Land Surface Air Temperature Research Articles

Impact of interannual and multidecadal trends on methane-climate feedbacks and sensitivity

We estimate the causal contributions of spatiotemporal changes in temperature (T) and precipitation (Pr) to changes in Earth’s atmospheric methane concentration (CCH4) and its isotope ratio δ13CH4 over the last four decades. We identify oscillations between positive and negative feedbacks, showing that both contribute to increasing CCH4. Interannually, increased emissions via positive feedbacks (e.g. wetland emissions and wildfires) with higher land surface air temperature (LSAT) are often followed by increasing CCH4 due to weakened methane sink via atmospheric •OH, via negative feedbacks with lowered sea surface temperatures (SST), especially in the tropics. Over decadal time scales, we find alternating rate-limiting factors for methane oxidation: when CCH4 is limiting, positive methane-climate feedback via direct oceanic emissions dominates; when •OH is limiting, negative feedback is favoured. Incorporating the interannually increasing CCH4 via negative feedbacks gives historical methane-climate feedback sensitivity ≈ 0.08 W m−2 °C−1, much higher than the IPCC AR6 estimate.

Relationships Between Rapid Urbanization and Extreme Summer Precipitation Over the Sichuan–Chongqing Area of China

China has undergone rapid urbanization over the past few decades, and accordingly, changes have occurred in the extreme precipitation events. However, few studies have focused on the relationships between rapid urbanization and extreme precipitation events in southwest China, particularly in the Sichuan–Chongqing area, which has a complex topography and has experienced rapid urbanization over the past few decades. This is the first study to analyze the impact of urbanization on the amount, frequency, and intensity of extreme summer (June–August) precipitation events over the past 30 years. Our results indicate that extreme precipitation events primarily occurred in the urban-dominated Sichuan basin, particularly during the fast urbanization development stage (FUDS) of 1994–2015. Extreme precipitation amounts and intensities increased during the FUDS, implying the greater probability of individual precipitation events developing into heavy or extreme events in a particular area. In addition, the probability distribution functions of the occurrence and volume of strong convective events significantly increased during the FUDS. Finally, the annual increase in urban-scale land surface air temperature, increase in wet convection, and changes in wind speed are identified as essential factors leading to extreme precipitation events in this region.

CMIP6 model simulation of concurrent continental warming holes in Eurasia and North America since 1990 and their relation to the Indo-Pacific SST warming

Observations during 1990–2014 depict concurrent cooling trends in land surface air temperature (LSAT) over central-eastern Eurasia (CEE) and northwestern North America (NWNA), while significant warming trends are observed in other areas across the Northern Hemisphere (NH). This lack of surface warming of the continental CEE and NWNA is termed continental warming holes (CWH) here. The ability of the 6th phase of the Coupled Model Intercomparison Project (CMIP6) to reproduce the LSAT trends during 1990–2014 over the continental NH is evaluated, and the evaluation includes all CMIP6 historical simulations from 32 unique models and their multi-model ensemble mean (MMEM). The results show that the performances of the different models vary greatly. GISS-E2–1-G-CC model performs best among the 32 models, considering reproducing the correct sign of the LSAT trends as well as reproducing the trend magnitude. However, the MMEM simulates uniformly significant warming trends in NH and fails to capture the continental warming holes, indicating a role of internal variability in the CWH. The mechanisms of the continental warming holes are further investigated using reanalysis datasets, CMIP6 models and AGCM simulations. GISS-E2–1-G-CC also shows a better simulation ability, including the trends in NH sea level pressure and tropical sea surface temperature than other models. Both reanalysis data and GISS-E2–1-G-CC model show high-pressure trends over mid-high latitudes, which corresponded to enhanced Siberia High and weakened Aleutian Low. These atmospheric circulation changes result in cold advection over the CEE and NWNA regions, offsetting anthropogenic warming and leading to the emergence of the warming holes. Further analysis and AGCM simulations link these atmospheric circulation trends to Indo-Pacific SST warming. In response to the Indo-Pacific SST warming, tropical tropospheric warming leads to the equatorward shift of the mid-latitude jet stream, and subsequently, anomalous easterly and high pressure over mid-high latitudes tend to influence the Siberia High and Aleutian low, resulting in the LSAT cooling over the CEE and NWNA.

The Equatorial Pacific Cold Tongue Bias in CESM1 and Its Influence on ENSO Forecasts

Abstract The mean-state bias and the associated forecast errors of the El Niño–Southern Oscillation (ENSO) are investigated in a suite of 2-yr-lead retrospective forecasts conducted with the Community Earth System Model, version 1, for 1954–2015. The equatorial Pacific cold tongue in the forecasts is too strong and extends excessively westward due to a combination of the model’s inherent climatological bias, initialization imbalance, and errors in initial ocean data. The forecasts show a stronger cold tongue bias in the first year than that inherent to the model due to the imbalance between initial subsurface oceanic states and model dynamics. The cold tongue bias affects not only the pattern and amplitude but also the duration of ENSO in the forecasts by altering ocean–atmosphere feedbacks. The predicted sea surface temperature anomalies related to ENSO extend to the far western equatorial Pacific during boreal summer when the cold tongue bias is strong, and the predicted ENSO anomalies are too weak in the central-eastern equatorial Pacific. The forecast errors of pattern and amplitude subsequently lead to errors in ENSO phase transition by affecting the amplitude of the negative thermocline feedback in the equatorial Pacific and tropical interbasin adjustments during the mature phase of ENSO. These ENSO forecast errors further degrade the predictions of wintertime atmospheric teleconnections, land surface air temperature, and rainfall anomalies over the Northern Hemisphere. These mean-state and ENSO forecast biases are more pronounced in forecasts initialized in boreal spring–summer than other seasons due to the seasonal intensification of the Bjerknes feedback.

EFFECTS OF URBAN GREENERY ON HEALTH. A STUDY FROM REMOTE SENSING

Abstract. Global warming is causing increasing Heat Waves that affect human health. High temperatures markedly increase morbidity and mortality. Urban Heat Islands increase the effects of Heat Waves and are a serious inconvenience to human health and comfort. Cities can substantially increase local temperatures and reduce temperature drop at night. During the night, the greater thermal inertia of the central areas reduces their cooling capacity. On the other hand, it is important to highlight that urban vegetation plays a key role in adapting cities to Global Warming and Urban Heat Island. Green areas have lower temperatures than the rest of land uses and generate a cooling effect that spreads to their surroundings creating a "cool island" effect. The main objective of this paper is to establish the nocturnal land surface temperature and land surface air temperature of Barcelona Metropolitan Area (35 municipalities, 636 km2, 3.3 million inhabitants) in an episode of a nocturnal heatwave and to estimate its possible impact on health and mortality. Subsequently, nighttime temperatures are analysed in this extreme heat context to determine their spatial distribution and detect the urban landscapes that are most vulnerable to extreme night heat. Modelling of land surface temperature must reveal the elements that determine night Urban Heat Island and consequently identify actions that can be implemented at urban planning level to refresh the environment during the night and thus increase the resilience of the most vulnerable landscapes and improve residents’ health. This paper studies the effect of urban greenery and green infrastructures on Nighttime Urban Heat Island and propose climate adaptation measures and design for urban green areas to decrease high temperature in a Heat Wave context, which contributes to reducing the serious negative impacts on people's health.

Simplified estimation modeling of land surface solar irradiation: A comparative study in Australia and China

Solar irradiation maps are fundamental geospatial datasets that have been used in a variety of research fields. However, it is difficult to estimate the continuous distribution of solar irradiation over large areas accurately by using conventional interpolation or extrapolation methods based on only a few observation stations. To tackle this problem, this study proposed a method to estimate spatially continuous land surface solar irradiation based on four machine learning models, namely, Gradient Boosting Machine (GBM), Random Forest (RF), Support Vector Regression (SVR), and Multilayer Perceptron (MLP). Clear-sky solar irradiation data were computed based on time and location, cloud optical thickness (COT) and aerosol optical thickness (AOT) that significantly influence solar irradiation were retrieved from Himawari-8 meteorological satellite images, and land surface solar irradiation data were obtained from observation stations for training and evaluation. To explore the weather effects on land surface solar irradiation, air temperatures, humidity, wind, and atmospheric pressure were also quantified and integrated into the models. As a comparative study, this study collected six-year historical data and estimated solar distribution at a 5-km spatial resolution in Australia and China. Based on the coefficient of determination (R2), normalized Root Mean Square Error (nRMSE), normalized mean bias error (nMBE), and consumption of time (t), the results show that GBM achieved the highest accuracy with R2>0.7 at all stations, followed by RF, SVR, and MLP. It suggests that the proposed method can provide an accurate and reliable estimation of land surface solar irradiation, compared with the theoretical solar irradiation without the obstacle of the atmosphere. The annual solar distribution maps created by the built methods indicate that the proposed method is simple and effective for large geographical regions and can be used worldwide when similar datasets are obtained.

The global spatiotemporal heterogeneity of land surface-air temperature difference and its influencing factors

The water and energy in the land surface and lower atmosphere have a strong coupling relationship. Apart from the land surface temperature (Ts) and air temperature (Ta), the land surface-air temperature difference (Ts-Ta) is also an essential parameter reflecting the coupling process. However, the global spatiotemporal variations and influencing factors of Ts-Ta remain not well explored. Here, ERA5-land reanalysis data, GIMMS NDVI data, and elevation data were used to analyze the global spatiotemporal heterogeneity and influencing factors of Ts-Ta. It was found that annual mean Ts-Ta exhibited a decreasing trend from the equator to polar areas. And the annual Ts-Ta increased at 0.009 °C/10a from 1981 to 2020. The variations of global net radiation mainly determined the spatiotemporal heterogeneity of global Ts-Ta. The different properties of the land surface and near-surface atmosphere were the main factors affecting the Ts-Ta, including soil moisture, vegetation, snow cover, and the water vapor content in the atmosphere. In addition, Ts and Ta also affected each other. These findings are conducive to a better understanding of the land-atmosphere coupling, and it is of great significance to take better measures to adapt the global climate change.

Future evolution of global land surface air temperature trend based on Coupled Model Intercomparison Project Phase 6 models

AbstractIn recent decades, global warming has been an indisputable fact. The increase in extreme events accompanied by the rise in temperature has huge influences on many aspects of the society and economy globally. As a result, forecasting how global temperatures will change and evolve in the future is crucial, but it is restricted by the accuracy and uncertainty of model simulations. Based on ensemble empirical mode decomposition (EEMD) method, this study first evaluates the performance of the CMIP6 models in simulating different timescale components including high‐frequency components (HFC), low‐frequency components (LFC), and secular trend (ST) of surface air temperature (SAT). The results show that the performance of the CMIP6 models in simulating the ST is better. Following the observation constraint method's correction of CMIP6 model simulations, we investigate the evolution characteristics of future temperature secular trends under different Shared Socioeconomic Pathway (SSP) scenarios. The results show that under SSP245 scenario, the rise of SAT first appeared in northwest and east North America and eastern Europe. The spatial distribution of evolution characteristic under the SSP585 scenario is similar to that of the SSP245 scenario but with a larger magnitude. However, more pronounced differences appear in warming rate, which shows a downward trend over time under the SSP245 scenario and an upward trend with time under the SSP585 scenario.

Description of the China global Merged Surface Temperature version 2.0

Abstract. Global surface temperature observational datasets are the basis of global warming studies. In the context of increasing global warming and frequent extreme events, it is essential to improve the coverage and reduce the uncertainty in global surface temperature datasets. The China global Merged Surface Temperature Interim version (CMST-Interim) is updated to CMST 2.0 in this study. The previous CMST datasets were created by merging the China global Land Surface Air Temperature (C-LSAT) with sea surface temperature (SST) data from the Extended Reconstructed Sea Surface Temperature version 5 (ERSSTv5). The CMST 2.0 contains three variants: CMST 2.0 − Nrec (without reconstruction), CMST 2.0 − Imax, and CMST 2.0 − Imin (according to their reconstruction area of the air temperature over the sea ice surface in the Arctic region). The reconstructed datasets significantly improve data coverage, whereas CMST 2.0 − Imax and CMST 2.0 − Imin have improved coverage in the Northern Hemisphere, up to more than 95 %, and thus increased the long-term trends at global, hemispheric, and regional scales from 1850 to 2020. Compared to CMST-Interim, CMST 2.0 − Imax and CMST 2.0 − Imin show a high spatial coverage extended to the high latitudes and are more consistent with a reference of multi-dataset averages in the polar regions. The CMST 2.0 datasets presented here are publicly available at the website of figshare, https://doi.org/10.6084/m9.figshare.16929427.v4 (Sun and Li, 2021a), and the CLSAT2.0 datasets can be downloaded at https://doi.org/10.6084/m9.figshare.16968334.v4 (Sun and Li, 2021b). Both are also available at http://www.gwpu.net (last access: January 2022).

Combining Dynamical and Statistical Modeling to Improve the Prediction of Surface Air Temperatures 2 Months in Advance: A Hybrid Approach

A new type of hybrid prediction system (HPS) of the land surface air temperature (SAT) is described and its skill evaluated for one particular application. This approach utilizes sea-surface temperatures (SST) forecast by a dynamical prediction system, SINTEX-F2, to provide predictors of the SAT to a statistical modeling system consisting of a set of nine different machine learning algorithms. The statistical component is aimed to restore teleconnections between SST and SAT, particularly in the mid-latitudes, which are generally not captured well in the dynamical prediction system. The HPS is used to predict the SAT in the central region of Japan around Tokyo (Kantō) as a case study. Results show that at 2-month lead the hybrid model outperforms both persistence and the SINTEX-F2 prediction of SAT. This is also true when prediction skill is assessed for each calendar month separately. Despite the model's strong performance, there are also some limitations. The limited sample size makes it more difficult to calibrate the statistical model and to reliably evaluate its skill.

An Evaluation of Convection‐Permitting Ensemble Simulations of Coastal Nocturnal Rainfall Over South China During the Early‐Summer Rainy Season

AbstractConvection‐permitting ensemble Weather Research and Forecasting simulations were successively conducted on each day during the early‐summer rainy season (April‒June) in 2018 over South China. These simulations were configured by eight sets of parameterization schemes associated with the planetary boundary layer and surface layer. The performance of the ensemble products in reproducing the nocturnal rainfall in the coastal region of South China was evaluated using observations from densely distributed rain gauges and wind profiling radars. The simulations at a lead time of 12 hr present a relatively better performance than those at 36 hr lead time, but still exhibit a low skill especially after midnight. The degradation of forecast skill after midnight is closely associated with the worse underestimation of the low‐level onshore flow and land surface air temperature. The large‐scale onshore flow at low levels from the South China Sea (SCS) interacted with land/mountain breezes, enhancing the lower‐troposphere moisture pooling and convergence for the rainfall production over the coastal mountains/cities. In the longer lead‐time simulations, the low‐level onshore flow presented a deceleration tendency and thus indirectly amplified the effects of land/mountain breezes in nighttime, leading to more serious underestimation in rainfall amount and larger southward displacement error than the shorter lead‐time simulation. The findings highlight the dependence of the nocturnal rainfall forecast on accurately predicting the upstream low‐level onshore flow over the SCS and the thermal conditions in the urban region surrounded by mountains.

Daytime and nighttime warming has no opposite effects on vegetation phenology and productivity in the northern hemisphere

Over the past 50 years, global land surface air temperature has been rising at a much higher rate at night than during the day. Understanding plant responses to the asymmetric daytime and nighttime warming in the context of climate change has been a hot topic in global change biology and global ecology. It has been debatable whether the asymmetric warming has opposite effects on vegetation activity (e.g., phenology, productivity). Here we settle the debate by scrutinizing the underpinnings of different statistical methods and revealing how the misuse or improper use of these methods could mischaracterize the effects of asymmetric warming with in situ and satellite observations. The use of the ordinary least square (OLS) methods including both daytime (Tmax) and nighttime (Tmin) temperature in the multiple regression models could overlook the multicollinearity problem and yield the misinterpretations that Tmax and Tmin had opposite effects on spring phenology, autumn phenology, gross primary production (GPP), and normalized difference vegetation index (NDVI). However, when the OLS methods were applied with Tmax and Tmin included in separate models or alternatively the ridge regression (RR) method with properly selected ridge parameter was used, the effects of Tmax and Tmin on vegetation activity were generally in the same direction. The use of the RR method with improperly selected ridge parameter could also mischaracterize the effects of asymmetric warming. Our findings show that daytime and nighttime warming has no opposite effects on vegetation phenology and productivity in the northern hemisphere, and properly dealing with the multicollinearity problem is critical for understanding the effects of asymmetric warming on vegetation activity.

A Deep Neural Network-Based Approach for Studying the Relationship Between Land Surface Temperature and Surface Air Temperature

A Deep Neural Network-Based Approach for Studying the Relationship Between Land Surface Temperature and Surface Air Temperature

Assessment and synergy analysis of outdoor thermal comfort and thermal infrared remote sensing for urban heat island studies

The aim of this research is to explore the potentialities and limits of the integration of remote sensing biophysical data (land surface temperature) in the outdoor thermal comfort studies. Accordingly, by examining correlations between land surface temperature and air temperature, and using respectively remote sensing satellite data MODIS and different weather stations archives alongside questionnaire surveys. Currently, the parameters of thermal comfort indices are usually calculated using the data from one, or few permanent or portable ground-based weather stations. Due to the lack of adequate distribution of weather stations, those calculations generally do not accurately represent the alteration of thermal comfort, through time and space. Nevertheless, it has been essentially proved that despite strong tendencies between in-situ measured parameters and remotely sensed ones, various elements need to be studied (e.g., location, land surface type, vegetation, and elevation). Finally, preliminary results confirm that the proposed linear approaches are providing considerable and promising performance suitable for future specific situations and studies purposes.

Historical global land surface air apparent temperature and its future changes based on CMIP6 projections

The warming magnitudes under different shared socioeconomic pathways (SSPs) and the spatial distribution of global land surface air apparent temperature (APT) since the early of the 20 century were systematically analyzed, based on the comparisons among in-situ observations, extended reanalysis, and the CMIP6 model output. The warming of APT by the mid and late 21st century was then projected, as well as under the 1.5 °C and 2.0 °C threshold for global warming. The study reveals: 1) the CMIP6 multi-model ensemble mean (MME) agrees well with the observations in terms of the climatological mean and temporal variations for the global land surface air temperature (SAT) and the calculated APT over the past 100 years. 2) Although the spatial gradient distribution of SAT and APT is quite similar under SSP2-4.5 and SSP5-8.5, the warming trend of global surface APT over land is significantly larger than that of SAT. Population living in low latitudes will be more vulnerable to the enhanced warming of APT. 3) Under the global warming thresholds of 1.5 °C and 2.0 °C, the global mean APT estimated under SSP2-4.5 and SSP5-8.5 is identical, which are 1.9 °C and 2.7 °C, respectively. The projected APT will increase by 3.9 °C under SSP2-4.5 and 6.7 °C under SSP5-8.5 at the end of the 21st century relative to the pre-industrial. This study highlights that the probability and intensity of extreme warm events for land SAT and APT around the globe under SSP5-8.5 will be remarkably higher than SSP2-4.5 in the 21st century, implying the urgent demand of regulating greenhouse gas emissions toward reducing thermal discomfort in the future.

Local Climate Zones, Land Surface Temperature and Air Temperature Interactions: Case Study of Hradec Králové, the Czech Republic

The current application of local climate zones (LCZs) often ends with (inter)zonal comparation of land surface temperature (LST) or air temperature (AT). LST evaluation employs an enhanced concept of LCZs together with cluster analysis for LCZs grouped based on LST. The paper attempts to combine them into a complex approach derived from the case study on a medium-sized Central European city (Hradec Králové, the Czech Republic). In particular, the paper addresses the following. (i) The relation of LST and AT, when the daily course of temperature profile ranging clear off the surface up to 2 m was fitted by a rational 2D function. The obtained equation enables derivation of the AT from LST and vice versa. (ii) The differences in thermal response of LCZs based on LST or AT, where the highest average LST and average maximum LST show LCZs 10, 2, 3 and 8, i.e., with a significant proportion of artificial surfaces. The cluster of LCZs with a significant representation of vegetation, LCZs 9, B, D, A and G, have significantly lower LST. (iii) The contribution of LCZs to understanding of LST/AT relation and whether their specific relation could be expected in particular LCZs, when subsequent interaction assessment of LST and AT revealed statistically their significant correlation in LCZs for certain cases.

The Analysis of Angin Puting Beliung Risk Rate by Utilization of Remote Sensing and Geographic Information Systems in Semarang

This study examines the risk rate of Angin Puting Beliung in Semarang of Central Java. The Angin Puting Beliung is a local designation for small-scale tornadoes that occur in Indonesia, originates from differences in pressure of a weather system, leads to strong winds. Between January 2014 and December 2018, the occurrence of Angin Puting Beliung in Semarang city of Central Java reached 91 times with a total financial loss of around IDR 852,500,000 (USD 60,000). High population densities and settlements without being followed by control of spatial use and land-use change make Semarang more at risk of being hit by a tornado. This study specifically aims to determine the level of physical, social, and economic vulnerability as well as to analyze the risk level of Angin Puting Beliung in Semarang city. The survey is used as the main method in this study. Samples were taken to represent the population namely land cover, slope, and land surface air temperature with data analysis using a weighted tiered quantitative method to answer the purpose of knowing the distribution of hazard and vulnerability areas and analysis of the results of mathematical calculations to determine the risk of Angin Puting Beliung. The results obtained show that Semarang city has a high hazard level of 28,502% which is mostly found in the southwest and northeast of Semarang. High levels of vulnerability are in the sub-districts of West Semarang, Mijen, Gunung Pati, and Tembalang. The risk level with a high class ranks the least, namely from other classes with a distribution in the sub-district of Tugu and Tembalang, accounted at 16.294%.

An all-sky 1 km daily land surface air temperature product over mainland China for 2003–2019 from MODIS and ancillary data

Abstract. Surface air temperature (Ta), as an important climate variable, has been used in a wide range of fields such as ecology, hydrology, climatology, epidemiology, and environmental science. However, ground measurements are limited by poor spatial representation and inconsistency, and reanalysis and meteorological forcing datasets suffer from coarse spatial resolution and inaccuracy. Previous studies using satellite data have mainly estimated Ta under clear-sky conditions or with limited temporal and spatial coverage. In this study, an all-sky daily mean land Ta product at a 1 km spatial resolution over mainland China for 2003–2019 has been generated mainly from the Moderate Resolution Imaging Spectroradiometer (MODIS) products and the Global Land Data Assimilation System (GLDAS) dataset. Three Ta estimation models based on random forest were trained using ground measurements from 2384 stations for three different clear-sky and cloudy-sky conditions. The random sample validation results showed that the R2 and root-mean-square error (RMSE) values of the three models ranged from 0.984 to 0.986 and from 1.342 to 1.440 K, respectively. We examined the spatiotemporal patterns and land cover type dependences of model accuracy. Two cross-validation (CV) strategies of leave-time-out (LTO) CV and leave-location-out (LLO) CV were also used to evaluate the models. Finally, we developed the all-sky Ta dataset from 2003 to 2009 and compared it with the China Land Data Assimilation System (CLDAS) dataset at a 0.0625∘ spatial resolution, the China Meteorological Forcing Data (CMFD) dataset at a 0.1∘ spatial resolution, and the GLDAS dataset at a 0.25∘ spatial resolution. Validation accuracy of our product in 2010 was significantly better than other datasets, with R2 and RMSE values of 0.992 and 1.010 K, respectively. In summary, the developed all-sky daily mean land Ta dataset has achieved satisfactory accuracy and high spatial resolution simultaneously, which fills the current dataset gap in this field and plays an important role in the studies of climate change and the hydrological cycle. This dataset is currently freely available at https://doi.org/10.5281/zenodo.4399453 (Chen et al., 2021b) and the University of Maryland (http://glass.umd.edu/Ta_China/, last access: 24 August 2021). A sub-dataset that covers Beijing generated from this dataset is also publicly available at https://doi.org/10.5281/zenodo.4405123 (Chen et al., 2021a).

The Impact of Seasonality and Land Cover on the Consistency of Relationship between Air Temperature and LST Derived from Landsat 7 and MODIS at a Local Scale: A Case Study in Southern Ontario

Land surface temperature (LST) and air temperature (Tair) have been commonly used to analyze urban heat island (UHI) effects throughout the world, with noted variations based on vegetation distribution. This research has compared time series LST data acquired from the Moderate Resolution Imaging Spectroradiometer (MODIS) platforms, Landsat 7 Enhanced Thematic Mapper (ETM+) and Tair from weather stations in the Southern Ontario area. The influence of the spatial resolution, land cover, vegetated surfaces, and seasonality on the relationship between LST and in situ Tair were examined. The objective is to identify spatial and seasonal differences amongst these different spatial resolution LST products and Tair, along with the causes for variations at a localized scale. Results show that MODIS LST from Terra had stronger relationships with Landsat 7 LST than those from Aqua. Tair demonstrated weaker correlations with Landsat LST than with MODIS LST in sparsely vegetated and urban areas during the summer. Due to the winter’s ability to smooth heterogenous surfaces, both LST and Tair showed stronger relationships in winter than summer over every land cover, except with coarse spatial resolutions on forested surfaces.

The influence of fire aerosols on surface climate and gross primary production in the Energy Exascale Earth System Model (E3SM)

AbstractFire-emitted aerosols play an important role in influencing Earth’s climate, directly by scattering and absorbing radiation and indirectly by influencing cloud microphysics. The quantification of fire-aerosol interactions, however, remains challenging and subject to uncertainties in emissions, plume parameterizations, and aerosol properties. Here we optimized fire-associated aerosol emissions in the Energy Exascale Earth System Model (E3SM) using the Global Fire Emissions Database (GFED) and AERONET aerosol optical depth (AOD) observations during 1997-2016. We distributed fire emissions vertically using smoke plume heights from Multi-angle Imaging SpectroRadiometer (MISR) satellite observations. From the optimization, we estimate that global fires emit 45.5 Tg y-1 of primary particulate organic matter and 3.9 Tg y-1 of black carbon. We then performed two climate simulations with and without the optimized fire emissions. We find that fire aerosols significantly increase global AOD by 14 ± 7% and contribute to a reduction in net shortwave radiation at the surface (-2.3 ± 0.5 W m-2). Together, fire-induced direct and indirect aerosol effects cause annual mean global land surface air temperature to decrease by 0.17 ± 0.15°C, relative humidity to increase by 0.4 ± 0.3%, and diffuse light fraction to increase by 0.5 ± 0.3%. In response, GPP declines by 2.8 Pg C y-1, as a result of large positive drivers (decreases in temperature and increases in humidity and diffuse light) nearly cancelling out large negative drivers (decreases shortwave radiation and soil moisture). Our analysis highlights the importance of fire aerosols in modifying surface climate and photosynthesis across the tropics.