Shortwave Radiation Research Articles

Increasing heat waves frequencies over India during post-El Niño spring and early summer seasons

The increasing frequency of extreme Heat Waves (HWs) has generated significant societal impacts in recent years. This study used different observational datasets to investigate the HW characteristics over India during the post-El Niño spring and early summer seasons (April to June; AMJ). Analysis suggests that HW days are more prevalent over India, predominantly increased in south-central and northwest India, during the decaying phase of strong El Niño years. It is found that anomalous anticyclone circulation accompanied by high pressure extending from the Western North Pacific region towards the Bay of Bengal and India is responsible for enhanced HW days and intensity during the AMJ of strong El Niño decay years. This anomalous anticyclone-induced downdraft reduces the specific humidity in the lower troposphere, leading to decreased cloud cover over India. As a result, shortwave radiation is enhanced at the surface, which causes abnormal HWs over India. During the decaying phase of strong El Niño years, the HW days over India contributed to an increase in the frequency of Discomfort Index hours (above 28 °C), maximum temperatures exceeding 40 °C (hours per day), and Universal Thermal Climate Index days above 38 °C and 46 °C during the spring and early summer months, especially in the East Coast, central, and northwestern parts of India. Thus, proper prediction of large-scale atmospheric circulation over the Indo-western Pacific region during El Niño can help to predict the HW conditions three months in advance. This would help to implement suitable adaptation measures and put into practice strong mitigation policies to limit the increased risk of such events during AMJ of El Niño decay years.

On the sensitivity of aerosol–cloud interactions to changes in sea surface temperature in radiative–convective equilibrium

Abstract. Clouds play a vital role in regulating Earth's energy balance and are impacted by anthropogenic aerosol concentration (Na) and sea surface temperature (SST) alterations. Traditionally, these factors, aerosols and SST, are investigated independently. This study employs cloud-resolving, radiative–convective-equilibrium (RCE) simulations to explore aerosol–cloud interactions (ACIs) under varying SSTs. ACIs are found to be SST-dependent even under RCE conditions. Notably, changes in cloud radiative effects for both longwave radiation and shortwave radiation lead to a decrease in top-of-atmosphere (TOA) energy gain with increasing Na. The changes in TOA shortwave flux exhibit greater sensitivity to underlying SST conditions compared to longwave radiation. To comprehend these trends, we perform a linear decomposition, analyzing the responses of different cloud regimes and contributions from changes in the cloud's opacity and occurrence. This breakdown reveals that ice and shallow clouds predominantly contribute to the radiative effect, mostly due to changes in the cloud's opacity and due to the Twomey effect, which is proportional to the baseline cloud fraction. Moreover, with an increase in Na, we observe an increase in latent heat release at the upper troposphere associated with heightened production of snow and graupel. We show that this trend, consistently across all SSTs, affects the anvil cloud cover by affecting the static stability at the upper troposphere via a similar mechanism to the stability iris effect, resulting in an increase in outgoing longwave radiation. In conclusion, under the ongoing climate change, studying the sensitivity of clouds to aerosols and SST should be conducted concomitantly as mutual effects are expected.

Effects of increasing spatial resolution on the spatial information content and accuracy of downward surface shortwave radiation

Downward Surface Shortwave Radiation (DSSR) plays a crucial role in ecological, hydrological, and solar photovoltaic studies. The estimation of DSSR at higher spatial resolutions is increasingly popular. However, the impact of increasing spatial resolution on spatial information content and accuracy of DSSR estimates is not well understood. Addressing this knowledge gap, an all-sky DSSR dataset at varying spatial resolutions (0.8° to 0.01°) in China was estimated using MODIS top-of-atmosphere reflectance data. Subsequently, the spatial heterogeneity and accuracy of the DSSR dataset as well as its driving factors were analyzed. The results indicate that the magnitude of the increase in spatial heterogeneity varies across different regions as the spatial resolution increases. On the edge of the Qinghai-Tibet Plateau, the spatial heterogeneity experiences the most significant increase. To quantify the factors driving the spatial heterogeneity of DSSR, the Correlation Coefficients (CC) were calculated between the Coefficient Of Variation (COV) of DSSR and the COVs of relevant factors. In cloudy-sky conditions, the CC between the COV of DSSR and cloud optical thickness ranged from 0.54 to 0.62, whereas the CC between DSSR COV and Column Water Vapor (CWV) COV was between 0.72 and 0.78. Under clear-sky conditions, the COV of CWV, aerosol optical depth, and PM2.5 showed more significant correlations with DSSR COV compared to altitude COV. Validation using ground-based measurements revealed that the accuracy of DSSR decreased at coarser spatial resolutions due to the presence of mixed pixel samples. Moreover, it was demonstrated that a spatial resolution of 0.05° offered the highest cost-effectiveness when estimating DSSR. These insights significantly advance our understanding of the role of spatial resolutions in DSSR estimation and are instrumental in guiding the selection of optimal resolutions for such studies.

Enabling the Assimilation of CrIS Shortwave Infrared Observations in Global NWP at NOAA. Part I: Background and Methods

Abstract Radiance observations from Earth-observing satellites have a significant positive impact on numerical weather prediction (NWP) forecasts, but some spectral regions are not fully exploited. Observations from hyperspectral infrared (IR) sounders in the longwave region (650-1100 cm−1), for instance, are routinely assimilated in many NWP models, but observations in the shortwave region (2155-2550 cm−1) are not. Each of these regions provides information on the temperature structure of the atmosphere, but the shortwave IR (SWIR) region is considered challenging to assimilate due to noise equivalent delta temperature (NEDT) that is highly variable depending on scene brightness temperature and to phenomena that are difficult to model, like non-Local Thermodynamic Equilibrium (NLTE) and solar reflectance. With recent advances in small-satellite technology, SWIR temperature sounders may provide an agile and cost-effective complement to the current constellation of IR sounders. Therefore, a better understanding of the use and impact of SWIR observations in data assimilation for NWP is warranted. In part one of this study, as presented here, the amount of unique information (as determined by Empirical Orthogonal Decomposition (EOD)) made available to a data assimilation system by Cross-track Infrared Sounder (CrIS) SWIR observations is reviewed, recent advancements to the Community Radiative Transfer Model (CRTM) for the simulation of CrIS shortwave radiances are tested, and enhancements to NOAA’s Global Data Assimilation System (GDAS) for the assimilation of CrIS SWIR observations are implemented and evaluated. Part two of this study, which seeks to assess the value of assimilating shortwave IR observations in global NWP, is also introduced.

Correction: impact of vegetation greening on TOA clear-sky shortwave radiation in Northwest India

Correction: impact of vegetation greening on TOA clear-sky shortwave radiation in Northwest India

Physically based modelling of spectral transmittance through forest canopies

Abstract Physically based models simulating the spectral transmittance of solar radiation through forest canopies are useful tools for examining the connections between the shortwave radiation environment and the productivity and biodiversity of the forest floor. We report a comprehensive evaluation of two approaches simulating forest canopy spectral transmittance. The approaches were (i) three‐dimensional radiative transfer modelling in canopies composed of individual trees filled with turbid media and (ii) photon recollision probability theory (p‐theory), and were implemented using DART‐FT and PARAS models, respectively. The simulations were evaluated against mean and standard deviation of canopy transmittance spectra measured under clear‐sky conditions in forest plots across central and Northern Europe. In general, both models agreed well with the in situ measurements. They performed equally in conifer forests, while PARAS had a slightly lower accuracy than DART‐FT in broadleaved forests. We conclude that both approaches produce realistic simulations of canopy spectral transmittance at the spatial scale tested in this study, and that p‐theory constitutes a computationally efficient and easy‐to‐parameterize alternative to three‐dimensional radiative transfer.

Interplay of atmosphere and ocean amplifies summer marine extremes in the Barents Sea at different timescales

Marine extremes are recognized to cause severe ecosystem and socioeconomic impacts. However, in polar regions, such as the Barents Sea, the driving mechanisms of these extremes remain poorly understood and require careful consideration of the observed long-term ocean warming. Here we show that on short time scales of a few days, marine heatwaves and marine cold spells are dynamically driven by a dipole atmospheric circulation pattern between the Nordic Seas and the Barents Sea. Importantly, the dipole’s eastern component determines anomalies in shortwave radiation and latent heat fluxes. On interannual time scales, both changes in ocean heat supply and persistent atmospheric patterns can support severe marine extremes. We apply conventional marine heatwave detection methodology to OISSTv2 data, for the period of 1982–2021, and combine the analysis with ERA5 data to identify drivers. The ocean-atmosphere interplay across scales provides valuable information that can be integrated into fisheries and ecosystem management frameworks.

Effects of slow temperature acclimation of photosynthesis on gross primary production estimation

The slow temperature acclimation of photosynthesis has been confirmed through early field experiments and studies. However, this effect is difficult to characterize and quantify with some simple and easily accessible indicators. As a result, the impact of slow temperature acclimation of photosynthesis on gross primary production (GPP) estimation has often been overlooked or not integrated into most GPP models. In this study, we used a theorical variable-state of acclimation (S), to characterize the slow temperature acclimation. This variable represents the temperature to which the photosynthetic machinery adapts and is defined as a function of air temperature (Ta) and time constant (τ) required for vegetation to respond to temperature, to discuss its impact on GPP simulation. We used FLUXNET2015 dataset to calculate S and established a GPP model using S and shortwave radiation (SW) based on random forest algorithm (S model). As a comparison, we directly used Ta and SW to build the other GPP model (Ta model). Moreover, the divergent temperature acclimation capacities of plants are crucial to predict and make preparations for likely temperature stress in the future. Therefore, the spatial distribution of τ values was also mapped using satellite sun induced chlorophyll fluorescence (SIF) and Ta datasets. The results indicated that: (1) taking into account the slow temperature acclimation of photosynthesis led to a more precise estimation of GPP which mainly reflected in reduction of excessive fluctuations in GPP predictions; (2) considering the slow temperature acclimation of photosynthesis can reduce the sensitivity of vegetation to temperature; (3) the improvement of S model in GPP estimations was different in different vegetation growth stages which was more significant in the springtime recovery stage; (4) τ values had significant spatial distribution which was strongly affected by the determinants of vegetation growth and seasonal variations in temperature.

Near-surface ocean temperature and air-sea heat flux observed by a buoy array during summer to autumn in year 2014 in the northern South China Sea

The observational data of the air-sea interface and upper ocean elements from a buoy array deployed in the northern South China Sea from June to November 2014 is analyzed. The COARE 3.0 algorithm was adopted to examine the variability of air-sea heat fluxes and their contributions to near-surface ocean temperature changes. During the observation period, air-sea heat exchange in the northern South China Sea is primarily determined by solar shortwave radiation and latent heat flux, with net heat flux mostly negative, indicating that the ocean predominantly loses heat. In autumn, net heat flux loss significantly increased by an average of 34.4 W/m2, primarily due to enhanced latent heat loss driven by stronger winter monsoon. During typhoon period, net air-sea heat flux was significantly suppressed, mainly due to reduced solar shortwave radiation, with minimal contribution from sensible heat flux. Near-surface ocean temperature exhibited a seasonal cooling trend, averaging 29.7°C in summer and 28.4°C in autumn. The maximum cooling during typhoon reached 3.1°C, with minimal contribution from air-sea heat flux. During the monsoon transition period, weaker winds led to a slight increase in near-surface ocean temperature, with air-sea heat flux contributing 70.2% and 56.3% to this process. During winter monsoon, more uniform water column mixing resulted in a gradual decrease in near-surface ocean temperature, with air-sea heat flux contributing over 60% to this process.

Arctic September Sea Ice Concentration Biases in CMIP6 Models and Their Relationships with Other Model Variables

Abstract The models that participated in the Coupled Model Intercomparison Project (CMIP) exhibit large biases in Arctic sea ice climatology that seem related to biases in seasonal atmospheric and oceanic circulations. Using historical runs of 34 CMIP6 models from 1979 to 2014, we investigate the links between the climatological sea ice concentration (SIC) biases in September and atmospheric and oceanic model climatologies. The main intermodel spread of September SIC is well described by two leading EOFs, which together explain ∼65% of its variance. The first EOF represents an underestimation or overestimation of SIC in the whole Arctic, while the second EOF describes opposite SIC biases in the Atlantic and Pacific sectors. Regression analysis indicates that the two SIC modes are closely related to departures from the multimodel mean of Arctic surface heat fluxes during summer, primarily shortwave and longwave radiation, with incoming Atlantic Water playing a role in the Atlantic sector. Local and global links with summer cloud cover, low-level humidity, upper or lower troposphere temperature/circulation, and oceanic variables are also found. As illustrated for three climate models, the local relationships with the SIC biases are mostly similar in the Arctic across the models but show varying degrees of Atlantic inflow influence. On a global scale, a strong influence of the summer atmospheric circulation on September SIC is suggested for one of the three models, while the atmospheric influence is primarily via thermodynamics in the other two. Clear links to the North Atlantic oceanic circulation are seen in one of the models.

Weather Research and Forecasting Model (WRF) Sensitivity to Choice of Parameterization Options over Ethiopia

Downscaling seasonal climate forecasts using regional climate models (RCMs) became an emerging area during the last decade owing to RCMs’ more comprehensive representation of the important physical processes at a finer resolution. However, it is crucial to test RCMs for the most appropriate model setup for a particular purpose over a given region through numerical experiments. Thus, this sensitivity study was aimed at identifying an optimum configuration in the Weather, Research, and Forecasting (WRF) model over Ethiopia. A total of 35 WRF simulations with different combinations of parameterization schemes for cumulus (CU), planetary boundary layer (PBL), cloud microphysics (MP), longwave (LW), and shortwave (SW) radiation were tested during the summer (June to August, JJA) season of 2002. The WRF simulations used a two-domain configuration with a 12 km nested domain covering Ethiopia. The initial and boundary forcing data for WRF were from the Climate Forecast System Reanalysis (CFSR). The simulations were compared with station and gridded observations to evaluate their ability to reproduce different aspects of JJA rainfall. An objective ranking method using an aggregate score of several statistics was used to select the best-performing model configuration. The JJA rainfall was found to be most sensitive to the choice of cumulus parameterization and least sensitive to cloud microphysics. All the simulations captured the spatial distribution of JJA rainfall with the pattern correlation coefficient (PCC) ranging from 0.89 to 0.94. However, all the simulations overestimated the JJA rainfall amount and the number of rainy days. Out of the 35 simulations, one that used the Grell CU, ACM2 PBL, LIN MP, RRTM LW, and Dudhia SW schemes performed the best in reproducing the amount and spatio-temporal distribution of JJA rainfall and was selected for downscaling the CFSv2 operational forecast.

Urbanization Effect on Regional Thermal Environment and Its Mechanisms in Arid Zone Cities: A Case Study of Urumqi

Urumqi is located in the arid region of northwestern China, known for being one of the most delicate ecological environments and an area susceptible to climate change. The urbanization of Urumqi has progressed rapidly, yet there is a lack of research on the urbanization effect (UE) in Urumqi in terms of the regional climate. This study investigates the UE of Urumqi (urban built-up area) on the regional thermal environment and its mechanisms for the first time, based on the WRF (Weather Research and Forecasting) model (combined with the Urban Canopy Model, UCM) simulation data of 10 consecutive years (2012–2021). The results show that the UE on surface temperature (Ts) and air temperature at 2 m (T2m) is strong (weak) during the night (daytime) in all seasons, and the UE on these is largest (smallest) in spring (winter). In addition, the maximum UE on both Ts and T2m is present over southern Urumqi in winter, whereas the maximum UE is identified over the northern Urumqi in other seasons. The maximum UE on Ts occurred in northwestern Urumqi at 18 LST (Local Standard Time, i.e., UTC+6) in autumn (reaching 5.2 °C), and the maximum UE on T2m occurred in northern Urumqi at 4 LST in summer (reaching 2.6 °C). Urbanization showed a weak cooling effect during daytime in summer and winter, reflecting the unique characteristics of the UE in arid regions, which are different from those in humid regions. The maximum cooling of Ts occurred in northern Urumqi at 11 LST in summer (reaching −0.4 °C), while that of T2m occurred at 10 LST in northern and northwestern Urumqi in winter (reaching −0.25 °C), and the cooling effect lasted for a longer period of time in summer than in winter. The UE of Urumqi causes the increase of Ts mainly through the influence of net short-wave radiation and geothermal flux and causes the increase of T2m through the influence of sensible heat flux and net long-wave radiation. The UE on the land surface energy balance in Urumqi can be used to explain the seasonal variation and spatial differences of the UEs on the regional thermal environment and the underlying mechanism.

Quantifying Changes in the Arctic Shortwave Cloud Radiative Effects

AbstractThe shortwave cloud radiative effect (SWCRE) is important for the Arctic surface radiation budget and is a major source of inter‐model spread in simulating Arctic climate. To better understand the individual contributions of various radiative processes to changes in SWCRE, we extend the existing Approximate Partial Radiative Perturbation (APRP) method by adding the absorptivity for the upward beam, considering differences in reflectivity between upward and downward beams, and analyzing the cloud masking effect resulting from changes in surface albedo. Using data from CMIP model experiments, the study decomposes the SWCRE over the Arctic surface and analyzes inter‐model differences in quadrupled CO2 simulations. The study accounts for the influence of surface albedo, cloud amount, and cloud microphysics in the response of SWCRE to Arctic warming. In the sunlit season, CMIP models exhibit a strong, negative SWCRE with a large inter‐model spread. Arctic clouds dampen the surface albedo feedback by reflecting incoming solar radiation and further decrease the shortwave radiation reflected by surface, a fraction of which is scattered back to the surface by clouds. Specifically, this accounts for the majority of the inter‐model spread in SWCRE. In addition, increased (decreased) cloud amount and cloud liquid water reduce (increase) incoming shortwave fluxes at the surface, but they are found to be not critical to the Arctic surface radiation budget and its inter‐model variation. Overall, the extended APRP method offers a useful tool for analyzing the complex interactions between clouds and radiative processes, accurately decomposes the individual SWCRE responses at the Arctic surface.

Better representation of vegetation phenology improves estimations of annual gross primary productivity

Carbon uptake by vegetation plays a vital role in the global carbon cycle. Annual gross primary productivity (AGPP) represents the total amount of carbon compounds produced by vegetation photosynthesis over a year and is a crucial metric for quantifying vegetation carbon uptake. Although some theories have been developed to explain the spatiotemporal variation in AGPP, they often overlook the seasonal differences in photosynthesis across vegetation phenological periods. This gap highlights the need for a more reasonable representation of vegetation phenology in AGPP modelling. Therefore, we developed a novel theoretical AGPP model that decomposes AGPP into detailed vegetation phenological periods (green-up, maturation, and senescence) and investigated the effects of climatic factors on the seasonal components of AGPP. Compared with existing models, our model considers the length of the multiple vegetation phenological periods, rather than just the length of the carbon uptake period. When evaluated against flux tower data, our model outperformed the comparative model, with a higher determination coefficient and lower root mean square error. The analysis also demonstrated that the developed model can reproduce spatial and temporal variations in satellite-based global AGPP. In addition, we identified distinct differences in the responses of AGPP's seasonal components to climatic factors: shortwave radiation predominantly affected the seasonal component of AGPP during senescence, air temperature during green-up, and vapor pressure deficit during maturation. Our study proposes a potential mechanism for AGPP estimation and highlights the importance of accurately representing vegetation phenology in AGPP modelling.

Antarctic Warm Extremes Across Seasons and Their Response to Advection

AbstractAntarctic warm extremes impact the cryosphere, with very warm extremes driving surface melt on ice shelves. Here, we analyze temperatures exceeding the 90th percentile and the associated circulation patterns and radiation anomalies. ERA5 reanalysis data show positive geopotential height anomalies related to the occurrence of warm extremes. The highest temperature during warm extremes appears on the western periphery of high‐pressure systems, consistent with anticyclonic advection. Temperature anomalies during warm extremes are strongest in winter due to the transport of warm and moist air and a strong meridional temperature gradient. In summer, the weak meridional gradients of top‐of‐atmosphere downward solar radiation flux and surface air temperature contribute to weak temperature anomalies. Warm extremes are associated with positive longwave radiation anomalies in all seasons, but with negative shortwave radiation anomalies at the surface except during polar night. These relationships are verified by station observations. Our results confirm that Antarctic warm extremes are mostly driven by meridional advection of warm air, and suggest that these warm air masses are predominantly moist and cloudy.

Pivotal Role of Mixed‐Layer Depth in Tropical Atlantic Multidecadal Variability

AbstractThe tropical arm of Atlantic Multidecadal Variability (AMV) influences climate worldwide, yet the mechanisms generating it remain unclear. Here, we examine experiments with sea surface temperature (SST)‐restoring in the extratropical North Atlantic in multiple models and use mixed‐layer heat budgets to elucidate the important mechanisms. Our results demonstrate that the tropical AMV is driven by wind‐mixed‐layer‐SST feedback. The evolution has two phases with tropical AMV SST anomalies growing from April to October and decaying from November to March. The amplitude of the growth phase surpasses that of the decay phase, resulting in overall tropical Atlantic warming during positive AMV phases. During summer, positive SST anomalies in the extratropics weaken the trade winds, resulting in a shallower mixed‐layer with reduced heat capacity. Subsequent absorption of climatological shortwave radiation in this shallower mixed‐layer then causes SSTs to warm, generating the tropical AMV. Importantly, anomalous surface heat‐fluxes make modest contributions to tropical AMV in these experiments.

The emission, transport, and impacts of the extreme Saharan dust storm of 2015

Abstract. ​​​​​​​Each summer, the Saharan Air Layer (SAL) transports massive amounts of mineral dust across the Atlantic Ocean, affecting weather, climate, and public health over large areas. Despite the considerable impacts of African dust, the causes and impacts of extreme trans-Atlantic African dust events are not fully understood. The “Godzilla” trans-Atlantic dust event of 2020 has been extensively studied, but little is known about other similar events. Here, we examine the June 2015 event, the second strongest trans-Atlantic African dust event that occurred during the summers from 2003–2022. This event was characterized by moderately high dust emissions over western North Africa and an extremely high aerosol optical depth (AOD) over the tropical North Atlantic. The high dust loading over the Atlantic is associated with atmospheric circulation extremes similar to the Godzilla event. Both the African easterly jet (AEJ) and Caribbean low-level jet (CLLJ) have greatly intensified, along with a westward extension of the North Atlantic subtropical high (NASH), all of which favor the westward transport of African dust. The enhanced dust emissions are related to anomalously strong surface winds in dust source regions and reduced vegetation density and soil moisture across the northern Sahel. The dust plume reduced net surface shortwave radiation over the eastern tropical North Atlantic by about 25 W m−2 but increased net longwave flux by about 3 W m−2. In contrast to the Godzilla event, the 2015 event had minor air quality impacts on the US, partially due to the extremely intensified CLLJ that dispersed the dust plume towards the Pacific.

Estimating the Impact of a 2017 Smoke Plume on Surface Climate Over Northern Canada With a Climate Model, Satellite Retrievals, and Weather Forecasts

AbstractIn August 2017, a smoke plume from wildfires in British Columbia and the Northwest Territories recirculated and persisted over northern Canada for over two weeks. We compared a full‐factorial set of NASA Goddard Institute for Space Studies ModelE simulations of the plume to satellite retrievals of aerosol optical depth and carbon monoxide, finding that ModelE performance was dependent on the model configuration, and more so on the choice of injection height approach, aerosol scheme and biomass burning emissions estimates than to the choice of horizontal winds for nudging. In particular, ModelE simulations with free‐tropospheric smoke injection, a mass‐based aerosol scheme and comparatively high fire NOx emissions led to unrealistically high aerosol optical depth. Using paired simulations with and without fire emissions, we estimated that for 16 days over an 850,000 km2 region, the smoke decreased planetary boundary layer heights by between 253 and 547 m, decreased downward shortwave radiation by between 52 and 172 Wm−2, and decreased surface temperature by between 1.5°C and 4.9°C, the latter spanning an independent estimate from operational weather forecasts of a 3.7°C cooling. The strongest surface climate effects were for ModelE configurations with more detailed aerosol microphysics that led to a stronger first indirect effect.

Quasi-Force-Free Magnets of Small Volume for Generators of Short-Wave Microwave Radiation

Quasi-Force-Free Magnets of Small Volume for Generators of Short-Wave Microwave Radiation

Research on the outdoor thermal comfort of children in Hangzhou and Its influence on the underlying surface reflectance.

The outdoor thermal comfort (OTC) of children is more specific than that of adults, and the complex influence of outdoor activity spaces on children's thermal comfort warrants further investigation. To investigate the outdoor thermal comfort baseline (OTCB) of children in Hangzhou and explore the thermal impact of outdoor surfaces on children, a survey was conducted in six typical outdoor activity spaces in Hangzhou, China, during spring and summer utilizing physical measurements, questionnaire surveys, and the universal thermal climate index (UTCI). This study analyzed the differences in thermal perception among children in Hangzhou in different seasons, their OTCB, and the impact of surface reflectance (Rs) on children's OTC. The results indicated the following: 1) In spring, children in Hangzhou generally felt comfortable, but their discomfort with heat noticeably increased in summer. 2) The neutral UTCIs (NUTCIs) for Hangzhou children were 11.6°C (spring) and 27.7°C (summer), and the NUTCI ranges (NUTCIRs) were 9.7-17.5°C (spring) and 25.7-30.0°C (summer); additionally, the thermal acceptability ranges (TARs) were 13.2-25.2°C (spring) and 11.8-34.8°C (summer). 3) A high Rs made children feel more uncomfortable with heat, which was primarily due to the space's total shortwave and longwave radiation, which peaked between 14:00 and 15:00. 4) Based on the research findings, corresponding bioclimatic design strategies were proposed. Recommendations include using high Rs underlays with shading, composite underlays, or the future adoption of thermochromic coatings. Keeping permeable underlays moist is essential for activating their cooling mechanisms. Fundamental safety measures are imperative. This study provides valuable data for urban planners and landscape designers to create public spaces suitable for children's outdoor activities, contributing to a harmonious and unified living environment.