Seasonal Mean Temperature Research Articles

Temperature and Precipitation Change Assessment in the North of Iraq Using LARS-WG and CMIP6 Models

Investigating the spatial-temporal evolutionary trends of future temperature and precipitation considering various emission scenarios is crucial for developing effective responses to climate change. However, researchers in Iraq have not treated this issue under CMIP6 in much detail. This research aims to examine the spatiotemporal characteristics of temperature and rainfall in northern Iraq by applying LARS-WG (8) under CMIP6 general circulation models (GCMs). Five GCMs (ACCESS-ESM1-5, CNRM-CM6-1, MPI-ESM1-2-LR, HadGEM3-GC31-LL, and MRI-ESM2-0) and two emissions scenarios (SSP245 and SSP585) were applied to project the upcoming climate variables for the period from 2021 to 2040. The research relied on satellite data from fifteen weather sites spread over northern Iraq from 1985 to 2015 to calibrate and validate the LARS-WG model. Analysis of spatial-temporal evolutionary trends of future temperature and precipitation compared with the baseline period revealed that seasonal mean temperatures will increase throughout the year for both scenarios. However, the SSP585 scenario reveals the highest increase during autumn when the spatial coverage of class (15–20) °C increased from 27.7 to 96.29%. At the same time, the average seasonal rainfall will rise in all seasons for both scenarios except autumn for the SSP585 scenario. The highest rainfall increment percentage is obtained using the SSP585 for class (120–140) mm during winter. The spatial extent of the class increased from 25.49 to 50.19%.

Improved annual temperature cycle function for stream seasonal thermal regimes

AbstractSeasonal regimes of stream temperatures are important for ecological health as well as for societal water use. Seasonal regimes can be captured in the annual temperature cycle (the mean temperature for each day of the year) or in summary statistics such as seasonal mean temperatures, the former of which is the focus of this work. The annual temperature cycle is often characterized as a sine function, which performs satisfactorily for most streams. However, the sine function is unable to capture major seasonal variations, particularly for colder, drier, and high‐elevation regions. Seasonal summary statistics are effective for classification but do not capture the full time series, preventing the use of lost time‐series information, and lack context for the comparison of trends, hindering distinction between different causes of similar seasonal trends. We propose an improved function called the “three‐sine model” to describe the stream annual temperature cycle with higher accuracy and demonstrate its use in two case studies. The three‐sine model uses a cosine function over the entire year coupled with two seasonal anomaly sine functions. The three‐sine model captures the stream annual temperature cycle with eight parameters, reveals distinct spatial trends, and outperforms the sinusoidal model for all elevations and 99% of streams. We conclude that this approach can support improved stream temperature analysis by capturing detailed seasonal trends in context.

Structure and Trends in Climate Parameters of Wine-Growing Regions in Slovenia

This study examined the structure and trends of climate parameters important for grape production from 1952 to 2022 in the wine-growing regions of Podravje, Posavje, and Primorska in Slovenia. Average and extreme temperature and precipitation data from six meteorological stations in three wine-growing regions were divided into annual and growing seasons. The results show that in the period 1991–2022, there was a warming in the growing season in all regions by 1.4–1.7 °C, except the southern part of Primorska (Koper station) 0.6 °C, compared to the reference period 1961–1990. The heat accumulation indices (GDDs and HI) increased significantly, which is mainly due to the increase in the maximum temperature in the growing season temperature (GST max) and the number of days with Tmax > 30 °C (NDT30). The NDT30 increased the most, by a factor of more than four. In the reference period (1961–1990), however, the trend in the number of hot days was even slightly negative. The mean seasonal temperature rose to around 17 °C in regions with a continental climate and to around 19 °C in the Mediterranean region, which could be reflected in the earlier ripening of the grapes. The trends show a decrease in total annual precipitation (AP) after 1991, but this was significant only at one inland location (Maribor), while the total precipitation during the growing season (GSP) decreased significantly at three locations (Maribor, Bilje, and Koper).

Rapid advance of climatic tree limits in the Eastern Alps explained by on-site temperatures

In the European Alps, mean temperature has risen by 2.5 K since the end of the nineteenth century. A 2 K warming of the growing season has taken place in the last 4 decades only. The 2.5 K warming should rise the position of the climatic treeline by about 400 m. Actual shifts in uppermost tree positions reported here for the Austrian Defereggen Valley and the Swiss Lower Engadine region of the Eastern Alps reach only around 140 m of elevation above the limit of old trees that date back to the nineteenth century. Uppermost Pinus cembra trees of > 2 m height currently occur at c. 2500 m, representing elevation records for the Eastern Alps. In situ temperature records for 2022–2023 revealed seasonal mean temperatures for uppermost trees that are 1–3 K higher than the equilibrium treeline isotherm of c. 6 °C in both regions (corrected for temperature anomalies from long-term records). The 2 K span reflects microhabitat differences and two ways to define the season. Thus, tree advances lag behind the upslope shift of the treeline isotherm, on average, by more than 200 m. The uppermost trees currently grow under quite warm conditions with annual shoot length increments frequently reaching 20 cm. Even without additional future warming, the new steady-state climatic treeline will exceed the Holocene maximum elevation in the Eastern Alps substantially.

Assessing the Link between Wildfires, Vulnerability, and Climate Change: Insights from the Regions of Greece

Forests provide a wide range of ecosystem services which are important for achieving sustainable development. Anthropogenic climate change has led to the increased frequency and severity of forest fires, which imply losses of valuable ecosystem services. This paper provides a methodological framework based on Multiple Criteria Decision Aid methods for determining relative regional vulnerabilities associated with forest fires. Different notions of ex-post vulnerability are defined based on the forest area burned and the frequency of forest fires, and their relation to the regions’ area and certain socioeconomic characteristics. The climatic drivers of forest fire occurrence are explored by linking forest fires with summer and spring temperatures and precipitation, using econometric count data analysis. The methodology is applied to Greece and its administrative regions for the period 2000–2022. Ex-post vulnerability of regions to forest fires based on physical and socioeconomic characteristics is calculated, and expected changes in the frequency of fires of specific size classes conditional on the evolution of mean seasonal regional temperature and precipitation according to IPCC scenarios are predicted. Relative vulnerability estimates and the impact of specific climatic drivers on forest fires will be useful in designing policies for preserving forests as natural capital and promoting sustainability.

Anthropogenic influence on seasonal extreme temperatures in eastern China at century scale

Due to the scarcity of observational data in the early 20th century, very limited research has explored the impact of human activities on temperature extremes at the regional scale. Here we used a newly developed homogenized near-surface air temperature dataset from the beginning of the 20th century to estimate the frequency and intensity of extreme temperatures in eastern China and evaluate their anthropogenic influence based on models from the Coupled Model Intercomparison Project Phase 6 (CMIP6). We found clear increases in warm extremes and decreases in cold extremes since 1901 for both annual and seasonal mean temperatures, with more pronounced changes in recent decades. The most significant warming occurred in spring and winter, approximately double the smallest warming observed in autumn. The CMIP6 models generally replicated the century-scale warming in annual and seasonal temperature extremes, showing increases in the frequency and intensity of warm extremes and corresponding decreases in cold extremes. The optimal fingerprinting detections suggest that the century-scale warming can be clearly attributed to anthropogenic forcing, including changes in seasonal extreme temperatures. Most observed changes in extreme temperatures were attributable to anthropogenic greenhouse gas emissions, partially offset by a smaller negative impact from anthropogenic aerosol forcing, whereas natural forcing has played a minor role. These results provide important information for accurately projecting future changes in temperature extremes.

Unlocking Weather Observations at the End of the World: Late-XIX and Early-XX Century Monthly Mean Temperature Climatology for Southern Patagonia

A climate analysis of the monthly mean temperatures of Southern Patagonia during the late-XIXth and early-XXth centuries was carried out as part of the international data rescue Atmospheric Circulation Reconstructions over the Earth (ACRE) program partnership in Argentina, together with other data sources with regional and global records. The data from these diverse sources were combined to carry out a study in the coastal region of Patagonia, including Tierra del Fuego, between 42° S and 55° S for 11 locations. Furthermore, HadSST monthly/seasonal fields during the period 1880–1920 were also used. Both mean monthly and seasonal temperature values and timeseries variability were considered. Their analysis shows consistent behavior within the study region and compared to Southern Hemisphere mean results, which are characterized by a warm late-XIX century and a cooler early-XX century. This is also in agreement with SST variability along the coasts of Patagonia and hemispheric records. A comparison with present-day observations, where available, also yields consistent behavior. Low-frequency variability, i.e., in periods longer than 3 years, during the study period is consistent with present variability. Trend estimates at Trelew and Rio Gallegos for the period 1901–2020 yield significant trends, consistent with hemispheric warming at their latitudes.

Topography influences diurnal and seasonal microclimate fluctuations in hilly terrain environments of coastal California.

Understanding the topographic basis for microclimatic variation remains fundamental to predicting the site level effects of warming air temperatures. Quantifying diurnal fluctuation and seasonal extremes in relation to topography offers insight into the potential relationship between site level conditions and changes in regional climate. The present study investigated an annual understory temperature regime for 50 sites distributed across a topographically diverse area (>12 km2) comprised of mixed evergreen-deciduous woodland vegetation typical of California coastal ranges. We investigated the effect of topography and tree cover on site-to-site variation in near-surface temperatures using a combination of multiple linear regression and multivariate techniques. Sites in topographically depressed areas (e.g., valley bottoms) exhibited larger seasonal and diurnal variation. Elevation (at 10 m resolution) was found to be the primary driver of daily and seasonal variations, in addition to hillslope position, canopy cover and northness. The elevation effect on seasonal mean temperatures was inverted, reflecting large-scale cold-air pooling in the study region, with elevated minimum and mean temperature at higher elevations. Additionally, several of our sites showed considerable buffering (dampened diurnal and seasonal temperature fluctuations) compared to average regional conditions measured at an on-site weather station. Results from this study help inform efforts to extrapolate temperature records across large landscapes and have the potential to improve our ecological understanding of fine-scale seasonal climate variation in coastal range environments.

Evaluation of the Trends of Climate Variability During Different Crop Seasons in Dera Ghazi Khan

Climate change is profoundly impacting ecosystems and disrupting agricultural systems by altering temperature and precipitation patterns, affecting crop yields and food production. This study evaluates the trends of climate variability during different crop seasons in Dera Ghazi Khan, Pakistan, focusing on temperature and rainfall fluctuations from 1991 to 2020. Climatic data were collected and analyzed, including monthly average temperature and rainfall. The analysis of mean monthly decadal temperature in the study area during the Rabi crop season showed significant fluctuations ranging from 20.04°C, 20.5°C, and 19.73°C in three corresponding decades. The mean monthly seasonal temperature was calculated 20.1°C. Similarly, during the Kharif crop season, notable variations across the mean decadal temperature ranging from 34°C to 34.91°C with mean seasonal temperature 34.6°C. Overall, in both seasons during the past three decades +0.1°C was observed with an annual increase of 0.003°C. All these values were found statistically significant. Mean monthly rainfall also exhibited remarkable variability, with statistically significant differences observed for most months in both Rabi and Kharif seasons, except for November and December in the Rabi season and June and October in the Kharif season. Overall, rain has decreased in the last 30 years to -0.4mm during Rabi and -3.7mm during Kharif seasons. The literature suggests that minor changes in climatic factors remarkably impact crop production. These findings highlight the dynamic nature of climatic conditions in the region and underscore the importance of understanding and adapting to climate change for sustainable agricultural practices.

AgERA5 representation of seasonal mean and extreme temperatures in the Northern Cape, South Africa

Over regions with sparse observation networks, including South Africa’s Northern Cape Province, gridded data sets represent valuable supplementary data sources enabling spatially detailed climate investigations. Their performance is, however, influenced by regional characteristics, thus a performance assessment should be a prerequisite for any regional application. Through a pairwise comparison with eight point-based temperature records, we evaluated the AgERA5 data sets representation of mean summer (November–March; Tms) and winter (May–September; Tmw) temperatures and respective seasonal heatwave and coldwave characteristics across the Northern Cape for 1980–2020. Correlations ranging from 0.48 to 0.92 for Tms and from 0.38 to 0.94 for Tmw reflect relatively strong, but varying, temporal correspondence between the AgERA5 data and stations. Low biases, averaging −0.08 (0.17) °C and ranging from -0.79 to 2.10 (-0.40 to 1.47) °C for Tms (Tmw) were evident. Biases for the heatwave (coldwave) magnitudes were low, averaging -0.38 (0.19) °C2, and ranging from -1.55 to 1.47 (-2.05 to 2.91) °C2. Biases for the heatwave (coldwave) frequency were also low, but typically overestimated, averaging 1.19 (0.73) days, and ranging from -1.33 to 5.60 (-1.61 to 3.39) days. Biases for the heatwave (coldwave) number were low and typically overestimated, averaging 0.27 (0.08) events, and ranging from -0.28 to 1.40 (-0.39 to 0.39) events. Despite some stations depicting consistently poor performance, the study results support further application of the AgERA5 product for spatiotemporal analyses of mean and extreme temperatures across the Northern Cape, provided limitations are adequately acknowledged. Further application of the fine-resolution AgERA5 product will greatly inform impact-based studies exploring mean and extreme temperature influences over the Northern Cape Province.

A method for estimating the effect of climate change on monthly mean temperatures: September 2023 and other recent record‐warm months in Helsinki, Finland

AbstractWe describe a method for quantifying the contribution of climate change to local monthly, seasonal, and annual mean temperatures for locations where long observational temperature records are available. The method is based on estimating the change in the monthly mean temperature distribution due to climate change using CMIP6 (Coupled Model Intercomparison Project Phase 6) model data. As a case study, we apply the method to the record‐warm September 2023 in Helsinki, and then briefly examine all record‐warm months of the 21st century. Our results suggest that climate change made the record‐warm September in Helsinki 9.4 times more likely and 1.4°C warmer. Thus, the new monthly mean record in September 2023 would probably not have been set without the observed global warming. The presented and provided tool allows operational meteorologists and climatologists to monitor and report the impact of climate change on local temperatures in near real time.

Genetically based variation in heat tolerance covaries with climate in a globally important disease vector

IntroductionVector-borne diseases (VBDs) are an increasingly important global health concern in the face of climate change. Understanding the ecology and evolution of vector species is critical to predicting and combating VBD. Vectorial capacity models, used to forecast disease transmission, traditionally assume traits are constant among populations, and little is known about whether different vector populations vary in thermal tolerance. To test for geographic variation in upper thermal tolerance, we determined the critical thermal maximum (CTmax) of Aedes albopictus, a globally distributed mosquito and competent vector for many viruses including West Nile, chikungunya, and dengue.MethodsWe studied CTmax for eight different populations spanning four climate zones across the Eastern United States using common garden experiments to isolate genetic variation. To explore potential drivers of this variation we then tested the relationship between climatic variables measured near each population source and CTmax.ResultsWe found significant differences across populations for both adults and larvae, and these differences were more pronounced for adults. Larvae had higher CTmax values compared to adults. Several climatic variables improved models of CTmax for both adults and larvae including mean and max seasonal temperature, annual precipitation, and relative humidity. Annual precipitation appears to be particularly important and has a positive linear relationship with CTmax.DiscussionThe differences between life stages likely result from different selection pressures experienced in their terrestrial and aquatic habitats. Importantly, the assumption that mosquito populations within a species have the same upper thermal limits does not hold in this case, thus it is important to use population-specific CTmax values, among other important physiological parameters that may vary, to more accurately model and forecast VBDs.

Current dynamics of air temperature in Saint Petersburg and suburbs

The dynamics of mean annual, seasonal and monthly temperatures at meteorological stations of Saint Petersburg and its suburbs in 1960-2023 was studied. It was found that actual growth of temperatures is revealed for annual and seasonal averages, but not for monthly values - in half of the cases only. This trends reflects only global processes, as the difference between the city and suburbs has not changed significantly over the period under consideration. The increase is mainly due to winter temperatures, while summer temperatures increased minimally.

Otolith-temperature estimates in Atlantic bluefin tuna (Thunnus thynnus) from the Mediterranean Sea: Insights from clumped isotope measurements

The subpopulation and/or contingent structure of Atlantic bluefin tuna (Thunnus thynnus) within the Mediterranean Sea is undefined, leading to uncertainty regarding the best strategy for an effective assessment and management of this highly exploited stock. This study aimed to reconstruct temperatures experienced by Atlantic bluefin tuna during the early life period (<3.5 months) using clumped isotope temperature proxy, an innovative geothermometer for carbonates, that does not require previous knowledge of other environmental parameters such as water oxygen composition. We examined otolith chemistry in fish captured from 3 different areas of the Mediterranean Sea and adjacent waters. We found that mean seasonal temperature estimates from clumped isotopes did not differ significantly from satellite derived and otolith oxygen stable isotopic ratios derived temperatures, except for the central Mediterranean Sea, were clumped isotopes derived temperatures were significantly higher than satellite derived temperatures. However, the sensitivity of the clumped isotope thermometer was found to be lower than that based on oxygen fractionation equation, with high variance observed in the clumped isotopes derived temperature estimates. We also observed that clumped isotope derived temperatures were undistinguishable among bluefin tuna captured in the Gibraltar Strait, the central, and eastern Mediterranean Sea. In this paper, we discuss the major sources of uncertainty in temperature reconstructions using bluefin tuna otoliths.

Recent Climate Change in the Republic of Srpska and Bosnia and Herzegovina

The paper gives an overview of the observed climate change in the Republic of Srpska and Bosnia and Herzegovina; primarily the changes in air temperature and precipitation regime. In the period 1961‒2015 the positive trends of mean, maximum and minimum annual and seasonal air temperatures were determined, which were most pronounced in the summer season, whereas in the autumn season there is only a slight upward trend of temperatures. Warming is most pronounced in the northern part of the territory, whereas the increase is much lower in the higher parts of the Herzegovina region. Unlike air temperature, precipitation trends are, both seasonally and regionally, very variable and of different signs (positive and negative), but mostly insignificant. However, in some areas, the interannual variability of precipitation and the frequency of extreme precipitation have increased. In addition, the paper presents the results of research on possible climate change (based on expected changes in air temperature and precipitation) by the end of the 21st century in the Republic of Srpska and Bosnia and Herzegovina according to the climate scenario RCP8.5. The results indicate more pronounced climate change related to increasing annual temperatures up to 5 °C and reducing annual precipitation by up to 30%, whereas the deficit in the summer season (June, July and August) could go up to 40% by the end of the 21st century. Such changes point to the serious problem of drought and water scarcity and the need for more significant integration of climate change issues into planning and strategic documents.

Association between ambient temperature and common allergenic pollen and fungal spores: A 52-year analysis in central England, United Kingdom

Exposure to pollen and fungal spores can trigger asthma/allergic symptoms and affect health. Rising temperatures from climate change have been associated with earlier seasons and increasing intensity for some pollen, with weaker evidence for fungal spores. It is unclear whether climate change has resulted in changes in the exposure-response function between temperature and pollen/fungal spore concentrations over time. This study examined associations between temperature and pollen/fungal spores in different time periods and assessed potential adaptation using the longest pollen/fungal spore dataset in existence (52 years). Daily concentrations of pollen (birch and grass) and fungal spores (Cladosporium, Alternaria, Sporobolomyces and Tilletiopsis) collected between April and October from Derby (1970–2005) and Leicester (2006–2021), UK, were analysed. Cumulative seasonal concentrations (seasonal integral) and start-of-season were calculated and linked to seasonal mean temperatures (Tmeans) using generalized additive models. Daily concentrations were evaluated against daily Tmean with distributed lagged nonlinear models. Models were adjusted for precipitation, relative humidity, long-term trend and location. Seasonal and daily analyses were respectively stratified into two periods (1970–1995, 1997–2021) and five decades. Warmer seasonal Tmeans were associated with higher seasonal integral for birch, Cladosporium and Alternaria, as well as earlier start-of-season for birch, grass and Cladosporium. There were indications of changing associations with temperature in the recent decades. A warmer January was associated with higher seasonal integral for grass in 1997–2021, but not in 1970–1995. In 2000–2021, daily concentrations of birch pollen tended to remain at higher levels, vs. decrease during 1990s, when Tmean was between 13 and 15 °C. Our study suggests higher temperatures experienced in recent decades are associated with higher overall abundance of some pollen/fungal spores, which may increase future disease burdens of allergies. The changing responses of some pollen to higher temperatures over time may indicate adaptation to increasing temperatures and should be considered in climate change mitigation and adaptation planning.

Climate zones in Iran

AbstractClimate classification provides a framework for a better understanding of the dominant weather patterns in different regions of the Earth. This study aims at identifying climate zones in Iran based on the analysis of monthly temperature and precipitation over 139 synoptic stations across Iran during the period 1991–2020. Based on the application of the principal component analysis, we identified six distinct climate zones in Iran: mild and humid, cool and sub‐humid, cold and temperate semi‐arid, warm and semi‐arid, cool and arid, and warm and hyperarid. The highest precipitation occurs in the southern coastal plains of the Caspian Sea, characterized by a mild and humid climate. The climate of western Iran is identified as cool and sub‐humid, while northwestern Iran is characterized by a cold and temperate semi‐arid climate. Southwestern Iran is identified as a region with a warm and semi‐arid climate, while northeastern Iran has a cool and arid climate. Southeastern and central Iran are both characterized by a warm and hyperarid climate. The highest monthly and seasonal precipitation values over Iran occur in March (48.6 mm) and winter (134.2 mm), respectively, while the highest monthly and seasonal mean temperature values occur in July (29.1°C) and summer (28.0°C), respectively. In terms of seasonal variation, the maximum precipitation occurs in the southern coastal plains of the Caspian Sea in autumn, while the minimum occurs in southwestern Iran in summer. Our results have important implications for better understanding and analysing the climatic characteristics across Iran.

Potential consequences for rising temperature trends in the Oti River Basin, West Africa

IntroductionOne of the ways that climate change manifest itself is through temperature changes. Though the Oti River basin has been grappling with drought incidents, there has been little or no emphasis on analyzing temperature fluctuations in the basin. This study aimed to analyze the mean annual and seasonal temperature for the observed (1981–2010) and future periods (2021–2050) over the Oti River basin.MethodsHistorical data were obtained from meteorological stations and the National Aeronautics and Space Administration Prediction of Worldwide Energy Resources (NASA POWER). Data quality assessment was conducted, and the NASA POWER temperature was validated against the stations' temperature. Ensemble of eight models acquired from the Coordinated Regional Climate Downscaling Experiment (CORDEX–Africa) under two Representative Concentration Pathways (RCP4.5 and RCP8.5), were used for the future projection. The mean annual and seasonal temperatures were analyzed for 1981–2010 and 2021–2050 (under RCPs 4.5 and 8.5 scenarios). The Modified Mann–Kendall test was used for trend analysis at 5% significant level.Results and discussionIn the near-future, temperature is anticipated to increase at the mean monthly scale in the ranges of +0.88°C in October to +2.65°C in January under the RCP4.5 scenario, while the RCP8.5 predicts increases between +2.71°C in July and +6.48°C in January. The annual mean temperature change for the entire basin is projected at +1.47°C (RCP4.5) and +4.2°C (RCP8.5). For the rainy season period, the RCP4.5 projects annual mean temperature changes in the ranges of −0.72°C and +1.52°C while the RCP8.5 predicts changes between +1.06°C and +4.45°C. Concerning the dry season period, the anticipated changes in the annual mean temperature under the RCP4.5 would range from −0.43°C to +2.78°C whereas that of RCP8.5 would be between +1.97°C and 7.25°C. The Modified Mann–Kendall test revealed significantly increasing trends for temperature projections in the basin under both the RCPs 4.5 and 8.5 in the basin. The study provides significant contribution to the comprehension of temperature patterns in time and space which is necessary for the sustenance of rainfed agriculture and water resources within the basin.

Air Temperature Variability of the Northern Mountains in the Czech Republic

Analysis of a long-term temperature variability of mountains has been neglected for a long time. Here we homogenised and reconstructed four temperature series (1961–2020) of selected mountain stations situated above 1000 m representing the area of northern Moravian mountain ridges (the Czech Republic) and their spatiotemporal variability was examined. A statistically significant increase of 10-year linear trends of annual (0.26–0.38), summer/winter half-year and seasonal mean temperatures were found at most stations. Summer (JJA) was detected as the season with the highest mean temperature increase between 1961–1990 and 1991–2020 normal periods (1.5 °C). From the spatial point of view, the climate conditions were becoming warmer faster in higher elevations compared to the lower ones. At all stations, a statistically significant decrease in ice and frost days was observed (−9.1–19.9 and −9.5–16.3, respectively). Moreover, it was proved that the temperature series of a newly established Vysoká hole station are representative enough and comparable with measurements of other meteorological stations in the studied region. This research provided valuable insight into the temperature variability of mountain ridges and the results completed our sparse knowledge about temperature changes in the mountain regions in Central Europe.

Short-Term Climate Prediction over China Mainland: An Attempt Using Machine Learning, Considering Natural and Anthropic Factors

Climate extremes pose significant natural threats to socioeconomic activities. Accurate prediction of short-term climate (STC) can provide relevant departments with warnings to effectively reduce this threat. To accurately predict STC in China, this study utilizes machine learning algorithms, particularly the random forest (RF) model, to evaluate the role of both natural and anthropogenic factors. Monthly temperature and precipitation data from 160 meteorological stations spanning China, as well as natural climate factors and an economic activity index, were obtained to perform a seasonal hindcast of air temperature and precipitation observed from 1979 to 2018. Our focus was to predict the seasonal mean temperature and precipitation, specifically the summer (June, July, and August (JJA)) and winter (December, January, and February (DJF)) air temperature and precipitation anomalies using forecast factors from the preceding season. Results show that a comprehensive consideration of both natural and anthropogenic effects provides a more accurate fit to the observed climate trends compared to using only one factor. When both factors were integrated, the model scores (coefficient of determination) exceeded 0.95, close to 1.00, which is significantly higher than those of natural (0.86 for temperature, 0.85 for precipitation) or anthropogenic (0.90 for temperature and 0.50 for precipitation) factors alone. Furthermore, we also attempted to predict similar components for 2019 and 2020. The average relative error between predictions and observations was less than 10%, indicating that this integrated model’s performance exhibited a significant improvement in predicting the STC. The findings of this study underscore the importance of accounting for both natural and anthropogenic factors in predicting climate trends to inform sustainable decision-making in China.