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When long time series are analyzed, two nearby periods may show significantly different trends, which is known as trend turning. Trend turning is common in climate time series and crucial when climate change is investigated. However, the available detection methods for climate trend turnings are relatively few, especially for the methods which have the ability of detecting multiple trend turnings. In this article, we propose a new methodology named as the running slope difference (RSD) t test to detect multiple trend turnings. This method employs a t-distributed statistic of slope difference to test the sub-series trend difference of the time series, thereby identifying the turning points. We compare the RSD t test method with some other existing trend turning detection methods in an idealized time series case and several climate time series cases. The results indicate that the RSD t test method is an effective tool for detecting climate trend turnings.

Land use/land cover change and climate change have diverse impacts on the water resources of river basins. This study investigated the trends of climate change and land use/land cover change in the Nile River Basin. The climate trends were analyzed using the Mann–Kendall test, Sen’s slope estimator test and an innovative trend analysis method. Land use/land cover (LULC) change was examined using Landsat Thematic Mapper (TM) and Landsat Enhanced Thematic Mapper (ETM+) with a resolution of 30 m during 2012–2022. The findings revealed that forestland and shrub land area decreased by 5.18 and 2.39%, respectively. On the other hand, area of grassland, cropland, settlements and water bodies increased by 1.56, 6.18, 0.05 and 0.11%, respectively. A significant increasing trend in precipitation was observed at the Gondar (Z = 1.69) and Motta (Z = 0.93) stations. However, the trend was decreasing at the Adet (Z = −0.32), Dangla (Z = −0.37) and Bahir Dar stations. The trend in temperature increased at all stations. The significant changes in land use/land cover may be caused by human-induced activities in the basin.

Using quantile regression, which gives a detailed picture of climate trends for any of the quantiles in the range from 0 to 1, calculations of climate trends of maximum daily temperature and daily precipitation for the territory of the Russian Federation are made. For calculations, observational data from more than 1450 meteorological stations for the period from 1987 to 2021 inclusive are used. The clustering of the calculated climate trends is carried out and the spatial features of the clusters of meteorological stations and their correspondence to the quasi-homogeneous climatic regions used for assessments in the annual “Roshydromet Reports on the Peculiarities of the Climate on the Territory of the Russian Federation” have been analyzed. Fifteen clusters of meteorological stations on the territory of the Russian Federation have been identified, such that the stations included in the clusters demonstrate similar features (“patterns”) of trends in maximum daily temperature and daily precipitation amounts. At the same time, the values that determine the geographical location of weather stations are not involved in the clustering process. The results of the work can be used to refine the climatic zoning of the territory of the Russian Federation, taking into account the features of trends in long-term changes in surface climate.

Satellite microwave temperature sounding data have been widely used in the study of climate trends over the past decades. When merging advanced microwave sounding unit-A (AMSU-A) data from 1998 to 2017 from different satellites, brightness temperature observations can be affected by differences in the center frequency, incidence angle, and local equator crossing time (LECT) among the instruments. Atmospheric drag and gravity variations with latitude can also cause orbital drift that leads to changes in LECT of the same instrument. Inter-sensor calibration and diurnal correction are thus necessary before applying AMSU-A data to climate studies. In this study, AMSU-A data from the National Oceanic and Atmospheric Administration’s (NOAA’s) -15, -18, and -19 satellites and the European Organization for the Exploitation of Meteorological Satellites MetOp-A/-B collected during 1998–2017 were first inter-calibrated by a double difference method to remove inter-sensor biases. AMSU-A data from NOAA-18 was used as the reference for the double difference inter-sensor calibration. A diurnal correction was then applied to data over the Amazon rainforest to eliminate the effects of different LECTs. Finally, linear and nonlinear climate trends were calculated to show that warming (cooling) trends over the Amazon rainforest for window and tropospheric AMSU-A channels 1–8 and 15 (stratospheric channels 9–14) significantly decreased (increased) if inter-sensor calibration and a diurnal correction was applied. The nonlinear climate trends reveal more rapid warming trends for tropospheric sounding channels 3–7 and 15 and less rapid cooling trends for stratospheric channels 10–12 during 1998–2008 than during 2008–2017. Channels 1–2 (channel 13) have cooling (warming) and warming (cooling) trends before and after 2008, respectively.

Agricultural research has fostered productivity growth, but the historical influence of anthropogenic climate change (ACC) on that growth has not been quantified. We develop a robust econometric model of weather effects on global agricultural total factor productivity (TFP) and combine this model with counterfactual climate scenarios to evaluate impacts of past climate trends on TFP. Our baseline model indicates that ACC has reduced global agricultural TFP by about 21% since 1961, a slowdown that is equivalent to losing the last 7 years of productivity growth. The effect is substantially more severe (a reduction of ~26–34%) in warmer regions such as Africa and Latin America and the Caribbean. We also find that global agriculture has grown more vulnerable to ongoing climate change.Agricultural productivity has increased historically, but the impact of climate change on productivity growth is not clear. In the last 60 years, anthropogenic climate change has reduced agricultural total factor production globally by 21%, with stronger impacts in warmer regions.

Our study is aimed at detection of directional trends in streamflow data observed in large rivers located in different climatic zones and attribution of the detected changes to climate drivers. We consider detection and attribution as interrelated study stages within a suggested hypothesis testing framework with the use of a hydrological model. First, we test the significance of the trends in the observed streamflow data series of 74 to 82 years long and evaluate the model’s ability to reproduce the trends, so that the trends in the simulated data are statistically indistinguishable from the corresponding observed trends. Herewith, the model is forced by the reanalysis climate data. Then, for the basins where the model reproduces the trends, we move to the attribution stage of the study. At this stage, the hydrological model is forced by the counterfactual (detrended) climate data. If the trend is not detected in the counterfactual-climate-forced simulations, we conclude that the detected observed changes are likely to be attributed to the climate trend. The suggested testing procedure is applied for four river basins: Lena, Selenga, Vyatka, and Pechora. The corresponding hydrological models are developed on the basis of the ECOMAG modeling platform. We conclude that the detected trends in the observed annual flow data series for the Lena, Selenga, and Vyatka rivers, as well as the trends in high flow for the Lena and Selenga rivers, can be attributed to climate drivers with a high confidence. Regional differences in basin mechanisms governing the detected changes are analyzed.

Crop yields are projected to decrease under future climate conditions, and recent research suggests that yields have already been impacted. However, current impacts on a diversity of crops subnationally and implications for food security remains unclear. Here, we constructed linear regression relationships using weather and reported crop data to assess the potential impact of observed climate change on the yields of the top ten global crops-barley, cassava, maize, oil palm, rapeseed, rice, sorghum, soybean, sugarcane and wheat at ~20,000 political units. We find that the impact of global climate change on yields of different crops from climate trends ranged from -13.4% (oil palm) to 3.5% (soybean). Our results show that impacts are mostly negative in Europe, Southern Africa and Australia but generally positive in Latin America. Impacts in Asia and Northern and Central America are mixed. This has likely led to ~1% average reduction (-3.5 X 1013 kcal/year) in consumable food calories in these ten crops. In nearly half of food insecure countries, estimated caloric availability decreased. Our results suggest that climate change has already affected global food production.

Following over 3 decades of gradual but uneven increases in sea ice coverage, the yearly average Antarctic sea ice extents reached a record high of 12.8 × 10⁶ km² in 2014, followed by a decline so precipitous that they reached their lowest value in the 40-y 1979–2018 satellite multichannel passive-microwave record, 10.7 × 10⁶ km², in 2017. In contrast, it took the Arctic sea ice cover a full 3 decades to register a loss that great in yearly average ice extents. Still, when considering the 40-y record as a whole, the Antarctic sea ice continues to have a positive overall trend in yearly average ice extents, although at 11,300 ± 5,300 km²·y−1, this trend is only 50% of the trend for 1979–2014, before the precipitous decline. Four of the 5 sectors into which the Antarctic sea ice cover is divided all also have 40-y positive trends that are well reduced from their 2014–2017 values. The one anomalous sector in this regard, the Bellingshausen/Amundsen Seas, has a 40-y negative trend, with the yearly average ice extents decreasing overall in the first 3 decades, reaching a minimum in 2007, and exhibiting an overall upward trend since 2007 (i.e., reflecting a reversal in the opposite direction from the other 4 sectors and the Antarctic sea ice cover as a whole).

A previous reconstruction back to 800 ce indicated that the 2000–2018 soil moisture deficit in southwestern North America was exceeded during one megadrought in the late-1500s. Here, we show that after exceptional drought severity in 2021, ~19% of which is attributable to anthropogenic climate trends, 2000–2021 was the driest 22-yr period since at least 800. This drought will very likely persist through 2022, matching the duration of the late-1500s megadrought.Southwestern North America has been experiencing lower than average precipitation and higher temperatures since 2000. This emerging megadrought, spanning 2000–2021, has been the driest 22-year period since the year 800 and 19% of the drought severity in 2021 can be attributed to climate change.

Future climate projections focusing on precipitation and water resource trends over South America (SA) are investigated using two ensembles. One of them is composed of three global climate models (GCMs), and the other of eight regional climate models (RCMs) from the Coordinated Regional Climate Downscaling Experiment (CORDEX). The present (1970–2005) and the future (2006–2100) climate trends are analyzed for representative pathway scenarios 4.5 (RCP4.5) and 8.5 (RCP8.5). For the most pessimistic scenario (RCP8.5), trends in water resources are assessed considering the terrestrial branch of the hydrologic cycle by analyzing the precipitation minus evapotranspiration (P-ET). For the present climate, RCMs added value to the GCMs in simulating more realistic precipitation fields in several regions. GCMs and RCMs project, in general, the same precipitation change signal for the end of the 21st century over SA, which is stronger in RCP8.5 than in RCP4.5. For RCP8.5 in most regions, GCMs and RCMs ensembles have the same precipitation trend signal, but a great spread between the ensemble members, which is greater in austral summer than winter, can be noted. In winter a negative trend in rainfall in most members and regions predominates. At the end of the 21st century, relative changes in rainfall in RCP8.5 are in the range of +14% (over northeastern Brazil in summer) to − 36% (over the Andes Mountains in winter). In RCP8.5, the ensembles project an increase in air temperature with a similar magnitude, while in RCP4.5 the trends are weaker. For air temperature, there is small spread between members, and the positive trend is statistically significant for all ensemble members in the RCP8.5 scenario. In terms of water resources, on an annual scale, for RCP8.5 the RCM ensemble projects a larger area with wetter conditions in the future than GCMs. Regionally, it is expected a decrease in water availability in the Amazon basin and an increase over northeast Brazil and southeast SA during the summer. In other regions (northern Amazon, the Andes Mountains and Patagonia) the ensembles indicate drier conditions in the future winter, except in southern Amazon. It is expected that such information could be useful for devising adaptation and mitigation policies due to climate change over the SA.