Projection of climate extremes in China, an incremental exercise from CMIP5 to CMIP6

This study reports model assessments and future projections of the Coupled Model Intercomparison Project Phase 6 (CMIP6) models in simulating spring climate extremes over China and its eight subregions. The CMIP6 models are generally able to capture mean climate extremes and trends but have common deficiencies such as topography‐related cold‐temperature biases and precipitation overestimations. The CMIP6 multimodel median ensemble (MME) typically outperforms the individual models in simulating spring climate extremes. However, in terms of the model performance metrics, almost no models show better performance in reference to all indices compared to the observations. Furthermore, we investigate the future changes in these indices on national and regional scales over the near‐term (2021–2040), mid‐term (2041–2060), and long‐term (2081–2100) future periods relative to the 1995–2014 reference period under four Shared Socioeconomic Pathway (SSP) scenarios: SSP1‐2.6, SSP2‐4.5, SSP3‐7.0, and SSP5‐8.5. By the end of the 21st century, wet and warm extreme climate events are projected to increase, particularly in northern and western China. While dry and cold extreme climate events are projected to decrease, particularly in southern China. Additionally, the future projections of both temperature and precipitation extreme indices are remarkable under the high‐greenhouse‐gas (GHG)‐emission scenarios, with the projected changes exceeding those expected under the low‐GHG scenarios.

Given that climate extremes in China might have serious regional and global consequences, an increasing number of studies are examining temperature extremes in China using the Coupled Model Intercomparison Project Phase 5 (CMIP5) models. This paper investigates recent changes in temperature extremes in China using 25 state-of-the-art global climate models participating in CMIP5. Thirteen indices that represent extreme temperature events were chosen and derived by daily maximum and minimum temperatures, including those representing the intensity (absolute indices and threshold indices), duration (duration indices), and frequency (percentile indices) of extreme temperature. The overall performance of each model is summarized by a “portrait” diagram based on relative root-mean-square error, which is the RMSE relative to the median RMSE of all models, revealing the multi-model ensemble simulation to be better than individual model for most indices. Compared with observations, the models are able to capture the main features of the spatial distribution of extreme temperature during 1986–2005. Overall, the CMIP5 models are able to depict the observed indices well, and the spatial structure of the ensemble result is better for threshold indices than frequency indices. The spread amongst the CMIP5 models in different subregions for intensity indices is small and the median CMIP5 is close to observations; however, for the duration and frequency indices there can be wide disagreement regarding the change between models and observations in some regions. The model ensemble also performs well in reproducing the observational trend of temperature extremes. All absolute indices increase over China during 1961–2005.

In this study, we present a brief analysis of the performances of global climate models participating in the Coupled Model Intercomparison Project Phase 5 (CMIP5) in simulating climate extreme events in China and compare the results with those of the previous model generation (CMIP3). The primary focus of this analysis is the climate mean and variability of each extreme index. Results show that the CMIP5 models are generally able to capture the mean climate extremes and trends compared with a new gridded observational dataset. The model spread for some extreme indices is reduced in CMIP5 when compared with CMIP3. Furthermore, the models generally show higher skills in simulating the temperature-based indices than the precipitation-based indices in terms of means and linear trends. Results from six reanalyses further reveal large uncertainties for these indices and it is difficult to say which reanalysis is better for comparison with the simulations of all indices. Based on the relative errors of the climatology, the model evaluation varies considerably from one index to another. However, some models appear to perform substantially better than the others when the average of all indices is considered for each model, and the median ensembles outperform the individual models in terms of all the extreme indices and their means. Additionally, a relationship is observed between the improved simulation of the climate mean and the improved performance of its variability, although this improvement is limited to particular models.

Historical changes and possible future projections of temperature extremes in China, in terms of return values of annual extreme temperatures, are examined based on daily maximum and minimum temperatures from station observations and multiple models of the fifth and sixth phases of the Coupled Model Intercomparison Project (CMIP). The observations suggest that increases in temperature extremes are largely attributable to the changing mean climate, while the varying natural variability also has an important impact, which depends on the index of the variability. The models simulate warm extremes reasonably well but underestimate the spatial heterogeneity and temporal trend of cold extremes in China. In comparison, Coupled Model Intercomparison Project Phase 6 (CMIP6) models have higher skill in simulating temperature extremes in China, showing smaller biases and intermodel variability. MRI‐ESM2‐0 and NorESM2‐LM from CMIP6 and GFDL‐ESM2M and NorESM1‐M from CMIP5 are selected as reference models based on the better performance in reproducing observed temperature extremes in China. In the future, projections from CMIP6 multimodel ensemble (MME, represented as the multimodel median) and reference models all show a continued uptick in temperature extremes, with statistically significant increases in warm extremes mainly in the north and increases in cold extremes prominent in most parts of China. Different individual models, which have similar historical simulations, yield divergent future trends of temperature extremes, which may be associated with different climate sensitivities of models. In addition, MME usage should be treated with caution since its smoothing on spatial heterogeneity and possible information from poor models.

India aspires to increase its reliance on renewable energy sources to fulfil its climate commitments. Among renewables, Solar Photovoltaic (SPV) energy has grown rapidly around the world, including in India. However, little is known about how solar dimming and global warming may affect solar power over the region in the future. The production of SPV energy is influenced by meteorological parameters, highlighting the concerns related to grid stability, intermittency, and reliability caused by weather-induced variability.  Under the Paris Agreement, all the nations agree to restrict the global warming to “well below” 2°C above pre-industrial levels and, if possible, “pursue” efforts to limit warming at 1.5°C. Therefore, it is imperative to understand future climate change and their spatial heterogeneity at 1.5°C and 2°C warming for developing  strategies for renewables. This research examines the distribution and variability of India's solar resources by utilising state-of-art global climate models from Coupled Model Intercomparison Project phase 6 (CMIP6) and CMIP6 - NASA Earth eXchange Global Daily Downscaled Projections (NEX-GDDP). The analysis of global mean temperature changes reveals that the 2030s and 2040s will be the decade when majority CMIP6 models reach 1.5°C and 2°C warming under SSP2-4.5 (intermediate emission pathways) and SSP5-8.5 (high emission) scenarios respectively with respect to  pre-industrial period (1850–1900).We find that under the intermediate (high) emission scenarios, the annual mean surface solar radiation over the Indian landmass will decrease by -8±3 Wm-2 (-5±2 Wm-2) relative to the baseline period (1985-2014) at 1.5°C global warming. An additional 0.5°C of warming (at a global warming level of 2°C) results in a comparatively smaller decline in surface solar irradiance with respect to baseline under both scenarios. At 1.5°C and 2.0°C global warming, most regions are anticipated to experience an increase in surface irradiance under the SSP5-8.5 scenario, as compared to SSP2-4.5. The magnitude and direction of change of aerosols, clouds and associated meteorological parameters needs to be explored further. This research will contribute to crucial planning and decision-making processes concerning India and other nations with similar interests.

Выполнена оценка возможных изменений среднегодовой приповерхностной температуры воздуха (ПТВ) в Дальневосточном регионе, включающем территорию и окраинные моря России, а также северо-западную часть Тихого океана, до 2099 г., для чего используются осредненные по ансамблю данные 33 моделей проекта CMIP6 (Coupled Model Intercomparison Project Phase 6), полученные в рамках четырех сценариев, отвечающих разным уровням антропогенного радиационного форсинга (слабого, умеренного и значительного). Анализируются различия между осредненными за 30-летние периоды аномалиями ПТВ. Для верификации модельных результатов проанализировано потепление, произошедшее в регионе с 1940–1969 до 1994–2023 гг., для чего использованы данные реанализа ERA5 и эксперимента Historical CMIP6. По обоим видам данных средняя ПТВ в регионе выросла на 1.1 °С: с 1940–1969 к 1994–2023 гг.; это сходство обосновывает оценки будущих изменений ПТВ по моделям CMIP6. Все сценарии SSP (Shared Socio-economic Pathways) будущего радиационного форсинга показывают приблизительно одинаковое повышение ПТВ с 1994–2023 по 2024–2053 гг., оно составляет в среднем по региону 1.2–1.5 °С. К 2070–2099 гг. средняя ПТВ в рассматриваемом регионе возрастет соответственно темпу эмиссии парниковых газов – на 1.7, 2.7, 3.8 и 4.8 °С. Как показывают данные реанализа ERA5, от 1940–1969 к 1994–2023 гг. увеличение ПТВ над морскими акваториями региона происходило весьма неравномерно: наибольшие темпы наблюдались в северной части Охотского моря (до 2 °С и более) и в прибрежных районах северо-западной части Берингова моря (до 1.0–1.2 °С). Увеличение ПТВ ослабевало в направлении с северо-запада на юго-восток, т.е. с удалением от суши, и составило 0.2–0.6 °С в северо-западной части Тихого океана. Картина потепления над морскими акваториями по данным CMIP6 выражена сильнее, чем по данным реанализа ERA5, но при этом качественно им соответствует. An assessment of possible changes in the annual mean surface air temperature (SAT) in the Far East Region (35°–65° N, 130°–180° E) is made from the present to 2099, using ensemble-averaged data from 33 CMIP6 (Coupled Model Intercomparison Project Phase 6) models obtained within the framework of four scenarios corresponding to the weak, moderate, or significant anthropogenic radiative forcing resulting from СО2 emissions. To elucidate long-term climate change, SAT averaged for 30-year periods, namely, 1994–2023, 2024–2053 and 2070–2099 are analyzed. To verify the model results, the warming that occurred in the region from the mid-20th century (1940–1969) to the early 21st century (1994–2023) is analyzed, using ERA5 data with the fine spatial resolution of 0.25°, and CMIP6 data with the coarser resolution, mostly 1.0°–2.0°. According to both data types, the regional SAT increased, on average by 1.1 °C from 1940–1969 to 1994–2023, justifying the use of forecast estimates based on the CMIP6 models in this work. All scenarios of possible radiative forcing show the similar SAT increase from the 1994–2023 to 2024–2053, on average 1.2–1.5 °C. On the contrary, by the 2070–2099, the regional SAT will increase in accordance with the emission rates on average by 1.7, 2.7, 3.8 and 4.8 °C, respectively. As for the Russian Far East land area, ERA5 and CMIP6 show similar spatial warming patterns, with the warming, on average, of 1.2 °C from 1940–1969 to 1994–2023, i.e. higher than that for the entire considered region including marine areas. From 1940–1969 to 1994–2023 negative annual mean SAT changed to positive one in some areas of the Primorsky, Khabarovsky and Kamchatksky provinces, implying the permafrost melting. According to the CMIP6 models, the land warming of 2.0–2.1 °C, 3.0–3.5 °C, 4.7–5.3 °C, and 6.1–6.6 °C is expected by the end of the 21st century for the scenarios with the different levels of radiative forcing. As shown by the ERA5 data, the SAT increase from 1940–1969 to 1994–2023 was very uneven for the marine areas: the highest rates were observed in the northern Okhotsk Sea (up to 2 °C and more) and in the coastal northwestern Bering Sea (up to 1.0–1.2 °C), which can be explained by the ice cover decrease. The SAT increase weakened in the direction from the northwest to southeast, i.e. with the distance from the land, and amounted to only 0.2–0.6 °C in the northwestern Pacific, which can be attributed to the effect of Pacific Decadal Oscillation (PDO). The coastal Okhotsk Sea off the Sakhalin Island is the only area where SAT decreased by 0.2–0.6 °C from 1940–1969 to 1994–2023, which probably can be attributed to the changes in the East Sakhalin Current transporting Amur River water southward along the coast but this suggestion should be verified. The warming pattern over the marine areas according to CMIP6 data qualitatively corresponds to that one based on ERA5 data, keeping in mind the lower resolution of the modeled data. The warming in the Northwest Pacific from the modeled data exceeds that one from ERA5, which can be explained by elimination of the PDO effects when averaging CMIP6 multi-model data.

ABSTRACTUsing daily output from 29 climate models provided by the Coupled Model Intercomparison Project Phase 5, we project signals in 12 extreme temperature indices and 12 extreme precipitation indices relative to 1986–2005 over China associated with a 2 °C global warming above pre‐industrial levels under representative concentration pathways 4.5 (RCP4.5) and 8.5 (RCP8.5). The model output reflects the following projected changes: (1) It is robust and statistically significant that warm extremes are more frequent, more persistent, and more intense than those during the baseline period of 1986–2005, and local signals emerge from natural internal variability over most of China. In particular, southern China faces severe heat stress in summer based on warm extreme indices. (2) It is robust and statistically significant that there are fewer cold extremes in China. Most models show no significant changes in the longest duration and intensity of most cold extremes in southern China and northwest China. (3) The multi‐model median shows that more frequent and more intense wet extremes that deliver greater amounts of extreme precipitation occur in China, with a regional mean increase of 16.7–42.8 mm (8–42%) in total amount of annual wet extremes, but these changes are not significant and do not exceed natural internal variability over most of the country. Spatially, the Tibetan Plateau and northeast China have robust and significant changes in part of precipitation extremes, and some changes begin to emerge from natural internal variability at local scale. There are benefits to limiting global warming for China, including less frequent and less persistent warm extremes when comparing 1.5 °C with 2 °C of global warming and a later occurrence of significant changes in climate extremes when compared an intermediate mitigation scenario RCP4.5 with a high emission scenario RCP8.5.

Performance of six models in the Coupled Model Intercomparison Project phase 5 (CMIP5) and their new versions in CMIP phase 6 (CMIP6) in representing the climatological (1976–2005) precipitation extremes over China were evaluated based on five precipitation indices. Improvements are found in CMIP6 models in simulating the climatology of all five indices, in which GFDL‐CM4 and GFDL‐ESM4 show significant improvement. Dry biases over South China (SC) are reduced in five CMIP6 models (BCC‐CSM, CanESM, GFDL‐CM, GFDL‐ESM, and IPSL‐CM), with the largest decreased root‐mean‐square error (RMSE) of 59.2% in GFDL‐CM. The reduced dry biases in CMIP6 can be attributed to more moisture transported into SC from the southern boundary and dynamic processes of the atmosphere except in BCC‐CSM, where the increased evaporation dominates. Additionally, increased heavy precipitation events (>20 mm·day−1) are produced over SC in CMIP6 models. Wet biases over West China (WC) are also reduced. with the largest reduced RMSE of 46.8% in GFDL‐CM, which are related to the reduced precipitation frequency (more than 40%) and weakened precipitation intensity. In addition, the CMIP6 models show a higher skill in simulating the frequency distribution of daily precipitation intensity. More heavy precipitation over SC and Northeast China, and fewer weak precipitation (<20 mm·day−1) over WC can be reasonably reproduced. Although the CMIP6 models have obviously improved in simulating total precipitation on wet days, wet days (WD), simple daily intensity index (SDII), and extreme precipitation amount (R95T), the bias still exists in simulating consecutive dry days (CDD).

This study evaluates the ability of 23 climate models from phase 6 of the Coupled Model Intercomparison Project (CMIP6) in simulating extreme climate events over China. The multimodel ensemble (MME) performs better than most individual models in reproducing the climatological mean distribution of all extreme indices. The MME can reproduce well the climatological mean distributions of five extreme climate indices over China, including annual total precipitation (PTOT), maximum consecutive 5‐day precipitation (RX5), simple daily intensity (SDII), maximum daily maximum temperature (TXX), and minimum daily minimum temperature (TNN), with Taylor skill scores exceeding 0.7. SDII and TXX are the most skilful precipitation and temperature extreme indices simulated by the MME, respectively. The MME has relatively lower skill in simulating the climatological mean distribution of warm days (TX90P) and cold nights (TN10P) over China. Future projections of these extreme climate indices by the end of the 21st century are explored with the MME under the SSP1‐2.6, SSP2‐4.5, and SSP5‐8.5 scenarios. The PTOT and RX5 in northwestern China are all projected to increase by more than 30% under SSP5‐8.5. R20 is projected to increase by 4–5 days over southeastern China under SSP5‐8.5. There are fewer (more) consecutive dry days over north China (south China), with a change of 5 days under SSP5‐8.5. The extreme temperature indices, including TX90P, TXX, and TNN, all increase with time and higher SSP scenarios. The three indices increase by 40–55%, 4–6°C, and 4–7°C under SSP5‐8.5 over east China, respectively. The TN10P decreases by more than 6% over east China. The changes in these extreme indices under SSP1‐2.6 and SSP2‐4.5 are similar to those under SSP5‐8.5 but with a smaller magnitude. Large uncertainties still exist in the future projections, especially under the high SSP scenarios.

Assessment of climate extremes in future projections downscaled by multiple statistical downscaling methods over Pakistan

The Intergovernmental Panel on Climate Change (IPCC) suggests limiting global warming to 1.5 °C compared to 2 °C would avoid dangerous impacts of anthropogenic climate change and ensure a more sustainable society. As the vulnerability to global warming is regionally dependent, this study assesses the effects of 0.5 °C less global warming on climate extremes in the United States. Eight climate extreme indices are calculated based on Coupled Model Intercomparison Project—phase 5 (CMIP5), and North American—Coordinated Regional Climate Downscaling Experiments (NA-CORDEX) with and without bias correction. We evaluate the projected changes in temperature and precipitation extremes, and examine their differences between the 1.5 and 2 °C warming targets. Under a warming climate, both CMIP5 and NA-CORDEX show intensified heat extremes and reduced cold extremes across the country, intensified and more frequent heavy precipitation in large areas of the North, prolonged dry spells in some regions of the West, South, and Midwest, and more frequent drought events in the West. Results suggest that the 0.5 °C less global warming would avoid the intensification of climate extremes by 32–46% (35–42%) for heat extremes intensity (frequency) across the country and, by 23–41% for heavy precipitation intensity in the North, South, and Southeast. The changes in annual heavy precipitation intensity are mainly contributed by winter and spring. However, impacts of the limited warming on the frequency of heavy precipitation, dry spell, and drought frequency are only evident in a few regions. Although uncertainties are found among the climate models and emission scenarios, our results highlight the benefits of limiting warming at 1.5 °C in order to reduce the risks of climate extremes associated with global warming.

As the major renewable energy, wind can greatly reduce carbon emissions. Following the “carbon neutral” strategy, wind power could help to achieve the realization of energy transformation and green development. Based on ERA5 reanalysis data and the multi-ensemble historical and scenario simulations of the Coupled Model Intercomparison Project Phase 6 (CMIP6), a variety of statistical analyses are used to evaluate the performance of CMIP6 simulating the wind speed in China. The conclusions are as follows: spatial patterns of the nine CMIP6 models are similar with ERA5, but BCC-CSM2-MR and MRI-ESM2-0 highly overestimate the wind speed in northwest China. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM behave better than the other six CMIP6 models in four specific regions are chosen for detailed study. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM tend to simulate a larger wind speed than ERA5 except the yearly averaged wind speed in region II and region IV. CESM2-WACCM and NorESM2-MM simulate a large monthly mean wind speed, but the value is relatively close with ERA5 in the summer. HadGEM3-GC31-MM overestimates wind speed in region I and region II from April to October, but gets closer with ERA during winter. CESM2-WACCM, NorESM2-MM, and HadGEM3-GC31-MM simulate an increasing trend in Tibetan Plateau and Xinjiang in the next 100 years, while NorESM2-MM projects rising wind speed in the eastern part of Inner Mongolia, and HadGEM3-GC31-MM simulates increasing wind speed in the northeast and central China. The future wind speed in three models is projected to decline in region I, and the value of HadGEM3-GC31-MM is much larger. In region II, wind speed simulated by three models is projected to decrease, but the wind speed from HadGEM3-GC31-MM in region III and modeled wind speed in region IV from NorESM2-MM would climb with the slope equal to 0.0001 and 0.0012, respectively. This study indicates that the CMIP6 models have certain limitations to perform realistic wind changes, but CMIP6 could provide available reference for the projection of wind in specific areas.

A comparison assessment of model capabilities in simulating precipitation extremes across China was first implemented by using 30 models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) and using 36 CMIP6 models. The results indicate that the multi‐model median ensembles (MME) of both the CMIP5 and CMIP6 models can reasonably reproduce the climate means for the period from 1986 to 2005, and the biases are lower in most CMIP6 models compared to the CMIP5 models, especially over southern China. To provide further comparisons, 14 CMIP6 models are selected and compared with their predecessors in CMIP5. The results show that the CMIP6 models generally exhibit superior skill in simulating the extreme precipitation indices over China. The model spreads for most of the extreme indices in the CMIP6 version are also smaller. Additionally, the MMEs of the two CMIPs outperform individual models. However, some CMIP6 models also exhibit weaker skill levels in simulating some particular indices compared with those in CMIP5, which merits further investigation. The results from seven reanalyses further show large uncertainties for these indices; therefore, care should be taken in comparison with reanalyses. For future changes in precipitation extremes, total wet day precipitation (PRCPTOT), maximum 5‐day precipitation (RX5day) and very heavy precipitation days (R20mm) are projected to clearly increase across China over the coming century under the shared socioeconomic pathway (SSP) 2‐4.5 and SSP5‐8.5 scenarios. However, the dry condition index of CDD exhibits a decreasing tendency in the future, which implies that the dry conditions induced by precipitation anomalies will be mitigated. However, large uncertainties are still observed for future changes, which are primarily sourced from inter‐model and scenario variabilities, especially for the projected changes at the end of the 21st century.

Precipitation variability has great economic, social, and environmental impacts across the globe, and in particular in China. This paper evaluates the historical precipitation variability based on 20 general circulation models from the Coupled Model Intercomparison Project Phase 5 (CMIP5) archive over the 20th century relative to two observational data sets and quantifies CMIP5 improvements over CMIP3. Multimodel ensemble means and individual models are assessed. Three future emission scenarios are used (representative concentration pathways (RCP) 8.5, RCP 4.5, and RCP 2.6), and 21st century CMIP5 estimates are put into context based on the 20th century biases. We find that CMIP5 models can reproduce the spatial pattern of precipitation over China during the 20th century, which represents an improvement over CMIP3. However, the models overestimate the magnitude of seasonal and annual precipitation in most regions of China, especially along the eastern edge of the Tibetan Plateau, and underestimate summer precipitation over southeastern China. For China as a whole, CMIP5's overestimation of annual precipitation is greater than CMIP3, which can be traced back to a greater underestimation of summer precipitation in CMIP3. There is a large spread among individual models, with the greatest uncertainties in simulating summer precipitation. Trends and correlations also suggest a better agreement of CMIP5 with observations than CMIP3. Throughout the 20th century, both the observations and models show an increasing trend in precipitation over parts of northwestern China and a decreasing trend over the Tibetan Plateau. There is poor agreement in precipitation trends over the southeast and northeast regions. In general, multimodel means cannot capture the amplitude of observed multidecadal precipitation variability. In the 21st century, precipitation is generally projected to increase across all of China under all three scenarios. RCP 8.5 exhibits the largest significant trend at a rate of +1.5 mm/yr, corresponding to 16% precipitation increase by the end of the century. The RCP 2.6 scenario shows the smallest increases, at +0.5 mm/yr (6%) by 2100. The greatest increases are projected to occur over the Tibetan Plateau and eastern China in summer, suggesting an altered monsoonal circulation in the future. However, due to the uncertainties in CMIP5, future precipitation projections should be interpreted with caution.

This study investigated the recent and future spatiotemporal changes in summer hot days (HDs) and heat waves (HWs) and their relationships with large‐scale atmospheric circulations over Northeast China (NEC). We used daily maximum temperature data from 109 meteorological stations for the period 1961–2013 across NEC and the output of 11 general circulation models from Coupled Model Intercomparison Project Phase 5 under RCP4.5 and RCP8.5 emission scenarios. We found that the frequencies and intensities of summer HDs and HWs overall increased in the last 53 years. The increases mainly occurred in the northeastern Inner Mongolia, the north part of the Heilongjiang Province, and southern Changbai Mountains. The during time periods between the start and end dates of HDs were lengthened from 1961 to 2013 in the eastern Inner Mongolia, the northeastern Heilongjiang Province, and the southern Liaoning Province. Our results showed that HDs and HWs exhibited significant increasing trends from 2006 to 2100 under RCP4.5 and RCP8.5 emission scenarios. Generally, more frequent and intense summer HDs and HWs would happen in NEC over the 21st century. Especially, the northwest of NEC and the south and middle of Da Hinggan Mountains would experience the most frequent and intense HDs and HWs. The variations in atmospheric circulation in summers with abnormal frequency of HDs and HWs were driven by Asian Zonal Circulation and Western Pacific Subtropical High simultaneously. An anticyclonic circulation anomaly prevailing over the west of NEC and the higher summer surface sea temperature in Japan Sea resulted in more frequent HDs and HWs across NEC. This study may help to understand future changes in HDs and HWs and provide references for water resources management and policy‐making in agriculture and forestry.