Surface Air Temperature Research Articles

NOAAGlobalTemp version 6: An AI-based global surface temperature dataset

Abstract The National Oceanic and Atmospheric Administration (NOAA) Global Surface Temperature (NOAAGlobalTemp) dataset is widely used for scientific research, operational monitoring, and climate assessment activities. Aligning with NOAA’s mission values, NOAAGlobalTemp has been updated to version 6 (i.e., NGTv6), which includes two enhancements over its predecessor (NGTv5). The first enhancement is the expansion of the spatial coverage to encompass the entire globe and the extension of temporal coverage back to 1850 (an interim version of NOAAGlobalTemp with these features was released in February 2023). The expansion of spatial coverage is accomplished by utilizing surface air temperatures over the Arctic Ocean and by eliminating the data reconstruction mask used in NGTv5 that had suppressed interpolation in data-sparse regions. This change has important implications for global temperature trends since the Arctic region has been warming at a much faster pace, more than four times the global average, in the twenty-first century to date. The second enhancement is the implementation of a methodology based on artificial intelligence (AI) for reconstructing surface air temperature over the global land surface and the Arctic Ocean. The AI model employs an artificial neural network to fill data gaps and is demonstrated to be more robust, stable, and accurate than the previous gap-filling method, particularly in observation-sparse areas such as the polar regions. The model outperforms the previous approach across all evaluated statistical metrics, and the output reaches a stable state more quickly as observations are received, which facilitates climate monitoring.

Thermal regime of peatlands in Western Siberia near the southern border of the permafrost

Relevance. The permafrost degradation under the effect of global warming. It determines the necessity for a predictive assessment of permafrost stability to minimize disturbances to engineering installations in the permafrost zone. Aim. To assess the stability of frozen peatlands in the southern part of the zone of insular distribution of permafrost. Objects. Soils of flat-mound and high-mound palsa mires, which preserve permafrost on the southern border of the insular permafrost zone. Methods. Measurements of the temperature of peat and mineral soil in geocryological boreholes from 0 to 10 m deep using the SAM-N automatic monitoring surveillance network; determination of surface temperature from thermal channels of MODIS satellite images from 2000 to 2022; analysis of meteorological indices and determination of air temperature trends for predictive assessment of permafrost stability; calculation of indicator values of permafrost state, such as freezing degree-days and thawing degree-days, frost index, freezing and thawing N-factors. Results. Numerous features of unstable permafrost have been observed. In particular, the mean annual temperature of the surface layer was positive in all studied boreholes (+0.8...+1.3°C), and the temperature at the depth of zero amplitudes (10 m) is close to the melting point (with the predominance of temperatures in the range of –0.2...–0.3°C). Besides, a layer of soil that does not freeze throughout the year (non-merging permafrost) was identified. The thawing degree-days and frost number values correspond to areas with unfrozen soils. Positive trends in air temperature, Earth's surface temperature, and snow depth were observed. For 2000–2022, surface air temperature increased by 0.76°C/10 years on average. The land surface temperature increased in summer by an average of 0.42°C/10 years. If the positive trend in surface air temperatures continues, frozen peatlands in the southern part of the permafrost zone will completely melt in 50–70 years.

The study of the city of Rudny as a “heat island”

The annual increase in surface air temperatures contributes to climate change. This phenomenon is widespread over the entire surface of the Earth and is typical for any point of the planet. The purpose of this study was to identify patterns of changes in meteorological indicators in the city of Rudny, Kostanay region, Republic of Kazakhstan over the summer period 2018–2022. The scientific novelty of the work lies in the fact that for the first time the spatial distribution of the heat index in the territory of the city of Rudny was determined. To conduct the study, data from local automated weather stations were used, meteorological data was analyzed, compared for each year of the study, and the heat index was calculated. The reliability of the results is ensured by mathematical processing of daily empirical temperature and humidity data (calculation of the average for the day, the average for the month), coefficient of variation, coefficient of dispersion. The conducted research provides a clear description of climate changes in the city of Rudny, which can be used by municipal authorities to form measures to reduce the thermal impact on public health, heat loss in the housing and communal services system, and to improve air quality when planning urban infrastructure in the territory of the studied city.

Variation in the Quanta-to-Energy Ratio of Photosynthetically Active Radiation under the Cloudless Atmosphere

The quanta-to-energy ratio plays a crucial role in converting energy units to quantum units in the context of photosynthetically active radiation (PAR). Despite its widespread use, the effects of atmospheric particles and solar zenith angle (SZA) on the quanta-to-energy ratio remain unclear. In this study, both simulation and observation data revealed that the principal wavelength, which can be transformed into the quanta-to-energy ratio using a constant, exhibits a slow initial growth, followed by a rapid increase beyond 60° solar zenith angles and a subsequent dramatic decrease after reaching its maximum value. The measured quanta-to-energy ratio demonstrates a variable range of less than 3% for SZA under 70° in a cloudless atmosphere, with significant changes only occurring at zenith angles above 80°. Simulation data indicate that ozone, wind speed, surface-level pressure, surface air temperature, and relative humidity have negligible effects on the quanta-to-energy ratio. The Ångstrom exponent exerts a minor influence on the quanta-to-energy ratio by affecting diffuse radiation. Visibility, however, is found to have a substantial impact on the quanta-to-energy ratio. As a result, two relationships are established, linking the principal wavelength to visibility and the diffuse fraction of PAR. The principal wavelength serves as an effective measure of solar spectrum variability, remaining unaffected by radiation energy. This implies that atmospheric parameters which do not alter the solar spectrum will not influence the principal wavelength. The strong correlations between the principal wavelength, visibility, and the diffuse fraction of PAR suggest a broader range of applications for the principal wavelength in various research domains, opening up new avenues for exploration and potential contributions to numerous fields.

Decadal Relationship Between Arctic SAT and AMOC Changes Modulated by the North Pacific Oscillation

AbstractThe faster warming for Arctic Ocean surface air temperature (SAT) relative to that at lower latitude is connected with various processes, including local radiation feedback, poleward oceanic and atmospheric heat transport. It is unclear how combinations of different low‐frequency internal climate modes influence Arctic amplification on the decadal timescale. Here, the decadal Arctic SAT variation, its connection with the Atlantic meridional overturning circulation (AMOC) and possible underlying mechanisms, are investigated based on several independent observational proxies, pre‐industrial experiments, and historical large ensembles of two CMIP6 models. Our study suggests that AMOC and Arctic SAT vary in phase on the decadal timescale, whereas this relationship is insignificant at the interannual timescale. Further analysis shows that the AMOC accompanied with cross‐basin oceanic water/heat transport between Atlantic and Arctic would alter air–sea interface exchange over the melting ice regions, and then amplified poleward atmospheric heat and moisture transports. The resulting enhanced downward longwave radiation ultimately warms the Arctic SAT. Additionally, the decadal‐scale North Pacific Oscillation (NPO) can modulate the relationship between AMOC and Arctic SAT by influencing poleward moisture transport and cross‐basin circulation. Specifically, the phase shift of combined NPO and AMOC can contribute 14%–41% covariance relationship between AMOC and Arctic SAT. Our study provides potential sources for predicting the Arctic climate and constraining its uncertainty in future projections.

A century-long China homogenized daily surface air temperature dataset (CUG-CMA CHDT)

Daily meteorological observation data of the early period (pre-1950) were critically important for investigating the long-term trends and multi-decadal scale variability of extreme climate events. The high-resolution surface air temperature (SAT) data for time period before 1950 are lacking in China. We extended the SAT observations of China back to 1840 through developing a pre-1950 daily SAT dataset. The early-period daily SAT were manually corrected for the input and clerical errors, and then according to the length or coverage of time, the main series for each of the cities was determined. The observation time system of unknown sites was determined by the minimum difference method. After these operations, the data of all sites were unified into the same format. By using the ridge regressions established based on data from modern reference stations, the missing maximum temperature (Tmax) and minimum temperature (Tmin) were interpolated. The early-period data were combined with modern data to form the long-term daily SAT dataset of 1840–2020 in China. RHtest software was used to detect and adjust the inhomogeneities in the station data series. Finally, the century-long homogenized daily SAT dataset including 45 key city stations in China was obtained. Among the stations, there are 20 stations with observation record more than one hundred years. The length of temperature observation series of 17 stations is between 80 and 100 years. The series length of the remaining 7 stations is between 68 and 80 years. Finally, the angular distance weighting (ADW) method was used to interpolate the data into grid products, and the grid size is 2.5 ° × 2.5 °. The dataset was named CUG-CMA CHDT, which is applicable in monitoring, studies and assessments of regional extreme temperature change and variability in China.

The combined link of the Indian Ocean dipole and ENSO with the North Atlantic-European circulation during early boreal winter in reanalysis and the ECMWF-SEAS5 hindcast

Abstract During early boreal winter, the extra-tropical atmospheric circulation is influenced by Rossby waves propagating from the Indian Ocean towards the North Atlantic-European (NAE) regions, resulting in a North Atlantic Oscillation (NAO)-like pattern. The mechanisms driving these teleconnections are not well understood and are crucial for improving model skills. This study investigates these mechanisms using the ERA5 dataset and tests the predictive capabilities of the ECMWF-SEAS5 hindcast, exploring potential reasons for a weak model response. Linear regression methods are employed to examine the extra-tropical links with the Indian Ocean dipole (IOD), both in isolation and in combination with the El Niño-Southern Oscillation (ENSO). Our findings demonstrate a connection between October IOD sea surface temperature anomalies and December Indian Ocean precipitation patterns. Furthermore, a correlation between the October IOD and December NAO time series suggests a link between the IOD and NAE circulation. The early winter European response to a positive IOD is characterized by a north-south precipitation dipole and a large positive surface air temperature anomaly. Positive feedback from transient eddy forcing reinforces the wavenumber-3-like propagation across extra-tropical regions, with ENSO playing a minor role compared to the IOD. This phenomenon is particularly evident in regions such as the North Pacific and North Atlantic, where wave energy propagation is intensified. Although SEAS5 replicates the NAO response, its magnitude is significantly weaker. The model struggles to simulate the delayed rainfall dipole response to the IOD accurately and shows structural discrepancies compared to reanalysis data.

Frontal effects on the rapid formation of a deep layer of marine fog and cloud in the NW Atlantic

AbstractRapid formation of a deep layer of fog and clouds extending to a height of 2 km was observed over Sable Island off Nova Scotia on 13–14 July 2019. This fog and cloud event was dominantly caused by the rapid intrusion of a trough from a cyclone over NE Canada. The trough quickly moved from SW–W to E–NE and encroached on the coastal waters of Nova Scotia. Due to the warm front in the trough, the air temperature increased up to 4 km in height with increased stability, while the sea‐level pressure at Sable Island dropped by 11 hPa within 12 hours. The moderate to strong winds turned counterclockwise from the N to the SE, converging with the SE winds maintained by the ridge located to the east. There was no indication of an advection‐type fog mechanism because the surface air temperature was not adjusted to the colder sea‐surface temperature that could have resulted in saturation of the surface air. The leading portion of a massive cloud band in the trough passing over Sable Island included precipitation before fog formation. The rain that evaporated in the unsaturated and warm subcloud layer effectively reduced the air temperature and significantly increased the wet‐bulb temperature in this layer. The cloud extended into the moist subcloud layer, while fog formed at 1400 UTC 13 July and lasted until 2327 UTC 13 July (with two brief periods of mist lasting less than two hours). The development of this deep layer of fog and cloud in the NW Atlantic was caused by evolving synoptic conditions through the simultaneous effects of a rapid trough intrusion over coastal waters with a massive cloud band, induced precipitation causing cloud deepening into the subcloud layer, and surface wind convergence forcing vertical mixing in a stable marine boundary layer.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Surface warming in Svalbard may have led to increases in highly active ice-nucleating particles

The roles of Arctic aerosols as ice-nucleating particles remain poorly understood, even though their effects on cloud microphysics are crucial for assessing the climate sensitivity of Arctic mixed-phase clouds and predicting their response to Arctic warming. Here we present a full-year record of ice-nucleating particle concentrations over Svalbard, where surface warming has been anomalously faster than the Arctic average. While the variation of ice-nucleating particles active at around −30 °C was relatively small, those active at higher temperatures (i.e., highly active ice-nucleating particles) tended to increase exponentially with rising surface air temperatures when the surface air temperatures rose above 0 °C and snow/ice-free barren and vegetated areas appeared in Svalbard. The aerosol population relevant to their increase was largely characterized by dust and biological organic materials that likely originated from local/regional terrestrial sources. Our results suggest that highly active ice-nucleating particles could be actively released from Arctic natural sources in response to surface warming.

Internal Variability Dominated the Extreme Cold Wave Over North America in December 2022

AbstractIn December 2022, North America experienced an unprecedented extreme cold event. However, the underlying physical mechanisms of this cold wave, and the extent to which it is driven by internal variability or external forcing, are not fully understood. Using ERA5 reanalysis data and the HadGEM3‐A‐N216 attribution simulations, we identified internal variability as the main cause, contributing −5.14 K to surface air temperature (SAT) anomalies in North America. External forcing slightly mitigated the cold by 0.42 K. An internally generated wave train from the North Pacific, influenced in combination by Pacific‐North American (PNA) and North Pacific Oscillation (NPO) teleconnection patterns, initiated this intense cyclonic event, contributing −2.18 K and −2.12 K to SAT anomalies, respectively. La Niña‐like sea surface temperature anomalies amplified this wave train and resultant cold wave. Additionally, excessive snow cover in the previous November also intensified the December cold anomalies by enhancing surface albedo and reducing solar radiation.

Skillful Long-Lead Seasonal Predictions in the Summertime Northern Hemisphere Midlatitudes

Abstract In contrast to boreal winter when extratropical seasonal predictions benefit greatly from ENSO-related teleconnections, our understanding of forecast skill and sources of predictability in summer is limited. Based on 40 years of hindcasts of the Canadian Seasonal to Interannual Prediction System, version 3 (CanSIPSv3), this study shows that predictions for the Northern Hemisphere summer surface air temperature are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. These midlatitude regions of statistically significant predictive skill appear to be connected to each other through an upper-tropospheric circumglobal wave train. Although a large part of the forecast skill for the surface air temperature and 500-hPa geopotential height is attributable to the linear trend associated with global warming, there is significant long-lead seasonal forecast skill related to interannual variability. Two additional idealized hindcast experiments are performed to help shed light on sources of the long-lead forecast skill using one of the CanSIPSv3 models and its uncoupled version. It is found that tropical ENSO-related sea surface temperature (SST) anomalies contribute to the forecast skill in the western United States, while land surface conditions in winter, including snow cover and soil moisture, in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill in these regions. This implies that improving land surface initial conditions and model representation of land surface processes is crucial for the further development of a seasonal forecasting system. Significance Statement Useful seasonal predictions in the boreal summer midlatitude regions are of great value. In this study, we show that predictions for the boreal summer season are skillful more than 6 months in advance in several midlatitude regions, including eastern Europe–Middle East, central Siberia–Mongolia–North China, and the western United States. The forecast skill in these regions is associated with a circumglobal teleconnection atmospheric circulation pattern. Sources of the long-lead forecast skill include the global warming–related trend and anomalies in the ocean and land surface initial conditions. It is found that the wintertime snow cover and soil moisture in the Siberian and western U.S. regions have a delayed or long-lasting impact on the atmosphere, which leads to summer forecast skill.

Contribution of land-atmosphere coupling in 2022 CONUS compound drought-heatwave events and implications for forecasting

Severe compound drought-heatwave events were observed over three regions of the Contiguous United States (CONUS), Northwest (NW), Great Plains (GP), and Northeast (NE) regions, during July and August 2022. In this study, we have found that the developments of these drought-heatwave events were shaped by different land-atmosphere coupling behaviors which are associated with water and energy limitation regimes in these regions. In the NW and GP regions, the surface soil moisture (SM) and evapotranspiration (ET) were coupled through water-limited processes. Heatwaves in these two regions were affected by the decrease of ET and the available SM due to the precipitation deficit. This type of land-atmosphere coupling was especially prominent in the GP. In the NE region, the heatwave governed ET through the increase of potential ET (PET) based on energy-limited coupling, which played a crucial role in the development of drought.The impacts of the different land-atmosphere coupling behaviors on the predictability of the 13-km Geophysical Fluid Dynamics Laboratory (GFDL) System for High-resolution prediction on Earth-to-Local Domains (SHiELD) were also investigated by checking its 10-day forecasts during the same period. The analysis was particularly focused on the GP and NE regions, where different land-atmosphere coupling behaviors were observed. The model's warm bias in the GP region was associated with the overestimated net radiation, and the bias was further amplified through the water-limited coupling. In the NE region, the PET-related variables, including surface air temperature, influenced the predictability of drought onset by limiting ET through the energy-limited coupling. Based on our findings, this study highlights the crucial role of land-atmosphere coupling behaviors and provides a scientific strategy for enhancing the model predictability of compound drought-heatwaves.

Characterizing the local and global climatic factors associated with vegetation dynamics in the karst region of southwest China

Understanding the relationship between vegetation and climatic drivers is essential for assessing terrestrial ecosystem patterns and managing future vegetation dynamics. This study examines the effects of local climatic factors and remote large-scale ocean–atmosphere circulations from the Pacific, Atlantic, and Arctic Oceans, as well as the East Asian and Indian summer monsoons, on the spatiotemporal variability of the Normalized Difference Vegetation Index (NDVI) in the karst region of southwest China (KRSC) using Mann-Kendall test, Sen’s slope, cross-correlation, and wavelet analysis. We observed a significant increase in NDVI over karst and non-karst regions from 1981 to 2019, with a notable abrupt shift from 2001 onwards, underscoring the importance of understanding the underlying drivers. The significant correlation and coherence of surface air (TMP) and soil temperatures (ST) with NDVI, especially when analyzed using wavelet methods, indicate their crucial role in vegetation dynamics. Additionally, the broad coherence patterns of AMO and WHWP with NDVI at annual and decadal cycles suggest that ocean–atmosphere interactions also play a significant part. At interannual periodicities, most large-scale indices displayed significant coherence with NDVI. These findings highlight the complexity of NDVI variability, which is better explained by the integration of multiple local and global factors rather than by single variables. The integrated local–global drivers, particularly TMP-ST-AMO-NP-WHWP and PCP-SM-AMO-NP-WHWP with mean coherence of 0.90 and 0.89, respectively, showed the highest mean coherence, emphasizing the need for a multifaceted approach in understanding vegetation changes rather than a single local variable or atmospheric circulation index. These findings have significant implications for policymakers, aiding in better planning and policy formulations considering climate change and atmospheric variability.

Microclimate analysis of Ganga riverfront in Patna: spatial form perspective

ABSTRACT The investigation in this research scrutinizes the influence of spatial form factors on thermal comfort along the Ganga riverfront trail during the spring season. Understanding how spatial form factors impact local climate parameters and PET values is crucial for optimizing thermal comfort in urban waterfronts. The research addresses the need to improve thermal comfort in rapidly urbanizing areas through strategic urban design. Data were collected from eight measurement points along the riverfront, analyzing Air Temperature (Ta), Relative Humidity (RH), Wind Velocity (v), Surface Temperature (Ts), Global Radiation (G) and Mean Radiant Temperature (Tmrt). The study employs a combination of field measurements and statistical analysis to assess the correlation between spatial form factors and thermal comfort indicators. Findings reveal that lower building height (Hb) values are associated with more favorable thermal comfort conditions, while higher Hb negatively impacts thermal comfort. The study also highlights that building spacing (ADb) influences thermal comfort, with findings contrasting those from composite climates, suggesting a need for context-specific design strategies. The research underscores the significant role of Ta and v in shaping Physiological Equivalent Temperature (PET), aligning with previous studies in diverse urban contexts. The results emphasize the critical impact of spatial form on microclimate and thermal comfort, informing urban design and planning to enhance livability along the Ganga riverfront. This study contributes to the field by providing empirical evidence and practical insights for developing thermally comfortable walking trails.

Biennial variability of boreal spring surface air temperature over India

In this study, we report significant biennial variability or oscillation (BO) in the boreal spring (March–May) Surface Air Temperature (SAT) over India and unravelled the causative mechanisms. The positive phase of the BO exhibit significant seasonal warming over India, whereas seasonal cooling is observed during the negative phase of BO. Heat wave days are more during the positive phase of BO compared to negative or neutral phase. The positive (negative) phase of BO is generally coherent with the central (eastern) Pacific warming (cooling) years. The anomalous low-level divergence associated with low-level anticyclonic circulation induces less cloudiness and intense surface solar radiation ovr India during the positive phase, favouring surface warming. The evolution of some positive and/or negative phases of BO without any large scale forcing from the equatorial Pacific suggested the possibility of alternate pathways. The strong anomalous upper-level (at 200 hPa) anticyclonic circulation provoked by mid-latitude Rossby waves is found contributing to the positive phase, thereby highlighting the role of dominant mid-latitude pathways in the biennial SAT variability in addition to El Niño forcing. The sinking motion associated with persistent high, and the associated adiabatic compression also supported surface heating during the positive phase of BO. On the other hand, the mid-latitude Rossby wave induced upper-level cyclonic circulation is found contributing to the negative phase. The sinking motion associated with persistent high, and the associated adiabatic compression also supported surface heating during the positive phase of BO. In contrast, negative soil temperature anomalies and high latent heat flux release to the atmosphere supported surface cooling during the negative phase.

Sampling Error of Mean and Trend of Nighttime Air Temperature at Weather Stations Over China Using Satellite Data as Proxy

AbstractMeteorological observations of surface air temperature have provided fundamental data for climate change detection and attribution. However, the weather stations are unevenly distributed, and are still very sparse in remote regions. The possible sampling error is well known, but not well quantified because we are lack of the adequate and regularly distributed measurements. The high resolution of satellite land surface temperature retrieval during night time provide a nice proxy for near surface temperature as both temperatures controlled by surface longwave radiative cooling and the nocturnal temperature inversion depress land‐atmosphere turbulent exchange. The sampling error of mean value and trend were assessed by comparing station point measurements (pixel of ∼0.01°) with grid (1°) mean and national mean from 2001 to 2021. This method permits us to make the first assessment of under‐sampling error and spatial representative error on both national mean and trend of air temperature during nighttime collected at ∼2,400 weather stations over China. The sampling error in national mean temperature is more than 3°C. The under‐sampling error due to lack of observation explains two thirds and the spatial representative error due to the difference between station and grid/regional mean elevation contribute the other one third. The sampling error in trend account for one third of the national mean trend. The urban heat island effect associated with urbanization around the weather stations (spatial representative error) can explain four fifths of the sampling error in trend, which is consistent with existing studies based on air temperature collected at paired weather station.

Is home where the heat is? comparing residence-based with mobility-based measures of heat exposure in San Diego, California.

Heat can vary spatially within an urban area. Individual-level heat exposure may thus depend on an individual's day-to-day travel patterns (also called mobility patterns or activity space), yet heat exposure is commonly measured based on place of residence. In this study, we compared measures assessing exposure to two heat indicators using place of residence with those defined considering participants' day-to-day mobility patterns. Participants (n = 599; aged 35-80 years old [mean =59 years]) from San Diego County, California wore a GPS device to measure their day-to-day travel over 14-day intervals between 2014-10-17 and 2017-10-06. We measured exposure to two heat indicators (land-surface temperature [LST] and air temperature) using an approach considering their mobility patterns and an approach considering only their place of residence. We compared participant mean and maximum exposure values from each method for each indicator. The overall mobility-based mean LST exposure (34.7 °C) was almost equivalent to the corresponding residence-based mean (34.8 °C; mean difference in means = -0.09 °C). Similarly, the mean difference between the overall mobility-based mean air temperature exposure (19.2 °C) and the corresponding residence-based mean (19.2 °C) was negligible (-0.02 °C). Meaningful differences emerged, however, when comparing maximums, particularly for LST. The mean mobility-based maximum LST was 40.3 °C compared with a mean residence-based maximum of 35.8 °C, a difference of 4.51 °C. The difference in maximums was considerably smaller for air temperature (mean = 0.40 °C; SD = 1.41 °C) but nevertheless greater than the corresponding difference in means. As the climate warms, assessment of heat exposure both at and away from home is important for understanding its health impacts. We compared two approaches to estimate exposure to two heat measures (land surface temperature and air temperature). The first approach only considered exposure at home, and the second considered day-to-day travel. Considering the average exposure estimated by each approach, the results were almost identical. Considering the maximum exposure experienced (specific definition in text), the differences between the two approaches were more considerable, especially for land surface temperature.

The cooperative effects of November Arctic sea ice and Eurasian snow cover on the Eurasian surface air temperature in January–February

The cooperative effects of November Arctic sea ice and Eurasian snow cover on the Eurasian surface air temperature in January–February

Global assessment of climatic responses to ozone–vegetation interactions

Abstract. The coupling between surface ozone (O3) and vegetation significantly influences the regional to global climate. O3 uptake by plant stomata inhibits the photosynthetic rate and stomatal conductance, impacting evapotranspiration through land surface ecosystems. Using a climate–vegetation–chemistry coupled model (the NASA GISS ModelE2 coupled with the Yale Interactive terrestrial Biosphere, or ModelE2-YIBs), we assess the global climatic responses to O3–vegetation interactions during the boreal summer of the present day (2005–2014). High O3 pollution reduces stomatal conductance, resulting in warmer and drier conditions worldwide. The most significant responses are found in the eastern US and eastern China, where the surface air temperature increases by +0.33 ± 0.87 and +0.56 ± 0.38 °C, respectively. These temperature increases are accompanied by decreased latent heat and increased sensible heat in both regions. The O3–vegetation interaction also affects atmospheric pollutants. The surface maximum daily 8 h average O3 concentrations increase by +1.46 ± 3.02 ppbv in eastern China and +1.15 ± 1.77 ppbv in the eastern US due to the O3-induced inhibition of stomatal uptake. With reduced atmospheric stability following a warmer climate, increased cloud cover but decreased relative humidity jointly reduce aerosol optical depth by −0.06 ± 0.01 (−14.67 ± 12.15 %) over eastern China. This study suggests that vegetation feedback should be considered for a more accurate assessment of climatic perturbations caused by tropospheric O3.