restoration projects are the key factors influencing CPUG, emphasizing the need for anthropogenic vegetation management to curb urban warming. The proposed model in this study provides a powerful tool for quantitatively assessing the impact of long-term UG trends on urban warming. The results of the study are an important reference for building climate-adaptive cities.

Wildfire aerosols and their impact on weather: A case study of the August 2021 fires in Greece using the WRF‐Chem model

AbstractWildfires are significant contributors to atmospheric gases and aerosols, impacting air quality and composition. This pollution from fires also affects radiative forcing, influencing short‐term weather patterns and climate dynamics. Our research employs the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) to investigate the repercussions of wildfires on aerosol abundances and associated immediate weather responses. We examine the summer season of 2021, a period marked by severe wildfire events in the country during a heatwave period. We conducted sensitivity experiments including and excluding wildfire emissions to measure their effects on aerosol optical depth (AOD), radiative forcing, and weather features such as temperature, humidity, clouds, and atmospheric circulation. Our findings demonstrate that the radiative impacts of wildfires negatively influence the local temperature over the fire smoke plume‐affected areas. Conversely, neighbouring areas of continental Greece experience increases in temperature due to remote effects of wildfire emissions, caused by meteorological feedbacks that reduce atmospheric humidity. Crucially, including fire emissions significantly improves the simulated surface temperatures predicted by the model over the Greek domain. Our work demonstrates that wildfire‐generated aerosols can significantly impact weather conditions and highlights the importance of including both local radiative effects and remote feedback for achieving more accurate weather prediction.

Constrained Projections Indicate Less Delay in Onset of Summer Monsoon over the Bay of Bengal and South China Sea

AbstractThe summer monsoon onset over the Bay of Bengal and South China Sea signals the beginning of the Asian summer monsoon, critical for local fisheries, agriculture and livelihoods, so communities are concerned about its potential changes under global warming. Previous projections have suggested a delay, but the extent of this delay remains uncertain, undermining the reliability of the projections. Here, we show a significant correlation between the projected shift in Bay of Bengal/South China Sea monsoon onset and present‐day sea surface temperature (SST) simulation over the western Pacific (WP). This emergent relationship arises from the spread of the precipitation response over the western‐central Pacific to WP SST, as more precipitation induces stronger tropical upper‐tropospheric warming, increasing westerly vertical shear near South Asia, and facilitating the onset delay. The rectified projections indicate that the delayed shift is almost halved compared to raw projections, and the intermodel uncertainty is reduced by 30%.

Improved WRF simulation of surface temperature and urban heat island intensity over Metro Manila, Philippines

Accurate representation of the land use/land cover (LULC) in the Weather Research and Forecasting (WRF) model has been shown to be crucial for improved simulation of surface variables and urban heat island (UHI) phenomenon. In this study, a ground truthed LULC dataset over Metro Manila (MM) was integrated in the WRF model to assess and quantify the relative contributions of using an updated land use/land cover (ULULC), activating the single-layer urban canopy model (WSLUCM) and anthropogenic heat (WAH), and consideration of urban surface heterogeneity (LIR) representation on simulated Surface Temperature (ST) relative to the default model settings (CTRL) of the WRF model. Additionally, the study examines the daytime and nighttime UHI features over MM, using a reference simulation where urban grids were replaced with cropland (CRP). Numerical simulations were carried out for April 2018, a typical dry month without monsoonal influence over MM to get a clear UHI characterization. Data from nine weather stations distributed across MM was used for performance evaluation. Results show negligible daytime biases in ST for CTRL, ULULC, and WAH, and underestimated ST for WSLUCM. At night, CTRL and ULULC (WSLUCM and WAH) tend to overestimate (underestimate) ST. Of the four factors analyzed, updating LULC, using SLUCM, including AH, and accounting for urban surface heterogeneity can change daytime ST by 0.3 °C, -0.6 °C, 0.3 °C, and 0 °C, respectively, and nighttime ST by 0.5 °C, -1.2 °C, 0.3 °C, and 0.4 °C, respectively. Widespread daytime warming and a distinct nocturnal UHI following urbanization were confirmed for MM when the LULC accounted for urban heterogeneity. While data limitations for MM limit the specification of the SLUCM, its UHI features are best represented in WRF when an updated land cover dataset is used.

Impact of Convective and Land Surface Parameterization Schemes on the Simulation of Surface Temperature and Precipitation Using RegCM4.7 During Summer Period Over the DPR Korea

Impact of Convective and Land Surface Parameterization Schemes on the Simulation of Surface Temperature and Precipitation Using RegCM4.7 During Summer Period Over the DPR Korea

Network-based constraint to evaluate climate sensitivity

The 2015 Paris agreement was established to limit Greenhouse gas (GHG) global warming below 1.5°C above preindustrial era values. Knowledge of climate sensitivity to GHG levels is central for formulating effective climate policies, yet its exact value is shroud in uncertainty. Climate sensitivity is quantitatively expressed in terms of Equilibrium Climate Sensitivity (ECS) and Transient Climate Response (TCR), estimating global temperature responses after an abrupt or transient doubling of CO2. Here, we represent the complex and highly-dimensional behavior of modelled climate via low-dimensional emergent networks to evaluate Climate Sensitivity (netCS), by first reconstructing meaningful components describing regional subprocesses, and secondly inferring the causal links between these to construct causal networks. We apply this methodology to Sea Surface Temperature (SST) simulations and investigate two different metrics in order to derive weighted estimates that yield likely ranges of ECS (2.35–4.81°C) and TCR (1.53-2.60°C). These ranges are narrower than the unconstrained distributions and consistent with the ranges of the IPCC AR6 estimates. More importantly, netCS demonstrates that SST patterns (at “fast” timescales) are linked to climate sensitivity; SST patterns over the historical period exclude median sensitivity but not low-sensitivity (ECS < 3.0°C) or very high sensitivity (ECS ≥ 4.5°C) models.

Effect of Tropical Cyclone Wind Forcing on Improving Upper Ocean Simulation: An Idealized Study

We examined how wind forcing affects the upper ocean response under idealized tropical cyclone (TC) conditions. In this study, we constructed three parameterized wind fields with varying spatial and temporal resolutions for TCs of different intensities and translation speeds. The simulated surface and subsurface temperatures were cooler and deeper when using the blended wind fields owing to their higher wind speeds compared to those from coarse–resolution wind fields. Additionally, TC–induced currents were significantly stronger on the right side of the TC track, with notable differences in current velocities. Similar to the increase in ocean currents, the simulated turbulent kinetic energy driven by the blended winds is significantly higher than that simulated by the coarse-resolution wind fields. These findings suggest that using high-quality wind fields to drive ocean models can enhance the accuracy of the upper ocean response to TCs. The sensitivity of the upper ocean responses to wind forcing depends on the TC’s intensity and translation speed. Stronger and slower-moving TCs induce greater vertical shear and enhanced mixing. Therefore, accurate wind stress as a surface boundary condition is crucial for numerical ocean models.

Seasonal Cycle Delay of the Western North Pacific Tropical Cyclone Genesis Frequency in CMIP6 Simulations

AbstractObvious biases in simulating tropical cyclone (TC) genesis of the current climate models hamper our understanding of TC changes. In this study, we found a delay of the seasonal cycle of TC genesis frequency over the western North Pacific (WNP) in most Coupled Model Intercomparison Project Phase 6 models. During the active TC season, the simulated south‐warming and north‐cooling surface temperature bias amplifies the meridional gradient and excites thermal winds. This weakens the western North Pacific Subtropical High and easterly monsoon trough, which further reduces TC genesis frequency over the western WNP in summer. But in autumn, positive TC genesis biases were only observed in coupled models over the eastern WNP. Both seasons contribute to the delayed seasonal cycle of TC frequency in models. Our findings highlight the importance of accurate simulation of surface temperature by climate models to TC simulations and aid in future model improvements.

Simulation of urban surface temperature and surface heat balance in the Tokyo metropolitan area

This study aimed to propose a method for simulating ground surface temperature and surface heat balance using thermal infrared remote sensing and an urban surface heat balance model and to conduct a simulation in the Tokyo metropolitan area. Previous studies have typically employed ground surface temperatures obtained through satellite remote sensing to analyze urban thermal environments. However, these studies were limited to using surface temperature data available only at the time of satellite passage. Therefore, in this study, we conducted a simulation for the Tokyo metropolitan area by integrating a one-dimensional heat balance model with ground surface temperatures observed by satellites. The novelty of this study lies in the utilization of parameters related to heat balance obtained through calculations of physical processes, as opposed to relying on empirical formulas or parameters. Consequently, realistic ground surface characteristics, including parameters such as evaporation efficiency and thermal inertia essential for the simulation, were derived by this combined method. Furthermore, diurnal variations in ground surface temperature distributions were accurately represented by using realistic surface temperature data based on satellite remote sensing, considering the effect of heat storage in urban areas—an aspect challenging to reproduce using conventional local meteorological models.

Impact of Satellite Wind on Improving Simulation of the Upper Ocean Response to Tropical Cyclones

Accurate modeling of the ocean response to tropical cyclones (TCs) requires high-quality wind fields to force ocean models. In this study, blended wind fields are generated using multi-source satellite data and the Climate Forecast System Reanalysis (CFSR) wind data. We utilize the hybrid wind fields to drive the Regional Ocean Modeling System (ROMS) for simulating oceanic dynamic and thermodynamic parameters. The model’s simulated ocean surface and sub-surface temperatures, as well as current speeds, are generally consistent with satellite and in situ observations collected during TC Winston and Freddy. The results are significantly better than those simulated by ROMS using wind forcing from CFSR alone. These results suggest that incorporating satellite wind data into the atmospheric forcing has the potential to enhance vertical mixing and improve simulations of the upper ocean response to TCs.

Evaluation of the characteristics of Indian summer monsoon simulated by CMIP6 models

AbstractThe present research is aimed at evaluating the climate coupled models with respect to the observational datasets for the investigation of the characteristics of the Indian summer monsoon (ISM). The monthly averaged historical simulations from 12 coupled climate models that participated in Coupled Model Intercomparison Project Phase 6 (CMIP6) are compared with the ground based observational data, satellite and reanalysis datasets for the study period of 1980–2014. This study uses these high‐resolution models to investigate monsoon features, onset and withdrawal dates, primarily focusing on individual model performances rather than highly documented multi‐model mean performance. Performance evaluation of the models suggests that sea surface temperature (SST) simulations by the models are in better agreement for the Arabian Sea than the Bay of Bengal with respect to the ERA5 reanalysis data sets. Further, inter‐model differences amongst the CMIP6 models in estimating the spatial distribution of various monsoon system variables during pre‐monsoon and monsoon seasons are noted which may be attributed to the different model components and varying physics configuration, nonetheless, models like NESM3 and INM‐CN5 are able to reproduce ISM pattern reasonably well. The annual precipitation cycle demonstrates a good agreement between most climate models and IMD data. Evolution of tropospheric temperature gradient (ΔTT) estimated from the CMIP6 models mimics the temporal pattern of the annual rainfall and therefore, is used to estimate the onset and withdrawal dates from CMIP6 models; however, high variability is noted amongst the CMIP6 models in retrieving the onset and withdrawal dates when compared with the IMD observations. Most of the models show a shorter rainy season except NorESM2‐MM. Overall our results suggest that the CMIP6 models can be used for the seasonal mean evaluation of monsoon system parameters and process‐based studies to improve our present understanding of the ISM system.

First comprehensive assessment of industrial-era land heat uptake from multiple sources

Abstract. The anthropogenically intensified greenhouse effect has caused a radiative imbalance at the top of the atmosphere during the industrial period. This, in turn, has led to an energy surplus in various components of the Earth system, with the ocean storing the largest part. The land contribution ranks second with the latest observational estimates based on borehole temperature profiles, which quantify the terrestrial energy surplus to be 6 % in the last 5 decades, whereas studies based on state-of-the-art climate models scale it down to 2 %. This underestimation stems from land surface models (LSMs) having a subsurface that is too shallow, which severely constrains the land heat uptake simulated by Earth system models (ESMs). A forced simulation of the last 2000 years with the Max Planck Institute ESM (MPI-ESM) using a deep LSM captures 4 times more heat than the standard shallow MPI-ESM simulations in the historical period, well above the estimates provided by other ESMs. However, deepening the LSM does not remarkably affect the simulated surface temperature. It is shown that the heat stored during the historical period by an ESM using a deep LSM component can be accurately estimated by considering the surface temperatures simulated by the ESM using a shallow LSM and propagating them with a standalone forward model. This result is used to derive estimates of land heat uptake using all available observational datasets, reanalysis products, and state-of-the-art ESM experiments. This approach yields values of 10.5–16.0 ZJ for 1971–2018, which are 12 %–42 % smaller than the latest borehole-based estimates (18.2 ZJ).

ФИЗИЧЕСКИ ОБОСНОВАННАЯ СХЕМА ПАРАМЕТРИЗАЦИИ ПРОЕКТИВНОГО ПОКРЫТИЯ РАСТИТЕЛЬНОСТИ

The leaf area index (LAI) and fractional vegetation cover (FVC) are very important parameters in land-atmosphere interactions. In this study, a very simple but robust and mechanism-based method was developed to derive FVC data based on the relationships between the canopy gap fraction, LAI, and direct solar extinction coefficient. For validation, the LAI data and NDVI-based and mechanism-based FVC data were assimilated into the integrated urban land model (IUM). Using the mechanism-based FVC data as the input, the simulation of the annual average land surface temperatures (LSTs) in the Beijing area were improved compared with those using the NDVI-based FVC data as the input.

The surface temperatures of flat areas on the Moon

Lunar surface temperature (LST) is a quantity of special interest for interpreting the thermal character of the regolith. It is affected by the solar irradiance, earthshine, and heat flow for the flat areas on the Moon. We present an improved transient temperature model to calculate temperatures of lunar flat surfaces. The model consists of one-dimensional thermal diffusion equation and two boundary conditions. The improved lunar surface boundary condition allows one to precisely determine the effective solar irradiance (ESI) and earthshine. The simulated surface temperatures suggest consistency with the measured temperatures from the thermocouples of Apollo 15 and 17 heat flow experiments. From the LST simulated with the improved model, it is found the annual-seasonal variations present obvious latitude characters, with the highest surface temperature occurring in late October, November, and December separately at high (&amp;gt;∼72°) latitudes, middle latitudes, and low (&amp;lt;∼20°) latitudes, respectively; the lowest surface temperatures occur in late July. Furthermore, the discrepancies between the maximum and minimum temperatures decrease as latitude increases, as do the maximum and minimum lunar surface temperatures. The surface daytime temperature would change by 179.4 K with a 1322.5 W/m2 change in the ESI. A 0.12 W/m2 and 0.02 W/m2 change of the earthshine and heat flow would lead to 0.5 and 0.09 K in surface nighttime temperature, respectively.

Simulating influences of land use/land cover composition and configuration on urban heat island using machine learning

Urban heat island (UHI) has become a worldwide concern under global warming and urbanization waves, which pose significant threats to human health and urban sustainability. Despite Land Use/Land Cover (LULC) being regarded as a crucial factor influencing UHI, few studies simulated the nonlinear influences from LULC compositions and configurations, especially considering the difference between monocentric and polycentric cities. Therefore, we measured the patterns of LULC and UHI from 2006 to 2022 using cases of a monocentric city (Chengdu) and a polycentric city (Chongqing). Further, we simulated future and land surface temperature (LST) using Machine Learning (ML) models, and further calculated the simulated UHI in 2030 by urban-rural LST differences. The results showed that Chengdu had prominent UHI intensity in cores and the surrounding satellite towns. Chongqing's main center and subcenters had high UHI intensity under clustered patterns. ML results indicated that built-up percentage was the most crucial factor driving LST. Configuration factors dominated in impacting Chengdu's LST, while composition factors were more critical in Chongqing. Moreover, future UHI is estimated to decrease in Chongqing's core, compared with the remaining high UHI in Chengdu's core. These results might provide insights for understanding and mitigating future UHI effects.

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks: Climatology, Interannual Variability, and Extremes

Correcting Climate Model Sea Surface Temperature Simulations with Generative Adversarial Networks: Climatology, Interannual Variability, and Extremes