Surface Temperature Trends Research Articles

AGRO-TOURISM IMPACT ANALYSIS OF CLIMATE CHANGE USING GOOGLE EARTH ENGINE IN THE RAHOVEC WINE REGION OF KOSOVO

Abstract. Climate change is a global problem and has a major impact on the economy of the developing world. The use of open data and modeling provides a considerable resource for governments, municipalities, and NGOs to develop an understanding of how climate change will impact their communities. This paper discusses the statistical concepts used to model MODIS Land Surface Temperature dataset and calculation of water balance with interpretations of the results. The results of this paper will utilize open datasets, an analysis of the impacts of temperature changes on the developing tourist economy in the Republic of Kosovo, and the knowledge of the capacity for leveraging large geographic datasets for open climate change research. The data from this paper shows a high level of vulnerability to viticulture in Kosovo due to significant trends in water balance and land surface temperature trends within the Drini I Bardhë basin and the future impacts of this on the regions wine agro-tourism by understanding the development of climate change in the region.

Arctic warming trends and their uncertainties based on surface temperature reconstruction under different sea ice extent scenarios

IPCC AR6 states that the neglect of recent rapid warming in the Arctic generally leads to an underestimate of global warming trends. In this study, by reconstructing the surface temperature (ST) datasets in the Arctic under different sea ice extent scenarios (Imax and Imin), we respectively evaluated the annual and seasonal warming trends and their uncertainties from 1900 to 2020. The results show that the reconstructed datasets have a good representation of the ST change trends in the Arctic. In 1900–2020, the annual warming trends in the Arctic (0.17 ± 0.031 and 0.14 ± 0.025 °C per decade under the Imax and Imin reconstruction, respectively) are roughly 1.6–1.8 times the global mean warming trends (0.10 ± 0.008 and 0.09 ± 0.008 °C per decade). While in 1979–2020, the Arctic warming trends (0.66 ± 0.100 and 0.55 ± 0.080 °C per decade) increase to 3.1–3.5 times of the global warming trend (0.19 ± 0.023 and 0.18 ± 0.023 °C per decade) for Imax and Imin, respectively, indicating that the Arctic amplification effect has been significantly enhancing in recent decades. Although the seasonal warming trends are closely related to cloud feedback mechanisms, atmospheric circulation, and ocean circulation, they are not sensitive to the different reconstruction scenarios.

City densification and temporal dynamics of traditional inner core of Ibadan, Nigeria

The conversion of vegetation land cover contributes to the retention of solar radiation in the environment, resulting in the formation of the urban heat island. This study analyses the distribution pattern of urban heat island in the traditional core of Ibadan amidst the rapid urbanization experienced. The remote sensing tool was used to analyse the trend of land surface temperature, normalized difference built-up index, and normalized difference vegetation index for the traditional Ibadan's core between the year 2000 and the year 2020. This reveals that increasing built-up areas will continue to strengthen the effects of urban heat island in the traditional core of Ibadan, while vegetated land covers will weaken the effects of urban heat island. This is because anthropogenic activities resulting from rapid urbanisation has adversely altered the natural landscape in the traditional core of Ibadan. This alteration manifests in converting vegetation land covers into physical developments and other impervious surfaces by the increasing urban population. Thus, increasing the land surface temperature. The maximum average land surface temperature of 35.34℃, 36.62℃ and 31.86℃ were record for the years 2000, 2013 and 2020 respectively. This study further recommends that urban greening and proper urban planning should be encouraged in the traditional core of Ibadan. Keywords: Land Surface Temperature, Normalized Difference Built-up Index, Normalized Difference Vegetation Index, traditional core, urban heat island

Historical and Projected Trends of the Mean Surface Temperature in South-Southeast Mexico Using ERA5 and CMIP6

This study aimed to determine the mean temperature trends in the south-southeast region of Mexico during the historical period of 1980–2014, as well as during the future periods of 2021–2040, 2041–2060, and 2081–2100, as recommended by the IPCC. Additionally, the study sought to identify the climate change scenario that is most closely aligned with the socio-environmental conditions of the south-southeast zone of Mexico and that has the greatest impact on the region’s average temperature. The downscaling method of bias correction was conducted at a spatial resolution of 0.25° × 0.25°, and an analysis of historical trends was performed for the period 1980–2014 with ERA5 and four CMIP6 models (CNRM-ESM2-1, IPSL-CM6A-LR, MIROC6, and MRI-ESM2-0). This process was extended to future projections. The models indicated temperature differences of less than 0.5 °C with respect to ERA5, in agreement with other studies. Additionally, the current study calculated future trends for the south-southeast region using three of the CMIP6 scenarios (SSP2-4.5, SSP4-6.0, and SSP5-8.5). The z-eq proposal was used to compare the slopes, enabling us to determine which of the three scenarios corresponded to the historical trend, assuming identical socio-environmental conditions. The SSP4-6.0 scenario was found to correspond to the historical trend.

Global Surface Temperature Variability and Trends and Data Based Attribution to Carbon Emissions

Global Surface Temperature Variability and Trends and Data Based Attribution to Carbon Emissions

Long-term shift and recent early onset of chlorophyll-a bloom and coastal upwelling along the southern coast of Java

Long-term change in the timing of coastal upwelling due to climate variations alters the heat budget and biogeochemical balance in the regional ocean and is an important issue in local fisheries. In this study, we investigated decadal changes in the onset of coastal upwelling along the southern coast of Java over the past two decades (2003–2020) based on the timing of chlorophyll-a (Chl-a) bloom. We estimated the bloom from satellite Chl-a concentration data. On average, the onset of coastal upwelling observed (the first Chl-a bloom of the year) was around mid-June. In the most recent decade (2011–2020), earlier-onset upwelling (before early June) was observed frequently, and the linear trend for the onset date during 2003–2020 was about 2 weeks earlier/decade. To explore the causes of the change in the timing of the upwelling, we focused on the season (April–June) during which these earlier upwelling onsets occurred, and investigated decadal changes in atmosphere and ocean conditions associated with climate change. While sea surface temperature (SST) trends reflected a basin-wide warming pattern in the Indian Ocean, warming was not significant in the southeastern Indian Ocean. During the onset period of coastal upwelling, significant SST warming trends were also observed west of Sumatra. In association with the SST warming pattern, enhanced convective activity and convergent zonal winds around Sumatra were observed. Atmospheric forcing revealed trends favoring Ekman downwelling in the equatorial eastern Indian Ocean and upwelling in the southeastern Indian Ocean, which was consistent with the trends in thermocline depth. This study provides the first results regarding the recent decadal shift in the onset timing of coastal upwelling. Ongoing monitoring is needed to better understand the long-term change of the upwelling system in the eastern tropical Indian Ocean.

Low frequency ambient noise dynamics and trends in the Indian Ocean, Cape Leeuwin, Australia

Examination of 18 years of nearly continuous low frequency deep ocean ambient noise offshore Cape Leeuwin, Australia, finds evidence of a decreasing nonlinear trend suggestive of long-term cyclic dynamics. The nonlinear trend is found to be consistent with trends in oceanographic sea surface temperature, which are thought to drive changes in Antarctic sea ice extent. Assessment of oscillatory dynamics finds causal covariance between ambient noise levels and Indian Ocean sea surface temperature dipoles. Dynamics of annual ambient noise and Antarctic sea ice extent are examined suggesting a phase-locked relationship revealing nonlinear characteristics of the presumed dependence. Collectively, the hypotheses that deep water ambient noise dynamics in the Indian Ocean are influenced by Antarctic sea ice extent and melt dynamics and that linear models do not fully capture long-term ambient noise trends and dynamics are supported.

Opportunities and Challenges of Spaceborne Sensors in Delineating Land Surface Temperature Trends: A Review

Understanding the land surface temperature (LST) trends is crucial for policymakers and stakeholders to develop adaptation and mitigation strategies suitable for a sustainable environment coping in the face of climate change. This article presents a systematic review of the studies related to delineating spaceborne sensor-based LST trends, including information on the instruments and constellations of satellites (missions) that provide thermal infrared (TIR) and passive microwave (PMW) observations. About 99% of the studies used TIR, where 76% were Moderate Resolution Imaging Spectroradiometer (MODIS, onboard Terra/Aqua) observations. Opportunities, challenges, and research gaps for using the TIR and PMW observations were also explored, with instruments onboard either polar-orbiting or geostationary satellites. We identified that the calibrated dataset (e.g., processed, harmonized, and standardized) is extremely limited for each constellation, with multiple satellites and instruments, to make it fully useful for the entire mission period. A few problematic methodological concepts were identified, including using a few images in a longer time series. Using only a few images, acquired on different calendar months in different years, would not provide the true annual trends over the study period because they can be influenced by seasonal variations. To estimate the warming or cooling daytime, nighttime, or diurnal LST trends, the use of MODIS observations could be useful, even though it does not acquire images during the maximum or minimum temperature in a daily cycle. This article indicated further investigations into those research gaps and recommended directions to overcome most of these limitations.

Co-Occurrence of Atmospheric and Oceanic Heatwaves in the Eastern Mediterranean over the Last Four Decades

Heatwaves are now considered one of the main stressors of global warming. As a result of anthropogenic warming, atmospheric and oceanic heatwaves have increased in frequency, intensity and duration in recent decades. These extreme events have recently become a major concern in climate research due to their economic and environmental impacts on ecosystems. In this study, we investigated the co-occurrence and relationship between atmospheric and marine heatwaves (AHW/MHW) in the Eastern Mediterranean (EMED) over the last four decades (1982–2021). Furthermore, the spatio-temporal variability and trends of sea surface temperature (SST), near-surface air temperature (SAT), AHW and MHW characteristics (frequency and duration) were examined. For these objectives, we used daily gridded high-resolution satellite SST data (0.05° × 0.05°) and the fifth generation European Centre for Medium-Range Weather Forecasts (ECMWF-ERA5) atmospheric reanalysis SAT and wind components (0.25° × 0.25°). The results showed an average warming trend of about 0.38 ± 0.08 °C/decade and 0.43 ± 0.05 °C/decade for SAT and SST, respectively. A high statistically significant (p < 0.05) correlation (R = 0.90) was found between AHW and MHW frequency. Our results showed that more than half of the MHWs in the EMED co-occurred with AHWs throughout the study period. The most intense summer MHW in 2021, which co-occurred with AHW, was associated with higher positive anomalies of SAT and SST, and a decrease in the wind speed anomaly.

Evaluating land use/cover change associations with urban surface temperature via machine learning and spatial modeling: Past trends and future simulations in Dera Ghazi Khan, Pakistan

While urbanization puts lots of pressure on green areas, the transition of green-to-grey surfaces under land use land cover change is directly related to increased land surface temperature–compromising livability and comfort in cities due to the heat island effect. In this context, we evaluate historical and future associations between land use land cover changes and land surface temperature in Dera Ghazi Khan–one of the top cities in Pakistan–using multi-temporal Landsat data over two decades (2002–2022). After assessing current land use changes and future predictions, their impact on land surface temperature and urban heat island effect is measured using machine learning via Multi-Layer Perceptron-Markov Chain, Artificial Neural Network and Cellular Automata. Significant changes in land use land cover were observed in the last two decades. The built-up area expanded greatly (874 ha) while agriculture land (−687 ha) and barren land (−253 ha) show decreasing trend. The water bodies were found the lowest changes (57 ha) and vegetation cover got the largest proportion in all the years. This green-grey conversion in the last two decades (8.7%) and prospect along the main corridors show the gravity of unplanned urban growth at the cost of vegetation and agricultural land (−6.8%). The land surface temperature and urban heat island effect shows a strong positive correlation between urbanization and vegetation removal. The simulation results presented in this study confirm that by 2032, the city will face a 5° C high mean temperature based on historical patterns, which could potentially lead to more challenges associated with urban heat island if no appropriate measures are taken. It is expected that due to land cover changes by 2032, ~60% of urban and peri-urban areas will experience very hot to hot temperatures (> 31.5°C). Our results provide baseline information to urban managers and planners to understand the increasing trends of land surface temperature in response to land cover changes. The study is important for urban resource management, sustainable development policies, and actions to mitigate the heat island effect. It will further asset the broader audience to understand the impact of land use land cover changes on the land surface temperature and urban heat island effect in the light of historic pattern and machine learning approach.

Climate Change Impacts on LULC in the Jarmet Wetland and its Surrounding Areas in Western Ethiopia

The concerns over land use and land cover (LULC) change have emerged on the global stage due to realization that changes occurring on the land surface also influence climate, ecosystem and its services. This study aimed to map the temporal dynamic of LULC patterns and LST in the Jarmet wetland in Ethiopia. The dynamics and pattern of changes for a period of 21 years (2000-2021) were analyzed using geospatial techniques. Multi-temporal satellite images from Landsat ETM+ and Landsat-8 OLI sensor data were used to extract land-cover maps. The Land Surface Temperature (LST) trend of the study areas was computed using MODIS satellite imagery (2000-2021). Supervised classification using a Maximum Likelihood Classifier (MLC) was applied to prepare LULC maps of the watershed. The accuracy of the classified map was assessed using high-resolution data, and ground realities have been verified and ascertained through field observations. The results revealed a decreased trend in wetland, forest, shrubland and grassland in the period of 21 years (2000-2021) by -1148.71ha, -1073.26 ha, -1480.1 ha, and -87.73 ha, respectively. On the other hand, farmland and plantation areas followed an increasing trend. LST revealed decreasing trend in terms of mean and minimum with a fraction change of -0.018 and -0.073, whereas the maximum LST value shows an increasing trend with 0.021. The overall accuracy was 84.41%, with Kappa index of 76.13%. The analysis and findings of the study highlight important policy implications for sustainable LULC management in the study area. The study suggests the design and implementation of a guided natural resource policy, stopping the illegal expansion of farmland and educating society about the value of the sustainable management of habitat reserves.

Evolution of seasonal land surface temperature trend in pond-breeding newt (Neurergus derjugini) in western Iran and eastern Iraq

BackgroundTemperature, as one of the effective environmental stimuli in many aspects of species life and ecosystems, can affect amphibians in many ways. Knowing and predicting temperature change and its possible effects on the habitat suitability and movements of amphibians have led many researchers to use climate change scenarios and species distribution models (SDMs). One of the important remote-sensing products that received less attention of conservation biologists is the land surface temperature (LST). Due to the small difference between LST and air temperature, this component can be used to investigate and monitor the daily and seasonal changes of habitats. This study aims to investigate the seasonal trend of LST in the habitat suitability and connectivity of the critically endangered newt (Neurergus derjugini) in its small distribution range, using the MODIS LST time series (2003 to 2021) and with the help of SDMs, Mann–Kendall (MK) and Pettitt non-parametric tests.ResultsIn the last decade, the increasing trend of LST versus its decreasing trends is obvious. Based on MK and Pettitt tests, in the winter and spring, with the decrease in latitude of 35.45° and increase in longitude of 46.14°, the core populations which are located in the southeast have experienced an increase in temperature. Considering the period time of breeding and overwintering, the continuity of winter and spring can be effective on the survival of adult newts as well as larvae in the microclimate. Linkages with the highest current flow between core populations in the winter and summer are the most likely to be vulnerable. At the level of habitat, the increase in LST is proportional to the trend of thermal landscape changes, and all seasons have had an increase in LST, but in winter and summer, the largest area of the habitat has been involved. By continuing the current trend, many high-altitude southern habitats in Iran will be endangered, and the species will be at risk of local extinction.ConclusionThe increasing trend of temperature in all seasons such as winter will affect many adaptations of the species and these effects are mostly evident in the southern parts of its distribution range therefore, captive breeding and reintroduction are recommended for the populations of these areas.

Variability in Projected North American Mean and Extreme Temperature and Precipitation Trends for the 21st Century: Model‐To‐Model Differences Versus Internal Variability

AbstractWhile climate change is expected to alter mean and extreme temperature and precipitation over the 21st century, regional changes in these fields remain difficult to project. For a given future emissions scenario, the uncertainty in future projections can be attributed to either model‐to‐model differences, which can presumably be reduced if models are improved, or internal variability, which cannot be constrained to a single outcome but can be used to quantify a range of plausible outcomes. This study examines the range of possible trends in North American mean and extreme temperature and precipitation across phase 6 of the Coupled Model Intercomparison Project models under the SSP3‐7.0 emissions scenario. While model‐to‐model differences primarily govern the spread in mean and extreme temperature trends across these model simulations, the spread in mean and extreme precipitation trends is dominated by internal variability in many regions. A storylines approach—a type of regression‐based analysis—is used to identify key dynamical drivers that explain the variance in future trends across model simulations. The dynamical drivers considered include trends in global mean surface temperature (GMST) and four common modes of climate variability. Combinations of these drivers can reinforce each other to create high‐impact storylines. For example, model simulations with large positive GMST and El Niño‐Southern Oscillation trends are associated with a large reduction in the annual maximum number of consecutive dry days in the American Southwest. However, limiting these storylines to only models with climate sensitivities consistent with observational constraints reduces the likelihood of a large reduction of consecutive dry days in this region.

The Impact of Land Cover Change on Surface Water Temperature of Small Lakes in Eastern Ontario from 1985 to 2020

Land Cover Change (LCC) has been shown to significantly impact the magnitude and trend of Land Surface Temperature (LST). However, the influence of LCC near waterbodies outside of an urban environment remain less understood. Waterbodies serve as local climate moderators where nearby LCC has the potential to decrease their cooling ability. Altered water surface temperatures can lead to altered species migration and distribution in aquatic species depending on a given species thermal boundary. In this study, using remotely sensed land cover and surface temperature data, we investigate the role that LCC around small lakes (500 m) plays on the surface water temperature change of nine small lakes in the Cataraqui Region Conservation Authority’s watershed, located in Eastern Ontario, from 1985 to 2020. The Continuous Change Detection Classification (CCDC) algorithm was used alongside the Statistical Mono-Window (SMW) algorithm to calculate LCC and LST, respectively. Results indicated a strong positive relationship (R2 = 0.81) between overall LCC and lake surface water temperature (LSWT) trends, where LSWT trends in all inland small lakes investigated were found to be positive. The land cover class sparse vegetation had a strong positive correlation with water temperature, whereas dense vegetation displayed a strong negative correlation. This 35-year study contributes to the broader understanding of the impact that LCC has on the surface water temperature trends of inland lakes.

Colder Eastern Equatorial Pacific and Stronger Walker Circulation in the Early 21st Century: Separating the Forced Response to Global Warming From Natural Variability

AbstractSince the early 1990s the Pacific Walker circulation shows a multi‐decadal strengthening, which contradicts future model projections. Whether this trend, evident in many climate indices especially before the 2015 El Niño, reflects the coupled ocean‐atmosphere response to global warming or the negative phase of the Pacific Decadal Oscillation (PDO) remains debated. Here we show that sea surface temperature trends during 1980–2020 are dominated by three signals: a spatially uniform warming trend, a negative PDO pattern, and a Northern Hemisphere‐Indo‐West Pacific warming pattern. The latter pattern, which closely resembles the transient ocean thermostat‐like response to global warming emerging in a subset of CMIP6 models, shows cooling in the central‐eastern equatorial Pacific but warming in the western Pacific and tropical Indian Ocean. Together with the PDO, this pattern drives the Walker circulation strengthening in the equatorial band. Historical simulations appear to underestimate this pattern, contributing to the models' inability to replicate the Walker cell strengthening.

Score-driven threshold ice-age models: Benchmark models for long-run climate forecasts

Climate variables are known to be subject to abrupt changes when some threshold levels are surpassed. We use data for the last 798,000 years on global ice volume (Ice), atmospheric carbon dioxide level (CO2), and Antarctic land surface temperature (Temp) to model and measure those long-run nonlinear climate effects. The climate variables have very long and asymmetric cycles, created by periods of upward trends, followed by periods of downward trends driven by exogenous orbital variables. The exogenous orbital variables considered by the Milankovitch cycles are eccentricity of Earth’s orbit, obliquity, and precession of the equinox. We show that our new score-driven threshold ice-age models improve the statistical inference and forecasting performance of competing ice-age models from the literature. The drawback of using our 1000-year frequency observations, is that we cannot measure the nonlinear climate effects of humanity created during the last 250 years, which are known to have generated abrupt structural changes in the Earth’s climate, due to unprecedented high levels of CO2 and Temp, and low levels of Ice volume. On the other hand, the advantage of using low-frequency data is that they allow us to obtain long-run forecasts on what would have occurred if humanity had not burned fossil fuels since the start of the Industrial Revolution. These long-run forecasts can serve as benchmarks for the long-run evaluation of the impact of humanity on climate variables. Without the impact of humanity on climate, we predict the existence of turning points in the evolution of the three climate variables for the next 5,000 years: an upward trend in global ice volume, and downward trends in atmospheric CO2 level and Antarctic land surface temperature.

Uncertainty in Preindustrial Global Ocean Initialization Can Yield Irreducible Uncertainty in Southern Ocean Surface Climate

Abstract How do ocean initial states impact historical and future climate projections in Earth system models? To answer this question, we use the 50-member Canadian Earth System Model (CanESM2) large ensemble, in which individual ensemble members are initialized using a combination of different oceanic initial states and atmospheric microperturbations. We show that global ocean heat content anomalies associated with the different ocean initial states, particularly differences in deep ocean heat content due to ocean drift, persist from initialization at year 1950 through the end of the simulations at year 2100. We also find that these anomalies most readily impact surface climate over the Southern Ocean. Differences in ocean initial states affect Southern Ocean surface climate because persistent deep ocean temperature anomalies upwell along sloping isopycnal surfaces that delineate neighboring branches of the upper and lower cells of the global meridional overturning circulation. As a result, up to a quarter of the ensemble variance in Southern Ocean turbulent heat fluxes, heat uptake, and surface temperature trends can be traced to variance in the ocean initial state, notably deep ocean temperature differences of order 0.1 K due to model drift. Such a discernible impact of varying ocean initial conditions on ensemble variance over the Southern Ocean is evident throughout the full 150 simulation years of the ensemble, even though upper ocean temperature anomalies due to varying ocean initial conditions rapidly dissipate over the first two decades of model integration over much of the rest of the globe.

Application of WRF Optimal Parameterization Scheme for Different Air Quality Models

Accurate meteorological fields and applicable air quality models are important ways to optimize air pollution simulations. To improve the accuracy of winter air pollution models in the Sichuan basin, we conducted a meteorological field simulation using 25 sets of parameterized scheme combinations in the Weather Research and Forecasting (WRF) Model. Based on the optimal parameters, the air pollution levels were simulated using AERMOD and CALPUFF models in a local large steel plant, and the data were verified by comparing the data from four National Ambient Air Monitoring Stations (NAAMS). The results indicated that the WRF model parameters had substantial effects on the simulation of the ground wind field, high-altitude wind field, and ground humidity field. In contrast, the parameters had no significant effect on the simulation of the ground temperature field, high-altitude temperature field, and high-altitude humidity field. The combination of the SLAB land surface process scheme and Dudhia shortwave radiation scheme with four boundary layer schemes, namely YSU, ACM2, BouLac, and MRF, could well-simulate the trends of winter surface wind, temperature, and humidity fields in Sichuan basin. The simulation results were analyzed by combining the statistical parameters of high-altitude wind, temperature, and humidity. The group 1 parameter scheme was applicable to simulate the meteorological field of Dazhou. Group 13 and Group 17 parameters were applicable to simulate the meteorological fields in Chengdu during the day and night, respectively. The correlation between CALPUFF simulation and monitoring value was better than that for AERMOD. CALPUFF was more accurate than AERMOD when referring to the monitoring data from NAAMS No.3. In addition, the simulation quality of CALPUFF was slightly better than that of AERMOD with reference to data from NAAMS No.2. Using air pollutant monitoring data from NAAMS as a reference, the simulated results of CALPUFF on NOx and PM10 were improved compared to AERMOD at all four stations. Data from the Q-Q diagram indicated that the simulated results of CALPUFF on SO2, NOx, and PM10 were closer to the monitored values compared to those of AERMOD.

The Weakened Upwelling at the Upstream Kuroshio in the East China Sea Induced Extensive Sea Surface Warming

AbstractThe interannual sea surface temperature trend of the Kuroshio in the East China Sea (ECS‐Kuroshio) is significantly different from that of the ECS‐shelf region. Based on 33 years (1982–2014) of observational temperature data in the ECS, the upstream ECS‐Kuroshio, as an upwelling region, shows an opposite temperature trend to the downstream ECS‐Kuroshio, creating a horizontal “dipole” structure. Vertical heat transport modulated by upwelling variability in the upstream ECS‐Kuroshio can be the main factor for the distinct temperature trend in the upstream ECS‐Kuroshio. This upstream trend can expand to downstream areas through horizontal advections. As a result, the weakened upwelling triggered a significant warming trend in the entire upper layer of the ECS‐Kuroshio during the warming period (1999–2014). The weakening trend of upwelling and global rewarming conditions may lead to robust warming in the Kuroshio region in the middle of this century.

LakeCoSTR: A tool to facilitate use of Landsat Collection 2 to estimate lake surface water temperatures

AbstractAlthough remote sensing of lake surface water temperature has been ongoing for decades and has led to important discoveries regarding the warming of lake surface temperature in some regions, the ability to access and use remote sensing data is still primarily limited to remote sensing experts. Effective use of remote sensing data involves technical skills in coding (often in multiple programming languages), application of appropriate atmospheric corrections, and integrating spatially heterogeneous remote sensing data with in situ data obtained at specific geographic locations. To improve access to remote sensing data and broaden the understanding of changes in lake surface water temperature over the past four decades, we created lakeCoSTR (lake Collection 2 Surface Temperature Retrieval), a user‐friendly, cloud‐based script that gives ecologists lacking specialized training in remote sensing the ability to access the Landsat Collection 2 temperature estimates for lakes with a surface area of at least 0.4 ha. Additionally, if in situ data are provided, a paired dataset can be created within the tool. To demonstrate lakeCoSTR, we retrieved surface water temperature data for a lake with a long monitoring history, Lake Sunapee, NH, USA, and compared long‐term surface temperature trends between data obtained via lakeCoSTR and in situ measurements. When compared with Landsat Collection 1 temperature estimates derived from a single‐channel algorithm, the Landsat Collection 2 data from lakeCoSTR required no calibration to in situ data. This suggests that lakeCoSTR can be used to document temporal trends in lakes and to reprocess analyses that relied on Collection 1 data. From 1983 until 2020, remotely sensed surface water temperature estimates at Lake Sunapee increased by 0.07–0.09°C year−1 during July and August, a trend that agreed with long‐term in situ record. As climate change continues to impact freshwater systems, a better understanding of long‐term temperature data will be vital. Remotely sensed data, like those acquired using lakeCoSTR, may provide a window into temperature trends for lakes.