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When attempting to estimate the impacts of future climate change it is important to reflect on information gathered during the past. Understanding historical trends may also aid in the assessment of likely future agricultural and horticultural changes. The timing of agricultural activities, such as grape harvest dates, is known to be influenced by climate and weather. However, fewer studies have been carried out on grapevine yield and quality. In this paper an analysis is undertaken of long-term data from the period 1805-2010 on grapevine yield (hl/ha) and must sugar content (°Oe) and their relation to temperature. Monthly mean temperatures were obtained for the same time period. Multiple regression was used to relate the viticulture variables to temperature, and long-term trends were calculated. Overall, the observed trends over time are compatible with results from other long term studies. The findings confirm a relationship between yield, must sugar content and temperature data; increased temperatures were associated with higher yields and higher must sugar content. However, the potential increase in yield is currently limited by legislation, while must sugar content is likely to further increase with rising temperatures.

The transmission of hemorrhagic fever with renal syndrome (HFRS) is influenced by population dynamics of its main host, rodents. It is therefore important to better understand rodents' characteristic in epidemic areas. We examined the potential impact of food available and climatic variability on HFRS rodent host and developed forecasting models. Monthly rodent density of HFRS host and climate data in Changsha from January 2004 to December 2011 were obtained. Monthly normalized difference vegetation index (NDVI) and temperature vegetation dryness index (TVDI) for rice paddies were extracted from MODIS data. Cross-correlation analysis were carried out to explore correlation between climatic variables and food available with monthly rodent data. We used auto-regressive integrated moving average model with explanatory variables to examine the independent contribution of climatic variables and food supply to rodent density. The results indicated that relative rodent density of HFRS host was significantly correlated with monthly mean temperatures, monthly accumulative precipitation, TVDI and NDVI with lags of 1-6 months. Food available plays a significant role in population fluctuations of HFRS host in Changsha. The model developed in this study has implications for HFRS control and prevention.

Pine wilt disease (PWD) constitutes a serious threat to pine forests. Since development depends on temperature and drought, there is a concern that future climate change could lead to the spread of PWD infections. We evaluated the risk of PWD in 21 susceptible Pinus species on a global scale. The MB index, which represents the sum of the difference between the mean monthly temperature and 15 when the mean monthly temperatures exceeds 15°C, was used to determine current and future regions vulnerable to PWD (MB ≥ 22). For future climate conditions, we compared the difference in PWD risks among four different representative concentration pathways (RCPs 2.6, 4.5, 6.0, and 8.5) and two time periods (2050s and 2070s). We also evaluated the impact of climate change on habitat suitability for each Pinus species using species distribution models. The findings were then integrated and the potential risk of PWD spread under climate change was discussed. Within the natural Pinus distribution area, southern parts of North America, Europe, and Asia were categorized as vulnerable regions (MB ≥ 22; 16% of the total Pinus distribution area). Representative provinces in which PWD has been reported at least once overlapped with the vulnerable regions. All RCP scenarios showed expansion of vulnerable regions in northern parts of Europe, Asia, and North America under future climate conditions. By the 2070s, under RCP 8.5, an estimated increase in the area of vulnerable regions to approximately 50% of the total Pinus distribution area was revealed. In addition, the habitat conditions of a large portion of the Pinus distribution areas in Europe and Asia were deemed unsuitable by the 2070s under RCP 8.5. Approximately 40% of these regions overlapped with regions deemed vulnerable to PWD, suggesting that Pinus forests in these areas are at risk of serious damage due to habitat shifts and spread of PWD.

In this article, we present research results on the air temperature changes on the Labrador coast at the end of the 18th century (1771–1787). This important climatic variable was studied on the basis of valuable instrumental meteorological observations made by Moravian missionaries. The data were taken from meteorological registers available in three major archives: the Moravian Archives in Herrnhut (Germany) and the Moravian Archives at Muswell Hill and the Archives of the Royal Society, both located in London (UK). The series of meteorological observations we used in the paper are the oldest and longest long-term meteorological observations available not only for Labrador, but for anywhere in the entire Arctic. Moravian missionaries measured not only air temperature (analysed here) but also atmospheric pressure and wind (force and directions) two, three, or even four times a day. These unique data allow us to better understand the climate variability and trends in the region during the study's historical period. We have analysed sub-daily air temperature readings from three sites: Okak (1776–1787), Nain (1771–1786), and Hopedale (1782–1786). The data were converted into more relatable present-day units and have undergone rigorous quality control. Original mean daily air temperature data calculated using different numbers of measurements per day were corrected to the “real” mean daily values. The corrected values were subsequently used for statistical analysis. The historical temperatures documented during our specified study period along the Labrador coast were compared with those experienced today. The analysis shows a significant warming from historical to present times. Historical data from Nain, Okak, and Hopedale, representing different periods, were, on average, about 0.5 to 2.3 °C colder than in the modern period of 1990 to 2020, especially in winter and autumn. Most monthly mean air temperatures in historical times lie within 2 standard deviations of the modern mean. The frequency of temperature occurrence in 1 d intervals suggests a shift towards more stable and less extreme temperature distributions in contemporary times, implying substantial changes in climate patterns over time in this region. The continentality of the Labrador climate and the year-to-year variability in mean monthly temperatures were greater in historical times than at present.

Emulators of Earth system models (ESMs) are statistical models that approximate selected outputs of ESMs. Owing to their runtime efficiency, emulators are especially useful when large amounts of data are required, for example, for in-depth exploration of the emission space, for investigating high-impact low-probability events, or for estimating uncertainties and variability. This paper introduces an emulation framework that allows us to emulate gridded monthly mean precipitation fields using gridded monthly mean temperature fields as forcing. The emulator is designed as an extension of the Modular Earth System Model Emulator (MESMER) framework, and its core relies on the concepts of generalised linear models (GLMs). Precipitation at each (land) grid point and for each month is approximated as a multiplicative model with two factors. The first factor entails the temperature-driven precipitation response and is assumed to follow a gamma distribution with a logarithmic link function. The second factor is the residual variability in the precipitation field, which is assumed to be independent of temperature but may still possess spatial precipitation correlations. Therefore, the monthly residual field is decomposed into independent principal components and subsequently approximated and sampled using a kernel density estimation with a Gaussian kernel. The emulation framework is tested and validated using 24 ESMs from the sixth phase of the Coupled Model Intercomparison Project (CMIP6). For each ESM, we train on a single-ensemble member across scenarios and evaluate the emulator performance using simulations with historical and Shared Socioeconomic Pathways (SSP5-8.5) forcing. We show that the framework captures grid-point-specific precipitation characteristics, such as variability, trend, and temporal auto-correlations. In addition, we find that emulated spatial (cross-variable) characteristics are consistent with those of ESMs. The framework is also able to capture compound hot–dry and cold–wet extremes, although it systematically underestimates their occurrence probabilities. The emulation of spatially explicit coherent monthly temperature and precipitation time series is a major step towards a computationally efficient representation of impact-relevant variables of the climate system.

How the climate responded to orbital forcing during the Neoproterozoic snowball Earth events, the most extreme glaciations on Earth, is still unclear. Here, we investigate this problem using a climate model. To simplify the analysis, continents are removed. The results show that even in this simplified situation, the snowball Earth climate is sensitive to orbital configurations. The globally averaged annual surface temperature can differ by 2.4°C, and the maximum monthly mean temperature can differ by 3.7°C under different orbital configurations. Therefore, a snowball Earth could be deglaciated more easily in some orbital configurations than in others. The climatic effect of a particular orbital parameter is highly dependent on the values of other parameters. For example, the effect of obliquity on tropical surface temperature is generally small (<1°C), but it can become large (3.8°C) when eccentricity is large and the northern autumn occurs at perihelion (precession = 180°). Surprisingly, the global temperature is generally lower at high eccentricity than at near-zero eccentricity, even though the total insolation received by Earth is higher in the former than in the latter. Moreover, we find that the Milankovitch hypothesis is valid not only in the extratropical region, but also in the tropics; the snow thickness in the tropical region is inversely proportional to the maximum monthly insolation received in this region.

We 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. We describe a method for quantifying the contribution of climate change to local monthly mean temperatures. As a case study, we apply the method to the record‐warm September of 2023 in Helsinki. We show that climate change made the record‐warm September about nine times more likely and 1.4°C warmer than it would have been without human‐induced climate change.

Climate change is likely to make matters worse in Ethiopia, where the primary sources of food production depend on agriculture, mainly rain-fed agriculture. This study has two folds: first, we estimate the marginal impact of climate variables on the dominant staple food crops (teff, maize, wheat, and sorghum) grown in Ethiopia using feasible generalized least square (FGLS) and autocorrelation and heteroscedasticity consistent standard error for 31 years’ time series data. Second, based on these estimates, we used regional climate models (UQAM _CRCM5 and SMHI_ RCA4) to identify yield sensitivity change in the future. A significant rise in mean monthly temperature and positive change in rainfall were observed from 1988 to 2018. Though an increase in maximum temperature had a favorable effect on all crop yields, a similar increase in minimum temperature was found to have an adverse impact. Since 2000 there has been a considerable increase in total production, but the increasing trends have been due to increases in area cultivated. Towards the end of the twenty-first century, the projection of climate impacts has suggested that with significant increases in temperature and decreases in rainfall result the decline of sorghum yield by 18.1% and wheat yield by 13.2%. However, the yield of teff and maize will be expected to increase by 20.2 and 17.9% respectively. We recommend adopting and expanding locally fitted climate-smart agricultural practices to minimize the long-run climate change impacts on crop production and address the country’s food security problems sustainably.

The Yangtze region of South East China has experienced several extreme hot summer months in recent years. Such events can have devastating socio–economic impacts. We use a large ensemble of initialised climate simulations to assess the current chance of unprecedented hot summer months in the Yangtze River region. We find a 10% chance of an unprecedented hot summer month each year. Our simulations suggest that monthly mean temperatures up to 3 °C hotter than the current record are possible. The dynamics of these unprecedented extremes highlights the occurrence of a stationary atmospheric wave, the Silk Road Pattern, in a significant number of extreme hot events. We present evidence that this atmospheric wave is driven by variability in the Indian summer monsoon. Other extreme events are associated with a westward shift in the western North Pacific subtropical high. The most extreme simulated events exhibit combined characteristics of both the Silk Road Pattern and the shifted western North Pacific subtropical high.

The effect of atmospheric circulation on temperature variability and trends in Finland in 1979–2018 is studied using a trajectory-based method. On the average 81% of the detrended interannual variance of monthly mean temperatures is explained by the start points of the three-dimensional trajectories, with the best performance in autumn and winter. Atmospheric circulation change is only found to have had a small impact on the observed annual mean temperature trends, but it has considerably modified the trends in individual months. In particular, changes in circulation explain the lack of observed warming in June, the very modest warming in October in southern Finland, and about a half of the very large warming in December. The residual trends obtained by subtracting the circulation-related change from observations are robustly positive in all months of the year, exhibit a smoother seasonal cycle, and agree better with the multi-model mean temperature trends from models in the 5th Coupled Model Intercomparison Project (CMIP5). Nevertheless, some differences between the residual trends and the average CMIP5 trends are also found.