Observed Temperature Data Research Articles

Dynamics of soil water, temperature, and salt and their coupled effects in Haloxylon ammodendron forests of different ages in an arid desert oasis ecotone

Study regionThe study region is a typical desert oasis ecotone of the Hexi Corridor, Northwest China. Study focusBased on four years of in situ observational data of soil water, temperature, and electrical conductivity obtained from three Haloxylon ammodendron (H. ammodendron) forests (20, 30 and 50 years) in a typical desert oasis ecotone (DOE) of the Hexi Corridor, in this study, the mechanisms of change and coupled effects of water, heat, and salt in frozen desert soils along a long-term shrub plantation in a typical desert oasis ecotone are presented. New hydrological insights for the regionThis study revealed that changes in soil water, heat, and salt and their coupling effects were completely different between shallow (0–80 cm) and deep (160–200 cm) depths. At 0–80 cm, soil water gradually decreased in the 50-year-old H. ammodendron plot, but soil salt first increased in the 30-year plot and then decreased in the 50-year plot. When the freezing process occurred in the 0–80 cm depth interval, the soil water and salt contents decreased nonlinearly with the absolute value of the soil temperature (|T|), following a power function and logarithmic function, respectively. For the thawing process in the 0–80 cm depth interval, soil water and salt increased with increasing temperature, and there was a significant linear relationship between soil water and salt (P<0.01). In the 160–200 cm layer, soil water and salt significantly increased after 30–50 years during the growing season.

The recent increasing frequency of strong cooling event in Southwest China in February

AbstractA strong cooling event refers to a sharp change in the average temperature over a short period. The rapid change of temperature has important effect on human health and is highly concerned recent years. Based on the observed temperature data set from stations in Southwest China (SWC) from 1979 to 2017, this paper analyses the characteristics of the strong cooling event (SCE). The result shows that SCE occurs with the highest frequency during the time from February to May. Among them, the frequency of SCE in February exhibits an abrupt change before and after 2005 with a significant increase. Further study reveals that the change of SCE frequency in February is associated with the large‐scale background circulation patterns. After 2005, there is a cyclonic circulation anomaly in Northeast Asia and an anticyclonic circulation anomaly in the Tibet Plateau (TP). This pattern provides a favourable condition for the southward movement of cold air mass, thereby increasing the frequency of SCE in SWC. Furthermore, it is revealed that there is a strong correlation between the variation of SCE frequency in February and sea surface temperatures (SST) in the Northwest Pacific in January before 2005. The cold SST anomaly could favour the occurrence of extreme TD events in SWC through vertical circulation. After 2005, the correlation between SCE and Northwest Pacific SST is not significant. The sea ice in the northern Barents Sea and Kara Sea becomes the dominant impact factor. The abnormally low sea ice concentration is conducive to strengthen the meridional circulation over East Asia, leading to an increasing frequency of SCE in SWC.

ASSESSING THE DYNAMICS OF CLIMATE CHANGE IN KHULNA CITY: A COMPREHENSIVE ANALYSIS OF TEMPERATURE, RAINFALL, AND HUMIDITY TRENDS

The study analyzes the temperature, rainfall, and humidity dynamics of Khulna city from 1981 to 2020. The average rainfall changes are 0.0018 mm, with an increase in daytime temperatures of 0.0223°C each year. Nighttime temperatures also show an increasing trend, with an increase in minimum temperatures of 0.0342°C each year. The overall humidity decreases, indicating less humid weather. The decadal average rainfall ranges from 5.13 mm from 1981 to 2000, with a drop to 4.65 mm between 1991 and 2000. The maximum temperature ranges from 30.94°C to 31.31°C, with a slight increase to 22.17°C between 2011 and 2020. The decadal average humidity ranges from 81.28% from 1991–2000 to 80.72% from 2001–2010. Pre-monsoon average rainfall declines by 5.13 mm, indicating a drier season. The monsoon season has an inclining trend of 0.0239 mm, with a promising increment of rain resulting in a wetter monsoon. Post-monsoon average rainfall increases by 0.0121 mm, resulting in a wetter season each year. The winter season has a slight decline of -0.0043 mm at 6°C. The variability of temperature, rainfall, and humidity patterns in Khulna city reveals a correlation between rainfall and temperature, which indirectly impacts crop yield. Better observational rainfall, humidity, and temperature data are necessary for effective agriculture and crop production. The estimated value for the average temperature (maximum) from 1981 to 2020 is 0.023°C, suggesting that if the year increases by one year, the average maximum temperature increases by 0.023°C.

High-Resolution Temperature Evolution Maps of Bangladesh via Data-Driven Learning

As a developing country with an agricultural economy as a pillar, Bangladesh is highly vulnerable to adverse effects of climate change, so the generation of high-resolution temperature maps is of great value for Bangladesh to achieve agricultural sustainable development. However, Bangladesh’s weak economy and sparse meteorological stations make it difficult to obtain such maps. In this study, by mining internal features and links inside observed data, we developed an efficient data-driven downscaling technique to generate high spatial-resolution temperature distribution maps of Bangladesh directly from observed temperature data at 34 meteorological stations with irregular distribution. Based on these high-resolution historical temperature maps, we further explored a data-driven forecast technique to generate high-resolution temperature maps of Bangladesh for the period 2025–2035. Since the proposed techniques are very low-cost and fully mine internal links inside irregular-distributed observations, they can support relevant departments of Bangladesh to formulate policies to mitigate and adapt to climate change in a timely manner.

Variations in Maximum and Minimum Temperature in Mount Qomolangma during 1971–2020

Based on the daily maximum and minimum temperature observational data during 1971–2020, the variabilities of the maximum and minimum temperature of Mount Qomolangma are analyzed. The daily maximum temperature is 25.8 °C and the daily minimum temperature is −31.4 °C during the study period in Mount Qomolangma. Overall, there has been an upward trend with decadal laps for both maximum and minimum temperature. On monthly, seasonal, and annual scales, neither maximum temperature nor minimum temperature time series exhibit an increasing trend from 1971 to 2020. The increasing trends in monthly minimum temperature are even more pronounced than those in maximum temperature. Abrupt changes are noted in both monthly, seasonal, and annual maximum and minimum temperature time series. Specifically, an abrupt change in annual maximum temperature occurred in the 1980s, while an abrupt change in annual minimum temperature occurred in the 1990s. Differences between the north and south slope of Mount Qomolangma are evident, with temperature fluctuations of the north slope being more extreme than those of south slope. The seasonal and annual maximum temperature of the north slope is higher than that of the south slope, except for winter, and the seasonal and annual minimum temperatures of the north slope are all lower than those of the south slope. The tendences of maximum and minimum temperatures in the north slope are more dominant than those in the south slope. The findings are beneficial for understanding the characteristics of local climate change on the Tibetan plateau and to underscore the significant role of Mount Qomolangma in the context of global warming.

Sources and paths of extreme cold surges over China and their associated atmospheric circulations

AbstractThe extreme cold surges (ECSs) are among the extreme weather events that affect China. Here, a total of 31 ECSs that occurred from 1962 to 2018 in China were identified using observed temperature data. In addition, we used the hybrid single‐particle Lagrangian integrated trajectory (HYSPLIT) model to trace their sources. And the atmospheric circulation of ECSs with different paths were investigated. The ECSs were classified to K‐type, B‐type and S‐type ECSs (which was subdivided into the S1‐type, S2‐type and S3‐type) according to their origins and entry locations into China. The S‐type ECSs occurred the most, accounting for 58.06%. The duration and temperature drop of the K‐type ECSs were higher than those of others. And K‐type and S1‐type ECSs mainly affected Northeast China, S2‐type ECSs mainly affected the regions east of 105° E. For decadal variation, Since the 1980s, ECSs frequency has decreased (especially only one ECS occurred in the 1980s), and K‐type and S2‐type ECSs disappeared. Different types of cold surges have different atmospheric circulation characteristics. The “positive–negative–positive” wave train structures of geopotential height anomalies at 500 hPa were very conducive to the formations of K‐type, B‐type and S3‐type ECSs. For K‐type and S1‐type ECSs, the cold high gradually moved south to Siberia, and combined with the cyclone anomalies, causing the cold air to enter China. Before the B‐type ECSs occurred, cold high appeared in the west of Xinjiang, which made the southeast cold air invade China. Regarding S1‐type and S2‐type ECSs, the negative anomalies over Eurasia from day −6 to day −4 and the subsequent cyclone anomalies at 850 hPa accompanied by the southeastward extension of cold high led to ECSs.

Lake Water Temperature Modeling in an Era of Climate Change: Data Sources, Models, and Future Prospects

AbstractLake thermal dynamics have been considerably impacted by climate change, with potential adverse effects on aquatic ecosystems. To better understand the potential impacts of future climate change on lake thermal dynamics and related processes, the use of mathematical models is essential. In this study, we provide a comprehensive review of lake water temperature modeling. We begin by discussing the physical concepts that regulate thermal dynamics in lakes, which serve as a primer for the description of process‐based models. We then provide an overview of different sources of observational water temperature data, including in situ monitoring and satellite Earth observations, used in the field of lake water temperature modeling. We classify and review the various lake water temperature models available, and then discuss model performance, including commonly used performance metrics and optimization methods. Finally, we analyze emerging modeling approaches, including forecasting, digital twins, combining process‐based modeling with deep learning, evaluating structural model differences through ensemble modeling, adapted water management, and coupling of climate and lake models. This review is aimed at a diverse group of professionals working in the fields of limnology and hydrology, including ecologists, biologists, physicists, engineers, and remote sensing researchers from the private and public sectors who are interested in understanding lake water temperature modeling and its potential applications.

Physics-reserved spatiotemporal modeling of battery thermal process: Temperature prediction, parameter identification, and heat generation rate estimation

The battery thermal process, as a typical distributed parameter system (DPS), is critical to battery management. In practice, the process information is often partially known. Data-driven spatiotemporal modeling is always used to approximate the battery thermal process. However, purely data-driven spatiotemporal modeling methods suffer from expensive costs for data acquirement, unsatisfactory accuracies for long-term prediction, and poor generalization performance. These inherent shortcomings significantly limit the applications of this kind of method in DPS modeling. To address these issues, we propose a novel physics-reserved spatiotemporal modeling method that embeds the partially known physics into deep learning architecture. Specifically, the proposed method constructs a composite network with the battery current and terminal voltage as extra control inputs to model the battery thermal process. By fully using the information conveyed by the partially known physics and the observed temperature data, it can predict the battery temperature accurately. Meanwhile, it can identify the unknown parameters and estimate the heat generation rate of the battery thermal process, which has not yet been achieved by data-driven spatiotemporal modeling methods. This paper makes an attempt to bridge the gap between physics and observed data for spatiotemporal modeling. The effectiveness of the proposed method has been demonstrated by extensive experiments.

Coral Sr‐U Thermometry Tracks Ocean Temperature and Reconciles Sr/Ca Discrepancies Caused by Rayleigh Fractionation

AbstractUnderstanding climate change at the spatiotemporal scales necessary to improve climate projections requires proxy records that complement sparse and often contradictory observational temperature data sets. Massive long‐lived corals have tremendous potential in this regard, continuously recording information about ocean conditions as they grow. Nevertheless, extracting accurate ocean temperatures from corals is challenging because factors other than temperature influence skeletal chemistry. Here, we tested the ability of the coral Sr‐U thermometer to accurately capture annual sea surface temperatures (SSTs) in the subtropical Atlantic, where year‐to‐year temperatures vary by ∼1°C. Using laser ablation inductively coupled plasma mass spectrometry (LA‐ICP‐MS), we generated sufficient U/Ca – Sr/Ca pairs from a slow‐growing (1−2 mm/yr) Siderastrea siderea coral to calculate annual Sr‐U values. With the fine‐scale spatial resolution attained using the laser, skeleton accreted during both fast and slow growing times of the year was represented in our sampling. The resulting 30‐year‐long Sr‐U record tracked the amplitude and timing of annual SST to within ±0.2°C of observations (r = −0.71), whereas the Sr/Ca record did not (r = 0.23). Furthermore, Sr‐U corrected for Sr/Ca offsets among adjacent skeletal elements approximately 1 mm apart. These offsets are equivalent to differences of 2–3°C if typical Sr/Ca–SST calibrations are applied. Our observations indicate that Sr‐U can accurately constrain decadal‐to‐multidecadal variability and secular SST trends in regions where this information is urgently needed.

NN‐MLT Model Prediction for Low‐Latitude Region Based on Artificial Neural Network and Long‐Term SABER Observations

AbstractThe low‐latitude mesosphere and lower thermosphere (MLT) regions are distinct and, highly turbulent transition zones in Earth's atmosphere. The scarcity of reliable measurements makes continuous monitoring of these areas challenging. Therefore, the necessity for studies focused on the MLT region cannot be overstated, as they are essential for developing effective models that meet the accuracy requirements of satellite‐based observations. The neural networks NN‐MLT model, developed using 15 years of Thermosphere, Ionosphere, and Mesosphere Energetics and Dynamics/satellite, equipped with Broadband Emission Radiometry (SABER) observed temperature data spanning from January 2006 to December 2020, employs neural network techniques. The data set was split, with 90% used for training and the remaining 10% allocated for prediction. The model's validation was tested with two other partitions (80(20) and 70(30)). The 90(10) partition, exhibiting a high correlation coefficient (R), low standard deviation (σ), and low root mean square error (RMSE), demonstrated the model's good performance. As clearly shown from statistical metrics (R, RMSE, mean, and σ) at three specific altitude levels (60, 75, and 90 km), the NN‐MLT model's performance aligns closely with the empirical model (NRLMSISE2‐0) and SABER observations. The NN‐MLT model displays a high R (0.74) and low RMSE (4.35 K) at 60 km, indicating its effective performance compared to the other two heights of 75 and 90 km. The NN‐MLT model's spatiotemporal variability in MLT temperature prediction agrees well with the SABER data at all altitudes, particularly at 60 km. While the NN‐MLT model accurately captures the seasonal variations of MLT temperature, the analysis leads to the conclusion that it consistently outperforms the empirical model and aligns closely with observations.

Effect of Temperature and Rainfall Variability on Selected Crop Yields in Wenchi Municipality of Ghana

The paper examines how rainfall and temperature variability affect the yields of selected food crops in the Wenchi municipality of Ghana. The study employed the quantitative research method to achieve its objective. The datasets consisted of historical records of monthly observational data of temperature and rainfall collected over 22 years (2000-2021), and yearly records of cassava, yam, and maize yield data collected over 22 years (2000 to 2021). Trend analysis was conducted to examine the trend in the climate variables (temperature and rainfall) and the yield of the selected food crops (cassava, yam, and maize) over the study period. A marginal variation in annual rainfall and temperature was observed over the study period. The municipality experienced slightly decreasing trends in annual rainfall and increasing trends in annual temperatures. Significant variations and increasing trends in the yields of the respective food crops were observed. Using standard regression methods, the result shows that maximum and minimum temperatures are associated with increased yields for yam, maize and cassava, but annual rainfall is not significantly associated with the variations in the yields of the selected food crops. The study, therefore, suggests that further research be undertaken to investigate how other environmental and non-climatic factors influence crop production in the Wenchi municipality.

Monitoring sudden stratospheric warmings under climate change since 1980 based on reanalysis data verified by radio occultation

Abstract. We developed a new approach to monitor sudden stratospheric warming (SSW) events under climate change since 1980 based on reanalysis data verified by radio occultation data. We constructed gridded daily mean temperature anomalies from the input fields at different vertical resolutions (basic-case full resolution, cross-check with reanalysis at 10 stratospheric standard pressure levels or 10 and 50 hPa levels only) and employed the concept of threshold exceedance areas (TEAs), the geographic areas wherein the anomalies exceed predefined thresholds (such as 30 K), to monitor the phenomena. We derived main-phase TEAs, representing combined middle- and lower-stratospheric warming, to monitor SSWs on a daily basis. Based on the main-phase TEAs, three key metrics, including main-phase duration, area, and strength, are estimated and used for the detection and classification of SSW events. An SSW is defined to be detected if the main-phase warming lasts at least 6 d. According to the strength, SSW events are classified into minor, major, and extreme. An informative 42 winters' SSW climatology (1980–2021) was developed, including the three key metrics as well as onset date, maximum-warming-anomaly location, and other valuable SSW characterization information. The results and validation against previous studies underpin that the new method is robust for SSW detection and monitoring and that it can be applied to any quality-assured reanalysis, observational and model temperature data that cover the polar region and winter timeframes of interest, either using high-vertical-resolution input data (preferable basic case), coarser standard-pressure-levels resolution, or (at least) 10 and 50 hPa pressure level data. Within the 42 winters, 43 SSW events were detected for the basic case, yielding a frequency of about 1 event per year. In the 1990s, where recent studies showed gaps, we detected several events. Over 95 % of event onset dates occurred in deep winter (December–January–February timeframe, about 50 % in January), and more than three-quarters have their onset location over northern Eurasia and the adjacent polar ocean. Regarding long-term change, we found a statistically significant increase in the duration of SSW main-phase warmings of about 5(±2) d over the climate change period from the 1980s to the 2010s, raising the average duration by nearly 50 % from about 10 d to 15 d and inducing an SSW strength increase of about 40(±25) million km2 days from about 100 to 140 million km2 days. The results are robust (consistent within uncertainties) across the use of different input data resolutions. They can hence be used as a reference for further climate-change-related studies and as a valuable basis for studying SSW impacts and links to other weather and climate phenomena, such as changes in polar-vortex dynamics and in mid-latitude extreme weather.

Hybrid thermal modeling of additive manufacturing processes using physics-informed neural networks for temperature prediction and parameter identification

Understanding the thermal behavior of additive manufacturing (AM) processes is crucial for enhancing the quality control and enabling customized process design. Most purely physics-based computational models suffer from intensive computational costs and the need of calibrating unknown parameters, thus not suitable for online control and iterative design application. Data-driven models taking advantage of the latest developed computational tools can serve as a more efficient surrogate, but they are usually trained over a large amount of simulation data and often fail to effectively use small but high-quality experimental data. In this work, we developed a hybrid physics-based data-driven thermal modeling approach of AM processes using physics-informed neural networks. Specifically, partially observed temperature data measured from an infrared camera is combined with the physics laws to predict full-field temperature history and to discover unknown material and process parameters. In the numerical and experimental examples, the effectiveness of adding auxiliary training data and using the pretrained model on training efficiency and prediction accuracy, as well as the ability to identify unknown parameters with partially observed data, are demonstrated. The results show that the hybrid thermal model can effectively identify unknown parameters and capture the full-field temperature accurately, and thus it has the potential to be used in iterative process design and real-time process control of AM.

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.

Prediction of evaporation from dam reservoirs under climate change using soft computing techniques.

This study aimed to predict evaporation from dam reservoirs using artificial intelligence considering climate change. Mahabad Dam, near Lake Urmia, in northwestern Iran, is used to investigate the proposed approach. There are three parts to the study presented herein. In the first part, two machine learning models, namely group method of data handling (GMDH) and least squares support vector regression (LS-SVR), were used to model the inflow to the dam reservoir. Temperature, precipitation, and inflow during the previous month from 1990 to 2017 were used as input data. In the second part, the evaporation from the dam reservoir was modeled using the adaptive neuro-fuzzy inference system (ANFIS) and optimized ANFIS using Harris hawks optimization (HHO) and the arithmetic optimization algorithm (AOA) optimization algorithms. The input parameters in this part were temperature, precipitation, inflow to the dam reservoir, along with evaporation from the dam reservoir in the previous month. In the third part, precipitation and temperature were predicted using the fifth report of IPCC based on RCP2.6, RCP4.5, and RCP8.5 scenarios for the period 2020-2040. Out of 28 models presented in the fifth report, EC-ERATH and FIO-ESM had the greatest similarity with observational data of temperature and precipitation, respectively. The results of scatter plots and Taylor's diagram showed the higher performance of LS-SVR (root mean square error (RMSE), mean absolute percentage error (MAPE), and Nash-Sutcliffe efficiency (NSE) of 8.65, 4.69, and 0.96) compared to GMDH (RMSE, MAPE, and NSE of 11.65, 7.81, and 0.93) in modeling the inflow. Moreover, both hybrid modes (AOA-ANFIS and HHO-ANFIS) improved the performance of ANFIS in modeling the evaporation from the dam reservoir. The RMSE, MAPE, and NSE values for ANFIS were 0.56, 0.52, and 0.89, respectively, while these values for the AOA-ANFIS (RMSE, MAPE, and NSE of 0.31, 0.24, and 0.93) and HHO-ANFIS (RMSE, MAPE, and NSE of 0.20, 0.30, and 0.96) were improved remarkably. The impact of climate change reduced the inflow to the dam reservoir by about 0.45, 0.80, and 1.7 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. Also, the effect of climate change caused the evaporation from the dam reservoir to increase by about 0.2, 0.9, and 1 MCM in RCP2.6, RCP4.5, and RCP8.5 scenarios, respectively. The findings of this study show that the correct management of dam reservoirs needs to consider the potential effects of climate change in the future. Moreover, the hybrid machine learning models used in this study can be used to predict the amount of evaporation in other reservoirs.

The Role of Irrigation Expansion on Historical Climate Change: Insights From CMIP6

AbstractTo produce food for a growing world population, irrigated areas have increased from approximately 0.63 million km2 of land in 1900 to 3.1 million km2 of land in 2005. Despite this massive expansion, irrigation is still overlooked in most state‐of‐the‐art Earth system models (ESMs) involved in the Coupled Model Intercomparison Project phase 6 (CMIP6). To our knowledge, only three CMIP6 models represent irrigation activities: CESM2, GISS‐E2‐1‐G, and NorESM2‐LM. Here, we investigate the role of irrigation on historical climate at global and regional scales by analyzing trends of key surface climate variables in CMIP6 simulations during 1900–2014. The three models including irrigation show distinct behavior from the 15 models without irrigation over intensively irrigated areas (i.e., &gt;50% of grid cell area is equipped by irrigation): both annual (months that correspond to monthly air temperature higher than 274 K) mean latent heat flux (LHF) and soil moisture increase over time, in contrast to the models without irrigation that show no trend or even a negative trend. The positive LHF trend over intensively irrigated areas in the irrigation‐on models is consistent with three satellite‐based LHF products. While annual (considering the warmest month in a year) warming trends are found in these regions for most of the no‐irrigation models, the increase in LHF induces a cooling trend for the models with irrigation, which was not confirmed by observational air temperature data sets. These findings, supported by satellite data, indicate the importance of improved representation of land management in the next generation of ESM.

Homogenization of Swedish mean monthly temperature series 1860–2021

AbstractThe Swedish monthly average 2 m temperature observational data set is homogenized with a new automated version of the homogenization tool HOMogenizaton softwarE in R. Data from 836 individual time series (1860–2021) are merged into 456 time series with a novel automatic merging method. Merging limits the need of interpolation of data and increase the number of long time series without a net loss of data. Twenty‐two of the merged time series were found to be homogeneous. For the other time series, the median time per break‐point is 17 years. 37% of the detected break‐points are supported in meta data. 42% of the data are corrected by ±0.5°C or less, 3% by ±1°C or more. On average corrections are negative, larger in the early periods, and larger in the summertime. The average trend 1860–2021 in the resulting merged and homogenized data set is (0.13 ± 0.03)°C decade, which does not significantly differ from that of the raw observational data. Extremely warm months, defined as being outside of three times the standard deviation from the average of the full time series, are most frequent and extremely cold months least frequent in the most recent 30‐year period (1991–2020). In the homogenized data set, extremely warm months are even more frequent and extremely cold months even less frequent in 1991–2020, than in the raw observational data set.

Effects of climate change on vegetation and snow cover area in Gilgit Baltistan using MODIS data.

The Hindukush-Karakoram-Himalaya (HKH) mountain ranges are the sources of Asia's most important river systems, which provide fresh water to 1.4 billion inhabitants in the region. Environmental and socioeconomic conditions are affected in many ways by climate change. Globally, climate change has received widespread attention, especially regarding seasonal and annual temperatures. Snow cover is vulnerable to climate warming, particularly temperature variations. By employing Moderate Resolution Imaging Spectroradiometer (MODIS) datasets and observed data, this study investigated the seasonal and interannual variability using snowcover, vegetation and land surface temperature (LST), and their spatial and temporal trend on different elevations from 2001 to 2020 in these variables in Gilgit Baltistan (GB), northern Pakistan. The study region was categorized into five elevation zones extending from < 2000 to > 7000 masl. Non-parametric Mann-Kendall trend tests and Sen's slope estimates indicate snow cover increases throughout the winter, but decreases significantly between June and July. In contrast, GB has an overall increasing annual LST trend. Pearson correlation coefficient (PCC) reveals a significant positive relationship between vegetation and LST (PCC = 0.73) and a significant negative relationship between LST and snow cover (PCC = - 0.74), and vegetation and snow cover (PCC = - 0.78). Observed temperature data and MODIS LST have a coefficient of determination greater than 0.59. Snow cover decreases at 3000-2000 masl elevations while increases at higher 5000 masl elevations.The vegetation in low and mid-elevation < 4000 masl zones decreases significantly annually. The temperature shows a sharply increasing trend at lower 2000-3000 masl elevations in the autumn, indicating the shifting of the winter seasons at this elevation zone. These findings better explain the spatiotemporal variations in snow cover, vegetation, and LST at various elevation zones and the interactions between these parameters at various elevations across the HKH region.

Estimating return intervals for extreme climate conditions related to winter disasters and livestock mortality in Mongolia

Abstract. Mass livestock mortality events during severe winters, a phenomenon that Mongolians call dzud, cause the country significant socioeconomic problems. Dzud is an example of a compound event, meaning that multiple climatic and social drivers contribute to the risk of occurrence. Existing studies argue that the frequency and intensity of dzud events are rising due to the combined effects of climate change and variability, most notably summer drought and severe winter conditions, on top of socioeconomic dynamics such as overgrazing. Summer droughts are a precondition for dzud because scarce grasses cause malnutrition, making livestock more vulnerable to harsh winter conditions. However, studies investigating the association between climate and dzud typically look at a short time frame (i.e., after 1940), and few have investigated the risk or the recurrence of dzud over a century-scale climate record. This study aims to fill the gaps in technical knowledge about the recurrence probability of dzud by estimating the return periods of relevant climatic variables: summer drought conditions and winter minimum temperature. We divide the country into three regions (northwest, southwest, and east Mongolia) based on the mortality index at the soum (county) level. For droughts, our study uses as a proxy the tree-ring-reconstructed Palmer drought severity index (PDSI) for three regions between 1700–2013. For winter severity, our study uses observational data of winter minimum temperature after 1901 while inferring winter minimum temperature in Mongolia from instrumental data in Siberia that extend to the early 19th century. Using a generalized extreme value distribution with time-varying parameters, we find that the return periods of drought conditions vary over time, with variability increasing for all the regions. Winter temperature severity, however, does not change with time. The median temperature of the 100-year return period for winter minimum temperature in Mongolia over the past 300 years is estimated as −26.08 ∘C for the southwest, −27.99 ∘C for the northwest, and −25.31 ∘C for the east. The co-occurrence of summer drought and winter severity increases in all the regions in the early 21st century. The analysis suggests that a continued trend in summer drought would lead to increased vulnerability and malnutrition. Prospects for climate index insurance for livestock are also discussed.

Partly decoupled tree-ring width and leaf phenology response to 20th century temperature change in Sweden

The recent warming trend, and associated shifts in growing season length, challenge the principle of uniformitarianism, i.e., that current relations are persistent over time, and complicates the uncritical inferences of past climate from tree-ring data. Here we conduct a comparison between tree-ring width chronologies of Pinus sylvestris L. (Scots pine), Picea abies (L.) Karst. (Norway spruce) and Betula pubescens Ehrh. (Downy birch) and phenological observations (budburst and leaf senescence) of Fagus sylvatica L. (European beech), Quercus robur L. (European oak), Betula sp. (Birch), Norway spruce and Scots pine) in Sweden to assess to what extent the tree-ring width–temperature relationship and the timing of phenological phases are affected by increased temperature. Daily meteorological observations confirm a prolongation of the thermal growing season, most consistently observed as an earlier onset of around 1–2 weeks since the beginning of the 20th century. Observations of budburst closely mimic this pattern, with budburst of the deciduous trees occurring 1–2.5 weeks earlier. In contrast to the changes seen in phenology and observational temperature data, the tree-ring width–temperature relationships remain surprisingly stable throughout the 20th century. Norway spruce, Scots pine and Downy birch all show consistently significant correlations with at least one 30 day-long window of temperature starting in late June–early July season. Norway spruce displays the largest degree of stability, with a consistent 60 day-long temperature window with significant correlation starting around Julian calendar day 150. Thus, our results suggest that the principle of uniformitarianism is not violated during the period covered by modern meteorological observations. Further research is needed to determine at what thresholds the temperature sensitivity of these species may alter or deteriorate as a consequence of the ongoing climate change.