Warm Spells Research Articles

Future projections of extreme temperature events in Southwest China using nine models in CMIP6

Southwest China, which is close to the Qinghai-Tibet Plateau, presents complex topography. As a result of the combined influence of the South Asian monsoon, East Asian monsoon, and plateau monsoon, climate in this region is unique. Since Southwest China is one of the areas where extreme weather events occur more frequently, this region is sensitive and vulnerable to climate change. In the present research, daily temperature from 1969 to 2020 recorded at 93 weather stations in Southwest China, and data from nine models for the period 1995 to 2040 were used in CMIP6 (Coupled Model Intercomparison Project Phase 6) to calculate 17 ETIs (Extreme Temperature Indices). Furthermore, we analyzed and compared the annual change rate, temporal and spatial change trend, and mean change of extreme temperature events in Southwest China and four subzones during historical period and under SSP2-4.5 scenario for the next 20 years. The results showed: 1) The 8 ECTIs (Extreme Cold Temperature Indices) and the 8 EWTIs (Extreme Warm Temperature Indices) in 1969–2020 were corroborated. These results indicated a warming trend. Also, DTR (Diurnal temperature range) showed a decreasing trend, and different degrees of warming were observed in the four subzones. 2) From 2021 to 2040 and under the SSP2-4.5 scenario, the annual rates of change for 17 ETIs in Southwest China showed that extreme cold events will continue to decrease. On the other hand, extreme warm events will continue to increase. 3) Under the SSP2-4.5 scenario in the next 20 years, CSDI (cold spell duration indicator) will decrease, while WSDI (warm spell duration indicator), TMINmean (average daily minimum temperature), and TMAXmean (average daily maximum temperature) will increase. Moreover, the decrease in amplitude of CSDI was smaller than the increase in amplitude of WSDI. Also, the increase in amplitude of TMINmean was slightly smaller than that of TMAXmean. The projected WSDI, TMINmean, and TMAXmean obtained with the preferred three models and MEM-9 (nine-Model Ensemble Mean) showed an overall growing trend with respect to space; however, the increased range fluctuated in different regions. 4) In 2021–2040, mean values of 4 ETIs in different subzones indicated that the lowest TMINmean and TMAXmean were observed in the ZP (Zoigê Plateau), the highest in the YGP (Yunnan-Guizhou Plateau), and intermediate higher in the SB (Sichuan Basin). Compared with the 4 ETI mean values corresponding to 1969–2020, the persistence and average state of extreme cold and warm events in different subzones showed that future change trends depend on altitude.

Changes in temperature and precipitation extremes in the arid regions of China during 1960–2016

Extreme climate events have a greater impact on natural and human systems than average climate. The spatial and temporal variation of 16 temperature and nine precipitation extremal indices was investigated using the daily maximum and minimum surface air temperature and precipitation records from 113 meteorological stations in China’s arid regions from 1960 to 2016. The warmth indices [warm spell duration (WSDI); numbers of warm nights, warm days, tropical nights (TR), and summer days (SU)] increased significantly. On the contrary, the cold indices [numbers of frost days (FD), ice days (ID), cool days, and cool nights; cold spell duration (CSDI)] decreased significantly. The number of FD decreased fastest (−3.61 days/decade), whereas the growing season length (GSL) increased fastest (3.17 days/decade). The trend was strongest for diurnal temperature range (DTR) (trend rate = −7.29, P < 0.001) and minimum night temperature (trend rate = 7.70, P < 0.001). The cold extreme temperature events increased with increasing latitude, but the warm extreme temperature events decreased. Compared with temperature indices, the precipitation indices exhibited much weaker changes and less spatial continuity. Overall, changes in precipitation extremes present wet trends, although most of the changes are insignificant. The regionally averaged total annual precipitation for wet days increased by 4.78 mm per decade, and extreme precipitation events have become more intense and frequent during the study period. The spatial variability of extreme precipitation in the region was primarily influenced by longitude. Furthermore, the climate experienced a warm-wet abrupt climate change during 1990s.

Potential changes in temperature extreme events under global warming at 1.5 °C and 2 °C over Côte d’Ivoire *

This work investigated the impact of 1.5 °C and 2 °C of global warming levels (GWLs) above pre-industrial levels on annual and seasonal mean changes in temperature extremes over Côte d’Ivoire and its different climatic zones. We used the multi-model Coordinated Regional Climate Downscaling Experiment for Africa of 25 regional climate models under the RCP8.5 scenario. The changes in temperature are quantified relative to the period 1971–2000 based on five Expert Team on Climate Change Detection and Indices indexes namely for warm spells, hot nights, hot days, cold nights and cold days. We show that a global warming of 1.5 °C and 2 °C will lead to an increase in the frequency of warm days and warm nights and a decrease in the occurrence of cold days and cold nights across Côte d’Ivoire in all climatic zones and seasons. More than 80% of the model ensemble members project this change at both GWLs. Moreover, the assessment of differences in GWLs highlights that the difference between the 1.5 °C and 2 °C thresholds may intensify the changes over all the country, climatic zones and seasons. Therefore this 0.5 °C difference in global warming is likely to impact upon energy demand and the agricultural system throughout the country and over all of the seasons. This study provides climate information for decision makers related to sectors such as agriculture, energy in their adaptation strategies.

Historical trends in climate indices relevant to surface air temperature and precipitation in Japan for recent 120 years

AbstractGeographical distributions of trends in climate indices with statistical significance in Japan are investigated at each of the 51 observation stations for the recent 120 years. We employed a comprehensive set of the 70 climate indices on an annual timescale to detect climate changes and to facilitate climate information for a variety of sectors, which are developed by expert team on sector‐specific indices in the World Meteorological Organization. The rising trends have regional differences such as the annual mean of daily maximum and minimum surface air temperatures in the Kanto‐Koushin region. The statistically significant increasing trends in three daily heavy precipitation‐relevant climate indices, R95p, R99p, and RX1day, are seen in Hokuriku, Shikoku, the western part of Northern and Southern Kyusyu regions, and isolated stations in the other regions. Rotational empirical orthogonal function analysis of R99p revealed different features of statistically significant modes suggesting the trends are attributed to changes in different atmospheric phenomena as well as those of warm spell duration index. The number of the stations with statistically significant trends varies with an index. Indies relevant to daily minimum surface air temperatures have statistically significant trends at most of the 51 stations. Consecutive dry days are the most sensitive to climate changes for the 120 years among the indices relevant to precipitation in this analysis. The indices relevant to heavy precipitation are not always been most sensitive to climate change detections in Japan. The geographical distributions of the percentage of the number of indices with statistically significant changes to the total number of the stations provide information about stations sensitive to climate changes. The highest sensitivity is seen at Kumamoto. These results directly provide detection of climate changes and show the potentials of which index is sensitive to ongoing climate changes from the viewpoint of geographical distributions.

Dynamical downscaling of temperature extremes over China using the WRF model driven by different lateral boundary conditions

Extreme temperature events have considerable impact on society and natural ecosystems, which receive less attention. This study examined the impact of lateral boundary conditions (LBCs) on the downscaling simulations of the Weather Research and Forecasting (WRF) model for temperature extremes over China for the period 1981–2010. The driving data comprised two reanalysis datasets from two different institutes, ERA-interim and NECP-R2. Twelve extreme temperature indices were calculated from the WRF simulations and compared with both reanalysis products and observations. The comparison includes their climate mean and interannual variation. Results indicated that WRF can skillfully reproduce the spatial distributions of extreme temperature indices, despite some biases in certain regions. The large biases in the warmest and coldest days, warm spell duration and cool days over eastern China from reanalysis data were reduced significantly. The simulation driven by ERA-interim outperforms NECP-R2. In terms of interannual variation, the WRF model was able to capture the observational trends except the number of days with warm spell and days with moderate warm. Furthermore, the accuracy of WRF in simulating interannual variation was consistent with that of the driving data. The WRF simulation driven by ERA-interim appeared best in terms of bias frequency distributions for most indices. Overall, the results revealed that WRF dynamical downscaling of temperature extremes is sensitive to selection of LBCs, and that performance could be improved by adopting driving data such as ERA-interim.

Spatiotemporal Changes in Mean and Extreme Climate: Farmers’ Perception and Its Agricultural Implications in Awash River Basin, Ethiopia

The increase in the intensity and frequency of climate extremes threatens socioeconomic development. This study examines variability of mean and extreme climate, farmers’ perception of the changes, and impacts in the Awash River Basin. Daily rainfall and temperature data were used to analyze 23 extreme climate indices. The Mann–Kendall test was used to assess the magnitude and significance of the changes. Results show an increase in minimum (0.019–0.055 °C/year) and maximum temperatures (0.049–0.09 °C/year), while total rainfall is on a downward trend (from −3.84 mm/year to −10.26 mm/year). Warm extreme temperature indicators, including warmest day (TXx), warmest night (TNx), warm day (TX90p), warm night (TN90p), and warm spell duration indicator (WSDI), show a significant increasing trend (p < 0.05). Nevertheless, except the tepid–cool humid agroecology zone, cold extreme temperature indicators in cool days (TN10p), cool nights (TX10p), and cold spell duration (CSDI) are declining. Extreme precipitation indices, including maximum 1-day precipitation amount (RX1day), count of days when precipitation ≥10 mm (R10 mm), maximum 5-day precipitation amount (RX5day), count of days when precipitation ≥20 mm (R20mm), very wet days (R95p), extreme wet days (R99p), and total precipitation (PRCPTOT), show a decreasing trend. The perception of most farmers’ on climate change and climate extremes agreed with climate records. The major impacts perceived and asserted over all agroecologies are food price inflation, crop productivity decline, crop pests and diseases spread, livestock disease increase, and the emergence of pests and weeds. The increasing trend in extreme warm temperatures, decreasing trend in the cold extreme, and declining trend in precipitation indicators affected agricultural productivity and farmers whose livelihood depends on rainfed agriculture. This agroecology-specific study provides critical information to policymakers, decision makers, and farmers about the potential impacts of climate change and extreme events, leading to the development of agroecology-based adaptation measures.

Holocene wildfire regimes in western Siberia: interaction between peatland moisture conditions and the composition of plant functional types

Abstract. Wildfire is the most common disturbance type in boreal forests and can trigger significant changes in forest composition. Waterlogging in peatlands determines the degree of tree cover and the depth of the burnt horizon associated with wildfires. However, interactions between peatland moisture, vegetation composition and flammability, and fire regime in forest and forested peatland in Eurasia remain largely unexplored, despite their huge extent in boreal regions. To address this knowledge gap, we reconstructed the Holocene fire regime, vegetation composition, and peatland hydrology at two sites located in predominantly light taiga (Pinus sylvestris Betula) with interspersed dark taiga communities (Pinus sibirica, Picea obovata, Abies sibirica) in western Siberia in the Tomsk Oblast, Russia. We found marked shifts in past water levels over the Holocene. The probability of fire occurrence and the intensification of fire frequency and severity increased at times of low water table (drier conditions), enhanced fuel dryness, and an intermediate dark-to-light taiga ratio. High water level, and thus wet peat surface conditions, prevented fires from spreading on peatland and surrounding forests. Deciduous trees (i.e. Betula) and Sphagnum were more abundant under wetter peatland conditions, and conifers and denser forests were more prevalent under drier peatland conditions. On a Holocene scale, severe fires were recorded between 7.5 and 4.5 ka with an increased proportion of dark taiga and fire avoiders (Pinus sibirica at Rybnaya and Abies sibirica at Ulukh–Chayakh) in a predominantly light taiga and fire-resister community characterised by Pinus sylvestris and lower local water level. Severe fires also occurred over the last 1.5 kyr and were associated with a declining abundance of dark taiga and fire avoiders, an expansion of fire invaders (Betula), and fluctuating water tables. These findings suggest that frequent, high-severity fires can lead to compositional and structural changes in forests when trees fail to reach reproductive maturity between fire events or where extensive forest gaps limit seed dispersal. This study also shows prolonged periods of synchronous fire activity across the sites, particularly during the early to mid-Holocene, suggesting a regional imprint of centennial- to millennial-scale Holocene climate variability on wildfire activity. Humans may have affected vegetation and fire from the Neolithic; however, increasing human presence in the region, particularly at the Ulukh–Chayakh Mire over the last 4 centuries, drastically enhanced ignitions compared to natural background levels. Frequent warm and dry spells predicted by climate change scenarios for Siberia in the future will enhance peatland drying and may convey a competitive advantage to conifer taxa. However, dry conditions will probably exacerbate the frequency and severity of wildfire, disrupt conifers' successional pathway, and accelerate shifts towards deciduous broadleaf tree cover. Furthermore, climate–disturbance–fire feedbacks will accelerate changes in the carbon balance of boreal peatlands and affect their overall future resilience to climate change.

Spatio-Temporal Trends of Precipitation and Temperature Extremes across the North-East Region of Côte d’Ivoire over the Period 1981–2020

The northeast region of Côte d’Ivoire, where agriculture is the main economic activity, is potentially vulnerable to extreme climatic conditions. This study aims to make a comprehensive spatio-temporal analysis of trends in extreme indices related to precipitation and temperature for the Zanzan region of Côte d’Ivoire over the period of 1981–2020. The statistical significance of the calculated trends was assessed using the non-parametric Mann–Kendall test, while Sen’s slope estimation was used to define the amount of change. For extreme precipitations, the results showed a decreasing trend in annual total precipitations estimated at 112.37 mm and in daily precipitations intensity indices. Furthermore, the consecutive dry days’ index showed an increasing trend estimated at 18.67 days. Unlike the trends in precipitation extremes, which showed statistically non-significant trends, the trends in temperature extremes were mostly significant over the entire study area. The cold spells indices all show decreasing trends, while the warm spells show increasing trends. Drawing inferences from the results, it becomes clear that the study area may be threatened by food insecurity and water scarcity. The results are aimed to support climate adaptation efforts and policy intervention in the region.

Long-term trends in daily extreme air temperature indices in Ireland from 1885 to 2018

Long-term instrumental series are crucial to analyse extreme air temperature indices and to examine modern climate warming within a historical context. This research provides the first assessment in Ireland of the frequency, duration, intensity and geographical distribution of the daily extreme air temperature indices recommended by the ETCCDI (Expert Team on Climate Change Detection and Indices) and based on long-term rescued, quality-controlled and homogenised data. Prior analysis of air temperature indices in Ireland had focused on the period since the second half of the 20th century, where digital data were readily available. Earlier estimations of long-term air temperature trends in Ireland focused on non-homogenised data from a small network of stations. For this research, 11 long-term (1885–2018) maximum and minimum air temperature series were analysed to assess seasonal and annual trends and construct extreme air temperature indices in Ireland. The non-parametric Mann-Kendall test was employed to test the statistical significance of the maximum and minimum air temperatures and indices trends (p < 0.05). Significant increasing trends were assessed in the seasonal and annual maximum and minimum air temperature series in Ireland, with greater increases in the spring and autumn seasons. Significant increasing trends were determined in the warm days (+6.8 days), warm nights (+7.5 nights), warm spell duration index (+3.9 days), growing season length (+22 days), coldest night (+2.7 °C) and coldest day (+1.5 °C) in the period 1885–2018 in Ireland. In the same period, significant decreasing trends were identified in the cold days (−9.3 days), cold nights (−7 nights), frost days (−13.7 days), cold spell duration index (−6.9 days) and diurnal air temperature range (−0.1 °C) in Ireland. The results follow global patterns presented in the Six Assessment Report of the Intergovernmental Panel on Climate Change. The findings of this research will be crucial to assist stakeholders and policymakers in defining climate action, adaptation and mitigation plans in response to shifting trends in air temperature extremes.

High-resolution Projection Dataset of Agroclimatic Indicators over Central Asia

To understand the potential impacts of projected climate change on the vulnerable agriculture in Central Asia (CA), six agroclimatic indicators are calculated based on the 9-km-resolution dynamical downscaled results of three different global climate models from Phase 5 of the Coupled Model Intercomparison Project (CMIP5), and their changes in the near-term future (2031–50) are assessed relative to the reference period (1986–2005). The quantile mapping (QM) method is applied to correct the model data before calculating the indicators. Results show the QM method largely reduces the biases in all the indicators. Growing season length (GSL, day), summer days (SU, day), warm spell duration index (WSDI, day), and tropical nights (TR, day) are projected to significantly increase over CA, and frost days (FD, day) are projected to decrease. However, changes in biologically effective degree days (BEDD, °C) are spatially heterogeneous. The high-resolution projection dataset of agroclimatic indicators over CA can serve as a scientific basis for assessing the future risks to local agriculture from climate change and will be beneficial in planning adaption and mitigation actions for food security in this region.

Multi-Model Ensemble Projections of Winter Extreme Temperature Events on the Chinese Mainland.

Based on the downscaling data of multi-model ensembles of 26 global climate models (GCMs) from the Coupled Model Intercomparison Project Phase 6, this study calculated the extreme climate indices defined by the Expert Team on Climate Change Detection and Indices and the warm winter extreme grade indices to explore winter climate response on the Chinese mainland under different shared socioeconomic pathways (SSPs) and representative concentration pathways. The results showed that the temperature in winter increased overall, with the highest temperature increases of 0.31 °C/10a (Celsius per decade) (SSP245) and 0.51 °C/10a (SSP585) and the lowest temperature increases of 0.30 °C/10a (SSP245) and 0.49 °C/10a (SSP585). Warm-related extreme weather events such as warm days and warm spell duration indices showed an increasing trend, whereas cold-related extreme weather events such as cold spell duration indices, cold nights, ice days, and frost days showed a decreasing trend. On the regional scale, the maximum temperature increased by more than 2 °C/10a (SSP245) and 0.4 °C/10a (SSP585), except in South China, and the minimum temperature increased faster in Qinghai-Tibet and Northeast China compared to elsewhere on the Chinese mainland. Compared with that under SSP585, the frequency and intensity of warm winters in the latter half of the 21st century were lower under SSP245. At the end of the 21st century, under the SSP245 scenario, warm winter frequency in most regions will be reduced to below 60%, but under the SSP585 scenario, it will be more than 80%. Population exposures all showed a downward trend, mainly due to the reduction of warm winter events and the decline of the population under the SSP245 and SSP585 scenarios, respectively. If the greenhouse gas emission path is controlled in the SSP245 scenario, the population exposure risk in warm winters can be decreased by 25.87%. This study observed a consistent warming trend on the Chinese mainland under all SSPs in the 21st century and found that stricter emission reduction policies can effectively decrease the population exposure to warm winters.

Summertime changes in climate extremes over the peripheral Arctic regions after a sudden sea ice retreat

Abstract. The retreat of Arctic sea ice is frequently considered to be a possible driver of changes in climate extremes in the Arctic and possibly down to mid-latitudes. However, it remains unclear how the atmosphere will respond to a near-total retreat of summer Arctic sea ice, a reality that might occur in the foreseeable future. This study explores this question by conducting sensitivity experiments with two global coupled climate models run at two different horizontal resolutions to investigate the change in temperature and precipitation extremes during summer over peripheral Arctic regions following a sudden reduction in summer Arctic sea ice cover. An increase in frequency and persistence of maximum surface air temperature is found in all peripheral Arctic regions during the summer, when sea ice loss occurs. For each 1×106 km2 of Arctic sea ice extent reduction, the absolute frequency of days exceeding the surface air temperature of the climatological 90th percentile increases by ∼ 4 % over the Svalbard area, and the duration of warm spells increases by ∼ 1 d per month over the same region. Furthermore, we find that the 10th percentile of surface daily air temperature increases more than the 90th percentile, leading to a weakened diurnal cycle of surface air temperature. Finally, an increase in extreme precipitation, which is less robust than the increase in extreme temperatures, is found in all regions in summer. These findings suggest that a sudden retreat of summer Arctic sea ice clearly impacts the extremes in maximum surface air temperature and precipitation over the peripheral Arctic regions with the largest influence over inhabited islands such as Svalbard or northern Canada. Nonetheless, even with a large sea ice reduction in regions close to the North Pole, the local precipitation response is relatively small compared to internal climate variability.

Projection of Extreme Temperature Events over the Mediterranean and Sahara Using Bias-Corrected CMIP6 Models

Climate change continues to increase the intensity, frequency and impacts of weather and climate extremes. This work uses bias-adjusted Coupled Model Intercomparison Project Phase six (CMIP6) model datasets to investigate the future changes in temperature extremes over Mediterranean (MED) and Sahara (SAH) regions. The mid- (2041–2070) and far-future (2071–2100) are studied under two Shared Socioeconomic Pathways: SSP2-4.5 and SSP5-8.5 scenarios. Quantile mapping function greatly improved the performance of CMIP6 by reducing the notable biases to match the distribution of observation data, the Climate Prediction Center (CPC). Results show persistent significant warming throughout the 21st century, increasing with the increase in radiative forcing. The MED will record a higher increase in temperature extremes as compared to SAH. The warming is supported by the projected reduction in cold days (TX10p) and cold nights (TN10p), with the reduction in the number of cold nights exceeding cold days. Notably, warm spell duration index (WSDI) and summer days (SU) have a positive trend in both timelines over the entire study area. There is a need to simulate how climate sensitive sectors, such as water and agriculture, are likely to be affected by projected changes under different scenarios for informed decision making in the choice and implementation of adaptation and mitigation effective measures.

A temperature‐driven model of phenological mismatch provides insights into the potential impacts of climate change on consumer–resource interactions

Anthropogenic climate change increasingly affects species phenology. Because trophic interactions often occur at specific phenological stages, changes in one species' phenology may affect others through phenological mismatch. When a consumer and a resource both exhibit a seasonal resting period, the synchrony of the end of their respective resting periods is fundamental for the persistence of their interaction. Since the consumer and its resource may react differently to changes in temperature regime, the synchrony between them could be altered. We investigate potential effects of climate change on species' synchrony. We propose a general model that determines the duration of the resting period according to temperature, and its effects on the mismatch between phenological stages of two interacting species. We illustrate our approach using the spruce budworm–balsam fir system in eastern Canada. We find that an increase in temperature advances the end of the resting period. However, the effects of a warm or cold spell during the resting period strongly vary according to the timing and the duration of the spell. Depending on how a consumer and its resource react to the same temperature shift, the mismatch between them may increase or decrease. The spruce budworm–balsam fir model predicts that an increase in temperature may increase the mismatch between the insect and the tree in southern sites, but may increase the synchrony in northern sites. This modelling approach is of prime importance to investigate potential effects of climate change on consumer–resource systems.

Value of the Copernicus Arctic Regional Reanalysis (CARRA) in representing near-surface temperature and wind speed in the north-east European Arctic

The representation of 2-m air temperature and 10-m wind speed in the high-resolution (with a 2.5-km grid spacing) Copernicus Arctic Regional Reanalysis (CARRA) and the coarser resolution (ca. 31-km grid spacing) global European Center for Medium-range Weather Forecasts fifth-generation reanalysis (ERA5) for Svalbard, northern Norway, Sweden and Finland is evaluated against observations. The largest differences between the two reanalyses are found in regions with complex terrain and coastlines, and over the sea ice for temperature in winter. In most aspects, CARRA outperforms ERA5 in its agreement with the observations, but the value added by CARRA varies with region and season. Furthermore, the added value by CARRA is seen for both parameters but is more pronounced for temperature than wind speed. CARRA is in better agreement with observations in terms of general evaluation metrics like bias and standard deviation of the errors, is more similar to the observed spatial and temporal variability and better captures local extremes. A better representation of high-impact weather like polar lows, vessel icing and warm spells during winter is also demonstrated. Finally, it is shown that a substantial part of the difference between reanalyses and observations is due to representativeness issues, that is, sub-grid variability, which cannot be represented in gridded data. This representativeness error is larger in ERA5 than in CARRA, but the fraction of the total error is estimated to be similar in the two analyses for temperature but larger in ERA5 for wind speed.

The Future Extreme Temperature under RCP8.5 Reduces the Yields of Major Crops in Northern Peninsular of Southeast Asia.

This study explores the impact of rising near-future temperatures on crop yields, particularly rice and maize, in northern Thailand between 2020 and 2029. The potential for high temperatures in northern Thailand between 2020 and 2029 under the Representative Concentration Pathways 8.5 (RCP8.5) scenario indicates that Thailand experienced hot trends between 2020 and 2029 as measured by the annual maximum value of daily maximum temp (TXx), annual minimum value of daily maximum temp (TXn), annual minimum value of daily minimum temp (TNn), and annual maximum value of daily minimum temp (TNx). Northern Thailand had the most dramatic changes in TXn and TNn. Furthermore, TXn levels were found to be significantly higher in northern Thailand. The number of days when TX < 10th percentile (TX10p) intensity decreased, while the number of days with TN < 10th percentile (TN10p) intensity was increasing. The number of days when TN > 90th percentile (TX90p) has become increasingly rare in northern Thailand. The TN90p was dropping in northern Thailand, whereas the Warm Spell Duration Index (WSDI) was growing. Additionally, the cold spell duration index (CSDI) continues to decline. This indicates that the heat persistence index is increasing in northern Thailand. Temperature rises are the most likely to have a detrimental impact on agricultural production, and climate models can predict regional temperature changes with more precision than precipitation. Throughout the planting season (June-December), average yearly temperatures in rice and maize growing areas have climbed by 0.5–0.6°C. The impact estimates for maize and rice are generally negative, that is, −10 ± 4.6% per °C and −8 ± 3.5% per °C, respectively.

Contrasting responses of forest growth and carbon sequestration to heat and drought in the Alps

Climate change is expected to increase both the frequency and the intensity of climate extremes, consequently increasing the risk of forest role transition from carbon sequestration to carbon emission. These changes are occurring more rapidly in the Alps, with important consequences for tree species adapted to strong climate seasonality and short growing season. In this study, we aimed at investigating the responses of a high-altitude Larix decidua Mill. forest to heat and drought, by coupling ecosystem- and tree-level measurements. From 2012 to 2018, ecosystem carbon and water fluxes (i.e. gross primary production, net ecosystem exchange, and evapotranspiration) were measured by means of the eddy covariance technique, together with the monitoring of canopy development (i.e. larch phenology and normalized difference vegetation index). From 2015 to 2017 we carried out additional observations at the tree level, including stem growth and its duration, direct phenological observations, sap flow, and tree water deficit. Results showed that the warm spells in 2015 and 2017 caused an advance of the phenological development and, thus, of the seasonal trajectories of many processes, at both tree and ecosystem level. However, we did not observe any significant quantitative changes regarding ecosystem gas exchanges during extreme years. In contrast, in 2017 we found a reduction of 17% in larch stem growth and a contraction of 45% of the stem growth period. The growing season in 2017 was indeed characterized by different drought events and by the highest water deficit during the study years. Due to its multi-level approach, our study provided evidence of the independence between C-source (i.e. photosynthesis) and C-sink (i.e. tree stem growth) processes in a subalpine larch forest.

Eucalyptus obliqua tall forest in cool, temperate Tasmania becomes a carbon source during a protracted warm spell in November 2017

Tasmania experienced a protracted warm spell in November 2017. Temperatures were lower than those usually characterising heatwaves. Nonetheless the warm spell represented an extreme anomaly based on the historical local climate. Eddy covariance measurements of fluxes in a Eucalyptus obliqua tall forest at Warra, southern Tasmania during the warm spell were compared with measurements in the same period of the previous year when temperatures were closer to average. Compared with previous year, the warm spell resulted in 31% lower gross primary productivity (GPP), 58% higher ecosystem respiration (ER) and the forest switching from a carbon sink to a source. Significantly higher net radiation received during the warm spell was dissipated by increased latent heat flux, while canopy conductance was comparable with the previous year. Stomatal regulation to limit water loss was therefore unlikely as the reason for the lower GPP during the warm spell. Temperatures during the warm spell were supra-optimal for GPP for 75% of the daylight hours. The decline in GPP at Warra during the warm spell was therefore most likely due to temperatures exceeding the optimum for GPP. All else being equal, these forests will be weaker carbon sinks if, as predicted, warming events become more common.

The Alternating Change of Cold and Warm Extremes Over North Asia During Winter 2020/21: Effect of the Annual Cycle Anomaly

AbstractLarge‐scale cold and warm spells successively attacked North Asia (NA) and resulted in a subseasonal alternation of extreme surface air temperature (SAT) before and after 15 January 2021. Our results show the synergistic effect of annual cycle and intraseasonal oscillation component (AC and ISO) of SAT and circulation anomalies caused temperature extremes. Before 15 January 2021, an eastward‐propagating Rossby wave train strengthened the cold circulation regime with an enhanced Ural blocking high (UBH) and surface Siberian high (SH). Circulation anomalies of the negative ISO were superimposed on the counterpart of AC, which easily resulted in cold extremes via nonlinear interaction of meridional temperature gradient. After mid‐January 2021, however, weakened UBH and SH caused warm advection and spells over NA. This synergistic effect of AC and ISO anomalies in SAT accounted for most historical temperature extremes, implying that AC anomalies have important indications for subseasonal alternation of temperature extremes.

Photoprotection contributes to freezing tolerance as revealed by RNA-seq profiling of rhododendron leaves during cold acclimation and deacclimation over time.

Cold acclimation (CA) and deacclimation (DA), which are often accompanied by changes in freezing tolerance (FT), carbohydrates and hormones, are crucial for winter survival, especially under global warming. Plants with weak CA and premature DA caused by warm winters and/or unseasonal warm spells can be easily injured by adverse reactions to cold. Thus, understanding the molecular mechanisms of FT is imperative. In this study, we used high-throughput RNA-seq to profile the CA and DA of leaves of overwintering Rhododendron "Miyo-no-Sakae" over time; these leaves do not undergo dormancy but do undergo photoprotection during CA, and they do not grow during DA. Using Mfuzz and weighted gene coexpression network analysis, we identified specific transcriptional characteristics in each phase of CA and DA and proposed networks involving coexpressed genes and physiological traits. In particular, we discovered that the circadian rhythm is critical for obtaining the strongest FT, and high expression of circadian rhythm-related genes might be linked to sugar accumulation during winter. Furthermore, evergreen leaves exhibited robust photoprotection during winter, as revealed by high values of nonphotochemical quenching, high expression of transcripts annotated as "early light-induced proteins", loss of granum stacks and destacking of thylakoids, all of which were alleviated during DA. The strong requirement of photoprotection could be the reason for decreased abscisic acid (ABA) and jasmonic acid (JA) contents during CA, and decreases in ABA and JA contents may contribute to decreases in lignin content. Our data suggest that the molecular mechanisms of FT in overwintering leaves are unique, which may be due to the high requirements for photoprotection during winter.