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Over recent decades, the southeastern United States (Southeast) has become increasingly well represented by the terrestrial climate proxy record. However, while the paleo proxy records capture the region's hydroclimatic history over the last several centuries, the understanding of near surface air temperature variability is confined to the comparatively shorter observational period (1895‐present). Here, we detail the application of blue intensity (BI) methods on a network of tree‐ring collections and examine their utility for producing robust paleotemperature estimates. Results indicate that maximum latewood BI (LWBI) chronologies exhibit positive and temporally stable correlations (r = 0.28–0.54, p < 0.01) with summer maximum temperatures. As such, we use a network of LWBI chronologies to reconstruct August‐September average maximum temperatures for the Southeast spanning the period 1760–2010 CE. Our work demonstrates the utility of applying novel dendrochronological techniques to improve the understanding of the multi‐centennial temperature history of the Southeast. Plain Language Summary: Tree‐ring data are important sources of paleoclimate information, which allow for the longer‐term evaluation of modern climate values and trends. Compared to much of North America, the Southeastern United States (Southeast) contains substantially fewer paleoclimate records from tree rings, and no estimates of past temperature variability which extend before the observational period. Employing a recently developed technique, which uses light reflectance properties of wood to obtain a representative metric of tree‐ring density, we develop a network of temperature‐sensitive tree‐ring records across the Southeast. These records enable us to reconstruct late summer maximum temperatures across the region spanning the period 1760–2023 CE. As few ground‐based, pre‐instrumental temperature records previously existed for this region, our reconstruction allows for an improved understanding of the region's multi‐centennial climatic history. Key Points: Maximum latewood blue intensity from tree rings can effectively be used to develop paleotemperature estimates for the southeastern USThe fidelity of tree‐ring density parameters for paleoclimate reconstruction are influenced by disturbance regimes and microsite conditionsCompared to the last 260 years, regional 20th‐century maximum late summer temperatures are not characterized by unprecedented positive trend [ABSTRACT FROM AUTHOR]

Future changes in Sahel precipitation are uncertain because of large differences among projections from various models. In order to explore this uncertainty, we use a storyline approach which seeks to identify alternative plausible evolutions of Sahel precipitation and their driving factors. By analyzing projections from the CMIP6 climate models, we show that changes in North Atlantic and in Euro‐Mediterranean temperatures explain up to 60% of the central Sahel precipitation change uncertainty. We then construct several storylines of Sahel precipitation change based on future plausible changes in North Atlantic and Euro‐Mediterranean temperatures. In one storyline, an amplified warming of both the North Atlantic and the Euro‐Mediterranean areas promotes a northward shift of the West African monsoon, increasing precipitation over the central Sahel, while, in the opposite storyline, a moderate warming in both regions is associated with a small change in precipitation over the central Sahel and a decrease in precipitation over the western Sahel, at the end of the 21st century. These results indicate that Sahel precipitation uncertainty will not be substantially reduced unless the uncertainty in the future warming of the North Atlantic and the Euro‐Mediterranean areas is constrained. Plain Language Summary: Variations in the strength of the West African Monsoon (WAM) circulation have societal impacts on around 80 million people from Senegal to Chad. However, future projections of the WAM precipitation are uncertain for the end of the 21st century because of strong differences from one climate model to another. We show here that sources of uncertainty in Sahel precipitation depend on changes in North Atlantic and Euro‐Mediterranean temperature, relative to changes in global mean surface air temperature. We show that differences in how climate models simulate the future warming over the North Atlantic and Euro‐Mediterranean area explain a large proportion of the uncertainty in precipitation change over the central Sahel. We provide a method that could be helpful at selecting models for impact studies, based on their sensitivity to climate change over the North Atlantic ocean and Mediterranean Sea. Key Points: Future changes in Sahel precipitation are uncertainChanges in North Atlantic and Euro‐Mediterranean temperatures explain up to 60% of the Sahel precipitation change uncertaintyUncertainty in changes in Sahel precipitation is associated with uncertainty in the future northward shift of the Saharan heat low [ABSTRACT FROM AUTHOR]

Surface air temperatures are significant indicators of environmental and climatic change that affect a diverse set of physical systems including permafrost. Most temperature products, such as gridded or reanalysis data, are still at a relatively low spatial resolution, limiting the ability to simulate heterogeneous permafrost changes and leading to large uncertainties. Here we apply a downscaling method based on elevation to obtain high‐resolution surface air temperatures from the sixth Coupled Model Intercomparison Project in Northern Hemisphere permafrost regions. Root‐mean‐square errors and mean absolute errors after downscaling are reduced by 34 and 37%, respectively, relative to meteorological site data and gridded observations from the Climatic Research Unit. Compared to the downscaled surface air temperature data, nondownscaled model projections overestimate by 0.12–0.39°C in the discontinuous, isolated, and sporadic permafrost regions and underestimate up to 0.18°C in the continuous permafrost region under different emission scenarios. The warming rates in Northern Hemisphere permafrost regions were 0.093°C/10 year during the historical (1850–2014) period and are projected to be 0.22°C/10 year for SSP1‐2.6, 0.48°C/10 year for SSP2‐4.5, 0.75°C/10 year for SSP3‐7.0, and 0.95°C/10 year for SSP5‐8.5 during 2015–2100, which is 1.4–1.6 times the warming of nonpermafrost regions. Warming rates in high latitudes are 1.2–1.7 times higher than those in high‐elevation regions. Continuous permafrost regions' warming will be 1.2–1.4 times higher than in other permafrost regions. For permafrost with high ground ice content, warming will be 1.1 times greater than in permafrost regions with medium or low ground ice content. [ABSTRACT FROM AUTHOR]

Estimates of global mean near‐surface air temperature (global SAT) for the Cenozoic era rely largely on paleo‐proxy data of deep‐sea temperature (DST), with the assumption that changes in global SAT covary with changes in the global mean deep‐sea temperature (global DST) and global mean sea‐surface temperature (global SST). We tested the validity of this assumption by analyzing the relationship between global SST, SAT, and DST using 25 different model simulations from the Deep‐Time Model Intercomparison Project simulating the early Eocene Climatic Optimum (EECO) with varying CO2 levels. Similar to the modern situation, we find limited spatial variability in DST, indicating that local DST estimates can be regarded as a first order representative of global DST. In line with previously assumed relationships, linear regression analysis indicates that both global DST and SAT respond stronger to changes in atmospheric CO2 than global SST by a similar factor. Consequently, this model‐based analysis validates the assumption that changes in global DST can be used to estimate changes in global SAT during the early Cenozoic. Paleo‐proxy estimates of global DST, SST, and SAT during EECO show the best fit with model simulations with a 1,680 ppm atmospheric CO2 level. This matches paleo‐proxies of EECO atmospheric CO2, indicating a good fit between models and proxy‐data. Plain Language Summary: The global mean surface temperature is a commonly used indicator to measure global climate change. Our understanding of how the global surface temperature has changed in the last 66 Myr is mainly based on the assumption that changes in the deep‐sea temperature (DST) reflect changes at the surface well. Here, we test this idea by using climate model simulations of a hothouse period, the early Eocene Climate Optimum (53–49 Myr ago). We find that changes in the global DST indeed correlate well with temperature changes at the surface. We find the best fit between the models and data from the early Eocene for atmospheric CO2 levels at around 1,680 ppm. Key Points: In early Eocene model simulations (Deep‐Time Model Intercomparison Project [DeepMIP]), global mean deep‐sea, and surface temperatures are equally sensitive to atmospheric CO2 changesModel‐simulated deep‐sea temperatures show limited spatial variability, making local estimates generally representative of the global meanThe model simulations with a CO2 forcing of 1,680 ppm match paleo‐proxies of global mean deep‐sea, sea‐surface, and surface temperature [ABSTRACT FROM AUTHOR]

Many studies have examined the influence of global climate change and regional human activities on the climate of arid Central Asia, but their separate contributions have rarely been reported due to lack of a comprehensive description of regional human activities, especially the irrigation process, in the model. In this study, we conducted two sets of 40‐year continuous simulations with and without human activities to distinguish the effects of regional human activities and global climate change on the surface air temperature (T2), humidity (Q2) and precipitation of a typical mountain‐oasis‐desert system in northern Tianshan by using a modified WRF‐Noah model with irrigation processes. The results indicated that the annual average T2, Q2 and convective precipitation during the past 40 years increased by 0.393°C, 0.063 g/kg and 0.014 mm/d, respectively, and the annual average large‐scale precipitation and total annual average precipitation decreased by approximately −0.017 and −0.003 mm/d, respectively. Regional human activities contributed 52.63% and 50.38% of the change in large‐scale precipitation and total precipitation, respectively, while global climate change accounted for more than 80% of the change in temperature and humidity. Among the human activities, irrigation had the largest impact on T2/Q2 over the local irrigation region and on precipitation in the upstream Tianshan mountainous area. However, for the whole region, the increase in the green vegetation fraction was the core factor that impacted the regional climate. Our results can deepen the understanding of the attribution and mechanism of climate change and provide water and heat change references for other mountain‐oasis‐desert systems in arid endorheic regions. Plain Language Summary: We counted the separate contributions of global climate change and regional human activities on the surface air temperature (T2), humidity (Q2) and precipitation of arid Central Asia by difference of two sets of 40‐year continuous simulations with and without human activities. The results indicated that regional human activities contributed 52.63% and 50.38% of the change in large‐scale precipitation and total precipitation, respectively, while global climate change accounted for more than 80% of the change in temperature and humidity. Among the human activities, irrigation had the largest impact on T2/Q2 over the local irrigation region and on precipitation in the upstream Tianshan mountainous area. But the vegetation greening was main reason for the changes of regional climate for the whole region. Our results can deepen the understanding of the attribution and mechanism of climate change and provide water and heat change references for other mountain‐oasis‐desert systems in arid endorheic regions. Key Points: The attribution and mechanism of regional climate change in a typical mountain‐oasis‐desert system of arid Central Asia were exploredGlobal climate change dominated regional temperature change while regional human activities dominated regional precipitation changeThe increase in the green vegetation fraction was the core factor that impacted regional climate [ABSTRACT FROM AUTHOR]

The rise in the Earth's global average surface temperature and the increase of extreme weather events have been the focus of scientific discussion in the last decades. Extreme heat combined with other environmental extremes, like high concentrations of air pollution, may induce health problems—especially in socioeconomically vulnerable populations. Spring in South America requires particular attention due to its association with hot, dry weather and air pollution in most parts of the continent. This paper intends to better comprehend the behaviour of heatwaves during the austral winter–spring transition. We propose identifying the spatial coverage, frequency and intensity of heatwaves in homogeneous areas of maximum air temperature near the surface (Tmax). We employed cluster analyses during the period between July and October for 41 years (1979–2019) through the ERA5 reanalysis. Homogeneous Tmax areas in South America were defined by cluster analyses that indicated three homogeneous Tmax regions, as follows: a larger area of the continent including the tropical region (Area 1), eastern and southeastern South America (Area 2) and southernmost and steep areas in the Andes (Area 3). The heatwave events identified via ERA5 reanalysis were classified according to their intensity (intense, moderate and weak events). Spatial frequency and trend analyses were also performed regarding the intensity and persistence of heatwave episodes. These methods allowed the identification of the behavioural aspect of heatwaves spanning the last four decades. The Mann–Kendall statistical test (MK) was applied in order to analyse the heatwave trend with a statistical significance level of 5%. A total of 191 heatwave episodes were identified. Of this total, 47.12, 35.60 and 17.28% of episodes occurred in Areas 2, 3 and 1, respectively. The hotter area extending from northeast to southwest in central South America stood out by its largest frequencies of intense heatwave episodes. Across the continent there was a significant increase in the intensity and persistence of heatwaves over the period of 1979 through to 2019. [ABSTRACT FROM AUTHOR]

The last millennium (LM, 1000–1850 AD) is crucial for studying historical climate change on decadal to multidecadal timescales. The summer surface air temperature (SAT) evolutions on regional scales (e.g. over China) are more uncertain than the globe/Northern Hemisphere, especially in response to external forcing factors and internal climate variability. Here, we provide one‐signal (full‐forcing) fingerprints of summer SAT in China derived from three large ensemble model archives with a multi‐proxy reconstruction during the LM, Little Ice Age (LIA, 1451–1850 AD), and Medieval Climate Anomaly (MCA, 1000–1250 AD), respectively. Our results show that (a) SATs in the northeast, southeast, northwest, and Tibetan Plateau (TP) regions of China show evident decreasing trends during the LM. External forcing response from all model archives agrees with the regional SAT reconstruction but underestimates variability in northwest China at the multidecadal timescale. (b) During the LIA, the summer regional SAT exhibits a cold condition in the reconstruction and simulations, especially in the northeast and northwest regions of China. External forcing responses in most model archives are the dominant factor on multidecadal SAT evolutions in the southeast, northeast, and TP regions of China and decadal SAT evolutions in northwest China. (c) During the MCA, detection and attribution of SAT shows that internal climate variability dominates in southeast, northeast, and TP regions of China, but external forcing dominates in northwest China at decadal to multidecadal timescales. These results contribute to a better understanding of the causes and mechanisms of regional climate change. [ABSTRACT FROM AUTHOR]

Long‐term satellite land surface temperature (LST) data are desirable to augment 2m air temperatures (T2m) measured in situ and as an independent measure of surface temperature change. However, previous studies show variable agreement between LST and T2m time series. The objective of this study is to assess the stability and trends in six new LST data sets from the European Space Agency's Climate Change Initiative for LST (LST_cci). LST anomalies are compared with homogenized station T2m anomalies over Europe, which verifies all six data sets are well coupled (LST vs T2m anomaly correlations and slopes: 0.6–0.9). The temporal stability of the LST_cci data is assessed through a comparison with the T2m anomaly time series. Only the LST_cci data sets for the MODerate resolution Imaging Spectroradiometer (MODIS) onboard Aqua and the Advanced Along‐Track Scanning Radiometer (AATSR) appear stable; the MODIS/Terra, ATSR‐2, and multisensor InfraRed and MicroWave data sets show non‐climatic discontinuities associated with changes in sensor and/or drift over time. For MODIS/Aqua (2002–2018), significant trends in LST of 0.64–0.66 K/decade compare well with the equivalent T2m trends of 0.52–0.59 K/decade. The LST and T2m trends for AATSR (2002–2012) are found to be statistically insignificant, likely due to the comparatively short study period and specific years available for analysis. No evidence is found to suggest that trends calculated using cloud‐free InfraRed observations are affected by clear‐sky bias. This study suggests that satellite LST data can be used to assess warming trends over land and for other climate applications if the required homogeneity is assured. Plain Language Summary: Long‐term changes in surface temperature due to climate change are usually studied using near‐surface air temperatures (T2m) from weather stations. However, these data are not available everywhere and changes in T2m are poorly understood in some regions (e.g., parts of Africa). This study assesses the potential for satellite‐observed land surface temperatures (LST) to help fill these gaps. The study finds that LST and T2m provide very similar information on surface temperatures and therefore LST can provide useful information where T2m data are unavailable. However, some of the LST data sets are found to be "unstable" and suffer from jumps and changes over time that result from for example, changes in satellite instrument or a change in the orbit of the satellite platform. These unstable LST data sets cannot be used to measure changes in surface temperature. However, the study finds that for "stable" LST data sets, which do not suffer from these non‐climatic jumps and changes, the changes in LST over time are very similar to, or even the same as, the changes in T2m. Therefore, this work suggests that satellite LST data could be used to measure changes in surface temperature where there are not enough stations to make these measurements using T2m. Key Points: The stability of six satellite land surface temperature data sets is assessed using homogenized station air temperatures over EuropeOnly two data sets are stable; the other four show non‐climatic discontinuities associated with a change in sensor and/or drift over timeSignificant trends in land surface temperature of ∼0.65 K/decade over Europe are found for one of the stable data sets over 2002–2018 [ABSTRACT FROM AUTHOR]

An important metric for communicating the context and impact of climate change is regionally or nationally averaged temperature anomalies. Quantifying the associated uncertainty adds confidence in the reporting of temperature records and trends. For area‐averaged temperature anomalies derived from point‐source observations we model the uncertainty by a three‐step process: (1) observation random error; (2) spatial analysis grid point uncertainty; and (3) area‐average uncertainty. Each step should reflect the method used to calculate the area average. For Australian area‐average temperature anomalies, the observation random error is modelled as the sum of the sampling uncertainty and the uncertainty in the adjustment process. Daily maximum and minimum temperature time series from the 104 non‐urban locations of the Australian Climate Observation Reference Network—Surface Air Temperature (ACORN‐SAT) version 2 dataset for 1981–2010 are used to estimate the sampling uncertainty, based on predicted location pair zero‐distance correlations having values of less than 1.0. ACORN‐SAT version 2 breakpoint information is used to estimate the uncertainty from the selection of reference stations used in the detection and homogenization processes. Monthly area‐average temperature uncertainty is estimated for the 1910–2018 period from Monte Carlo simulations using the observation uncertainty and the available ACORN‐SAT network for each month. The uncertainty in annual area‐average temperature anomalies is then obtained from the monthly uncertainties and inter‐month correlations modelled with a periodic auto‐regressive model. The ±2σ annual mean temperature area‐average uncertainty is ±0.084°C in 2018 and ± 0.176°C in 1910. The larger value in 1910 is primarily due to decreased ACORN‐SAT coverage over areas of inland Australia. The Australian annual mean temperature change and associated uncertainty from ACORN‐SAT version 2 over 1910–2018 is found to be 1.42 ± 0.28°C. This is consistent with global air temperature warming by around 1°C since 1850. [ABSTRACT FROM AUTHOR]

The Community Earth System Model (CESM) is widely used for the prediction and understanding of climate variability and change. Accurate simulation of the behavior of near surface air temperature (T2m) is critical in such a model for addressing societally relevant problems. However, previous versions of CESM suffered from an overestimation of wintertime T2m variability in Northern Hemisphere (NH) land regions. Here, it is shown that the latest version of CESM (CESM2) exhibits a much improved representation of wintertime T2m variability compared to its predecessor and it now compares well with observations. A series of targeted experiments reveal that an important contributor to this improvement is the local effects of changes to the representation of snow density within the land surface component. Increased snow densities in CESM2 lead to enhanced conductance of the snow layer. As a result, larger heat fluxes across the snow layer are induced in the presence of T2m anomalies, leading to a greater dampening of surface and near surface atmospheric temperature anomalies. The implications for future projections with CESM2 are also considered through comparison of the CESM1 and CESM2 large ensembles. Aligned with the reduction in surface temperature variability, compared to CESM1, CESM2 exhibits reduced ensemble spread in future projections of NH winter mean temperature and a smaller decline in daily wintertime T2m variability under climate change. Overall, this improvement has increased the accuracy of CESM2 as a tool for the study of wintertime T2m variability and change. Plain Language Summary: A societally relevant quantity that is predicted by Earth System Models is near surface air temperature (T2m). Accurate simulation of this quantity requires accurate representation of atmospheric circulation, boundary layer processes and land‐atmosphere interaction. Here we show substantial improvements in the representation of wintertime T2m variability in the latest version of the Community Earth System Model and isolate the roles of different aspects of the model development. Increases in the density of snow in the new model are shown to reduce T2m variability. Snow density governs the heat fluxes induced across the snow layer via conductance and increased snow density in the model has increased the ability of heat fluxes across the snow layer to dampen T2m variability. The implications of this change for future climate projections are explored and it is found that CESM2 exhibits a reduced uncertainty in future projections of mean T2m over Northern Hemisphere land regions, as might be expected given the reduced sampling uncertainty in T2m due to reduced internal variability. Furthermore, wintertime T2m variability is expected to decrease under climate change and this decrease is larger in the older version of the model that exhibited greater T2m variability in its historical simulation. Key Points: Northern Hemisphere wintertime surface temperature variability was overestimated in Community Earth System Model Version 1 and is improved in Community Earth System Model Version 2 (CESM2)This improvement has arisen through an increase in snow density and associated snow conductanceA reduced ensemble spread and reduced decline in daily variance is also found in future projections of surface temperature with CESM2 [ABSTRACT FROM AUTHOR]

Spring snow depth (SSD) in the Tibetan Plateau (TP) plays an essential role in snow water estimation, vegetation growth, and climate projections for Southwest China and the surrounding Asian countries. Previous studies have reported a decline in SSD that is associated with increasing surface air temperature (Ts) across the TP over the past decades. However, only a few studies have explored the potential feedback of SSD to Ts, which has hindered the elucidation of the vulnerability of snow cover in the TP against the backdrop of rapid climate change. Based on data from 69 meteorological stations in the central and eastern parts of the TP, this study analyses changes in SSD and its feedback to Ts from 1961 to 2013. Overall, a slightly negative trend in SSD was observed from 1961 to 2013, during which two contrasting trends were detected using a piecewise regression approach: an initial increase from 1961 to 1981 (0.12 cm decades−1, p <.01) followed by a decrease from 1982 to 2013 (−0.36 cm decades−1, p <.01). Sensitivity analysis revealed that the SSD decline was systematically correlated with a positive Ts anomaly, with sensitivities of −0.050 cm °C−1 (p <.01) and −0.069 cm °C−1 (p <.01) for 1961–2013 and 1982–2013, respectively. However, following the accelerated SSD decline during 1982–2013, its potential feedback to Ts was attenuated, with averaged feedback values from −1.328°C cm−1 during 1961–2013 to −0.936°C cm−1 during 1982–2013 in latitudes below 3,500 m, distributed in the Qaidam and Yellow River basins, and the upper reaches of the Yangtze River basin. The decrease in feedback from SSD to Ts may lead to changes in the mechanism controlling Ts dynamics across the TP, which should be considered in climate change projections in future studies. [ABSTRACT FROM AUTHOR]

In tropical regions, particularly in Central and South America (CSA), the projections of climate seasonality under climate change are still uncertain. This is especially true for ecologically‐relevant variables such as precipitation and temperature. However, assessments of model‐based projections of seasonal climate for this region are scarce. We analyzed the simulation of seasonal precipitation and air surface temperature in CSA and six sub‐regions within from 49 models included in the Coupled Intercomparison Project Phase 5 (CMIP5) and 33 models from CMIP6. In general, continental patterns and seasonality of both variables are moderately well resembled, while most models show systematic biases over the oceans, producing unrealistic spatial patterns. To quantify how well CMIP5/CMIP6 models simulate these variables, we used Taylor diagrams with respect to TRMM for precipitation and ERA5 for temperature. Precipitation shows the largest spread among models. Conversely, temperature shows a better simulation. CMIP5/CMIP6 models exhibit a better performance simulating both variables during December–January–February and March–April–May than during the other seasons. This is partly due to the reduced model biases in representing the Intertropical Convergence Zone during these two seasons. In general, biases are reduced in the CMIP6 models with respect to CMIP5. Regarding regional evaluations, precipitation patterns for Mesoamerica, Cerrado and Chaco regions are better reproduced compared to TRMM, while the annual cycles for the Andes hotspot, Central Chile and Guianas are not well simulated, mainly during their wet seasons. However, these biases are reduced in CMIP6 models. In regard to precipitation projections, models only agree over most of the regions with decreasing precipitation. Conversely, temperature exhibits a general consensus on persistent warming even during the historical period, with an average increase of 6°C by the end of the century, according to the CMIP6 models. [ABSTRACT FROM AUTHOR]

Ecological research heavily relies on coarse‐gridded climate data based on standardized temperature measurements recorded at 2 m height in open landscapes. However, many organisms experience environmental conditions that differ substantially from those captured by these macroclimatic (i.e. free air) temperature grids. In forests, the tree canopy functions as a thermal insulator and buffers sub‐canopy microclimatic conditions, thereby affecting biological and ecological processes. To improve the assessment of climatic conditions and climate‐change‐related impacts on forest‐floor biodiversity and functioning, high‐resolution temperature grids reflecting forest microclimates are thus urgently needed. Combining more than 1200 time series of in situ near‐surface forest temperature with topographical, biological and macroclimatic variables in a machine learning model, we predicted the mean monthly offset between sub‐canopy temperature at 15 cm above the surface and free‐air temperature over the period 2000–2020 at a spatial resolution of 25 m across Europe. This offset was used to evaluate the difference between microclimate and macroclimate across space and seasons and finally enabled us to calculate mean annual and monthly temperatures for European forest understories. We found that sub‐canopy air temperatures differ substantially from free‐air temperatures, being on average 2.1°C (standard deviation ± 1.6°C) lower in summer and 2.0°C higher (±0.7°C) in winter across Europe. Additionally, our high‐resolution maps expose considerable microclimatic variation within landscapes, not captured by the gridded macroclimatic products. The provided forest sub‐canopy temperature maps will enable future research to model below‐canopy biological processes and patterns, as well as species distributions more accurately. [ABSTRACT FROM AUTHOR]

Rewetting of disturbed peatlands is an important restoration strategy for climate change mitigation. Previous work primarily focuses on the biogeochemical processes altered by rewetting and few studies have investigated the biophysical impacts, which can diminish or amplify biogeochemical effects beyond the ecosystem scale. We used a paired flux tower approach in a restored peatland to collect year‐round eddy covariance data to assess the biophysical impacts of disturbance and management practices. The first site was actively rewetted and is characterized by Sphagnum and white beak‐rush with patches of open water. The second site represents a disturbed ecosystem, which underwent natural regeneration and is dominated by scrub pine, Sphagnum, and low shrubs. We found that the actively restored site had higher net radiation compared to the second site due to more surface water ponding; however, the higher aerodynamic conductance at the passively restored site contributed to enhanced daytime turbulent fluxes, and hence, both sites had similar aerodynamic temperatures during the daytime. The actively restored site experienced warmer nighttime and seasonal aerodynamic temperature as much of the excess radiation during the day was stored in the water column and released at night. To achieve restoration goals, higher water tables are now maintained throughout large sections of the bog. The study implies that water table manipulation has the potential to minimize greenhouse gas emissions from the bog, thereby allowing the biophysical impacts of peatland restoration to enhance the biogeochemical benefits. Therefore, it is important to consider both biophysical and biogeochemical changes in peatland restoration management. Plain Language Summary: Peatland restoration through rewetting influences the climate by altering land‐atmosphere greenhouse gas dynamics and energy and water exchanges. The energy and water exchanges altered by rewetting can influence surface and air temperature, and hence local and regional climate; however, they are understudied. This study analyzed year‐round water and energy exchange measurements at two sites in the Burns Bog peatland in British Columbia, Canada, which represent two different dominant ecosystem types that experienced different restoration strategies. We found that the rewetted site with more open water had warmer nighttime and seasonal surface temperature. The results indicate that water levels and vegetation type influenced by restoration strategies have a strong impact on energy partitioning, surface aerodynamic characteristics, and consequently surface and air temperatures. Peatland restoration management should recognize the implications of both greenhouse gas fluxes and energy and water exchanges to fully understand climatic impacts of restoration. Key Points: Significant surface temperature differences at night between the two restored peatland sites with different surface characteristicsThe warmer nighttime and seasonal temperatures at the actively rewetted site were due to energy stored in standing water during daytimeRestoration management has to consider biophysical impacts in addition to biogeochemical changes [ABSTRACT FROM AUTHOR]

The recent summer surface air temperature (SAT) changes over densely populated Eurasia exhibit a non‐uniform pattern with amplified warming over Europe and East Asia (EA) but weak warming over Central Asia (CA), forming a wave train‐like structure. However, the key factors that determine this non‐uniform warming pattern remain unclear. By analyzing observations and model simulations, here, we show that more than half of the SAT multidecadal variations from 1950 to 2014 over Europe‐west Asia and EA may have resulted from external forcing, rather than from internal variability in the Atlantic as previously thought. In contrast, the recent SAT over CA is influenced mainly by internal variations in the Atlantic and Pacific oceans. Large ensemble model simulations suggest that the forced SAT multidecadal variations over Eurasia are mainly caused by changes in greenhouse gases and aerosols. Our findings provide strong evidence for major impacts of external forcing on multidecadal climate variations over Eurasia. Plain Language Summary: Previous studies have documented non‐uniform decadal warming rates in summer since the mid‐1990s over densely populated Eurasia, with amplified warming over Europe and East Asia (EA) but weak warming over Central Asia. This unique tripole warming pattern and its cause have attracted considerable attention. It may arise from internal variability (IV; e.g., Atlantic Multidecadal Oscillation [AMO]) and/or external forcing. Furthermore, it was found that the recent AMO resulted from both IV and decadal changes in aerosols. Thus, the relative contributions of IV and external forcing to the recent Eurasian warming pattern remain unclear. Here we show that the surface air temperature multidecadal variations since 1950 over Eurasia are caused by both IV and external forcing, with more than half of the variations over Europe‐west Asia and EA resulted from external forcing (i.e., greenhouse gases [GHGs] and aerosols), in contrast to the previous notion that the non‐uniform warming is mainly originated from the Atlantic Ocean. Looking ahead into the upcoming decades, aerosols will continue to decrease, whereas the AMO will likely be in its positive phase. These changes, together with continued increases in GHGs, will accelerate warming trends over Eurasia and further exacerbate summer heat waves. Key Points: More than half of the multidecadal surface air temperature (SAT) variations from 1950 to 2014 over Europe‐west Asia and East Asia resulted from external forcingThe forced SAT multidecadal variations over Eurasia are mainly caused by changes in greenhouse gases and aerosolsOur study helps reconcile human and natural drivers of the tripole pattern of multidecadal summer temperature variations over Eurasia [ABSTRACT FROM AUTHOR]

Water vapour is among the most critical variables that shape the Earth's energy balance and hydrologic cycle. Surface relative humidity (RH) and specific humidity (SH) are the main measures of atmospheric moisture. Long‐term changes in these variables in South Korea vary regionally as well as locally, but have not been explored using long‐term observation data. This study analyzes the annual trends of the means and SD of RH, surface air temperature, and SH in South Korea at 55 stations for the period 1973–2018. Three trend detection tests based on the Mann–Kendall test have been performed to detect trends at the 5% significance level for the annual and monthly statistics (means and SD) of these variables. Significant decreasing and increasing trends have been detected in the annual means of RH and air temperature, while the annual mean of SH has remained nearly constant. Overall, the surface air in South Korea has become drier over the study period, particularly in winter. The trend in RH and SH varies widely according to months to result in the increase in the SD over the annual cycle. The monthly SD of RH, SH, and surface air temperature show especially large positive trends for March and September, when the transition from winter to spring and summer to fall, respectively, occurs. [ABSTRACT FROM AUTHOR]

The Arctic has experienced prominent climate warming, at the beginning of the 20th century and currently. Comparing the driving mechanism during these periods helps to explain the causes of contemporary climate change. Our study explores the impact of regional circulation on Svalbard's surface air temperature (SAT, 2 m above ground). We used air temperature data from Svalbard Airport, Bjørnøya stations, and three regional circulation indices that describe the frequency of cyclonic conditions, zonal circulation, and meridional circulation. The indices were calculated for four circulation areas with differing circulation conditions and, therefore, may have various impacts on long‐term changes in SAT. This was checked for the entire study period (1920–2018), and 30‐year sub‐periods representing the most prominent climatic events: the early 20th‐century Arctic warming (ETCAW), contemporary Arctic warming (CAW), and a cold period between them (CAP). In autumn and winter, the deviations in SAT from the long‐term average during warm and cold periods were almost twice as large at Svalbard Airport as at Bjørnøya. In these seasons, the ETCAW was significantly warmer than the subsequent cold period, which was not the case for summer and spring. However, long‐term trends in the regional circulation indices were more evident in summer and spring than in autumn and winter. Air temperature was the most strongly influenced by meridional circulation over the eastern circulation areas, with the exception of spring, when air temperature variability was more affected by zonal circulation. The recent warming weakened the relationship between SAT and the indices in summer. We attributed the ETCAW in autumn to a southerly advection of sensible heat. During the same historical period, the impact of the indices was much weaker in winter. In winter during the CAP, there was a higher frequency of northern air advection, particularly over the northern part of the Greenland Sea. [ABSTRACT FROM AUTHOR]

We provide an updated analysis of instrumental Greenland monthly temperature data to 2019, focusing mainly on coastal stations but also analysing ice‐sheet records from Swiss Camp and Summit. Significant summer (winter) coastal warming of ~1.7 (4.4)°C occurred from 1991–2019, but since 2001 overall temperature trends are generally flat and insignificant due to a cooling pattern over the last 6–7 years. Inland and coastal stations show broadly similar temperature trends for summer. Greenland temperature changes are more strongly correlated with Greenland Blocking than with North Atlantic Oscillation changes. In quantifying the association between Greenland coastal temperatures and Greenland Ice Sheet (GrIS) mass‐balance changes, we show a stronger link of temperatures with total mass balance rather than surface mass balance. Based on Greenland coastal temperatures and modelled mass balance for the 1972–2018 period, each 1°C of summer warming corresponds to ~(91) 116 Gt·yr−1 of GrIS (surface) mass loss and a 26 Gt·yr−1 increase in solid ice discharge. Given an estimated 4.0–6.6°C of further Greenland summer warming according to the regional model MAR projections run under CMIP6 future climate projections (SSP5‐8.5 scenario), and assuming that ice‐dynamical losses and ice sheet topography stay similar to the recent past, linear extrapolation gives a corresponding GrIS global sea‐level rise (SLR) contribution of ~10.0–12.6 cm by 2100, compared with the 8–27 cm (mean 15 cm) "likely" model projection range reported by IPCC in 2019 (SPM.B1.2). However, our estimate represents a lower limit for future GrIS change since fixed dynamical mass losses and amplified melt arising from both melt‐albedo and melt‐elevation positive feedbacks are not taken into account here. [ABSTRACT FROM AUTHOR]

New Zealand's best‐known record of surface air temperature is the partially homogenised seven‐station series of the National Institute of Water and Atmospheric Research. The homogeneity analysis used in constructing this series depends on the completeness of the site histories and issues arise if relevant information is missing. Recent developments in the online availability of searchable historical records provide an opportunity to supplement early metadata. This potential is explored here using Albert Park (Auckland) as a case study. A wealth of relevant historical information about Albert Park was found in online archives, especially related to a major episode of site disturbance, evolving site environment and instrumentation changes. [ABSTRACT FROM AUTHOR]

The Tibetan Plateau (TP), known as earth's "Third Pole," influences regional and even global weather and climate systems through its mechanical and thermal‐dynamical forcing. Near‐surface (2 m) air temperature (Ta) and surface (skin) temperature (Ts) are two crucial parameters of land–atmosphere interactions and climate change. Their difference (ΔT = Ts − Ta) determines the heating source over the TP that drives the Asian summer monsoon. This study focuses on climatology, inter‐annual variability, and long‐term trend of ΔT over the TP in the last four decades (1979–2018), based on four latest reanalysis datasets including ERA‐Interim, ERA5, MERRA2, and JRA55, along with observational data. We show that ΔT‐based different datasets display fairly different climatology in terms of seasonality, spatial distribution, and long‐term trend. ΔT exhibits a clear seasonality with negative value in winter and positive ones in summer despite different strengths and timings presented by the reanalyses. Along with global warming, all reanalyses except JRA55 exhibit obvious downwards trends of ΔT in a spatially non‐uniform way. The median ΔT among the four reanalyses features uniform decreases in all seasons, with the most distinct area on the northern TP, as well as the largest and least decreases in autumn and spring, respectively. Further analysis shows that the differences in ΔT are most likely associated with discrepancies in radiation forcing, snow cover, wind speed, and boundary layer height within the reanalyses. The present findings highlight the difficulty for the state‐of‐the‐art reanalyses to represent the climate change over the TP and point to possible factors behind the deficiencies identified. [ABSTRACT FROM AUTHOR]

In climate change research, accurate temperature data are often demanded. However, affected by many factors, especially solar radiation, the accuracy of environmental air temperature measurement can be greatly reduced, since there is a difference in temperature between the environmental air and the related temperature measured by the sensor accommodated inside the radiation shield. In the paper, the term “temperature error” refers to the temperature difference described above. To improve the accuracy of the temperature data, a temperature errorcorrection method is proposed. First, a computational fluid dynamics (CFD) method is adopted to quantify the temperature errors accurately. A neural network algorithm is then applied to form a universal correction equation by fitting temperature errors calculated using the CFD method. Finally, to validate the correction equation, field observation experiments are performed. The root mean square error (RMSE) and the mean absolute error (MAE) between the temperature errors obtained experimentally using a sensor inside the DTR503A shield and the corresponding temperature errors determined by using the proposed correction method are 0.043 and 0.038°C, respectively. The RMSE and MAE for the DTR13 radiation shield are 0.049 and 0.044°C, respectively. This method may reduce the error of the temperature data to 0.05°C. If the environmental factors corresponding to the temperature data can be quantified accurately, the factors influencing the temperature error can be added to the correction method continuously. The accuracy of this correction method may be furtherly improved. [ABSTRACT FROM AUTHOR]

Multi‐model climate experiments carried out as part of different phases of the Coupled Model Intercomparison Project (CMIP) are crucial to evaluate past and future climate change. The reliability of models' simulations is often gauged by their ability to reproduce the historical climate across many time scales. This study compares the global mean surface air temperature from 29 CMIP6 models with observations from three datasets. We examine (1) warming and cooling rates in five subperiods from 1880 to 2014, (2) autocorrelation and long‐term persistence, (3) models' performance based on probabilistic and entropy metrics, and (4) the distributional shape of temperature. All models simulate the observed long‐term warming trend from 1880 to 2014. The late twentieth century warming (1975–2014) and the hiatus (1942–1975) are replicated by most models. The post‐1998 warming is overestimated in 90% of the simulations. Only six out of 29 models reproduce the observed long‐term persistence. All models show differences in distributional shape when compared with observations. Varying performance across metrics reveals the challenge to determine the "best" model. Thus, we argue that models should be selected, based on case‐specific metrics, depending on the intended use. Metrics proposed here facilitate a comprehensive assessment for various applications. Key Points: Global evaluation of mean surface air temperature from 29 CMIP6 models with observations from three data setsCMIP6 assessment based on warming and cooling rates, autocorrelation and long‐term persistence, entropic and distributional shape metricsCMIP6 reproduce well the 1880–2014 warming trend yet simulations and observations differ in distributional and long‐term persistence metrics [ABSTRACT FROM AUTHOR]

In this study, the climate changes over Arid Central Asia (ACA) during the mid‐Holocene (approximately 6,000 calendar years ago, MH) and the Last Glacial Maximum (approximately 21,000 calendar years ago, LGM) were investigated using multimodel simulations derived from the Paleoclimate Modelling Intercomparison Project Phase 3 (PMIP3). During the MH, the multimodel median (MMM) shows that in the core region of ACA, the regionally averaged annual surface air temperature (SAT) decreases by 0.13°C and annual precipitation decreases by 3.45%, compared with the preindustrial (PI) climate. The MMM of the SAT increases by 1.67/0.13°C in summer/autumn, whereas it decreases by 1.23/1.11°C in spring/winter. The amplitude of the seasonal cycles of the SAT increases over ACA due to different MH orbital parameters. For precipitation, the regionally averaged MMM decreases by 5.77%/5.69%/0.39%/5.24% in spring/summer/autumn/winter, respectively. Based on the analysis of the aridity index (AI), compared with the PI, a drier climate appears in southern Central Asia and western Xinjiang due to decreasing precipitation. During the LGM, the MMM shows that the regionally averaged SAT decreases by 5.04/4.36/4.70/5.12/5.88°C and precipitation decreases by 27.78%/28.16%/31.56%/27.74%/23.29% annually and in the spring, summer, autumn, and winter, respectively. Robust drying occurs throughout almost the whole core area. Decreasing precipitation plays a dominant role in shaping the drier conditions, whereas strong cooling plays a secondary but opposite role. In response to the LGM external forcings, over Central Asia and Xinjiang, the seasonal cycle of precipitation has a smaller amplitude compared with that under the PI climate. In the model‐data comparison, the simulated MH moisture changes over ACA are to some extent consistent with the reconstructions, further confirming that drier conditions occurred during that period than during the PI. [ABSTRACT FROM AUTHOR]

Attribution and prediction of global and regional warming requires a better understanding of the magnitude and spatial characteristics of internal global mean surface air temperature (GMST) variability. We examine interdecadal GMST variability in Coupled Modeling Intercomparison Projects, Phases 3, 5, and 6 (CMIP3, CMIP5, and CMIP6) preindustrial control (piControl), last millennium, and historical simulations and in observational data. We find that several CMIP6 simulations show more GMST interdecadal variability than the previous generations of model simulations. Nonetheless, we find that 100‐year trends in CMIP6 piControl simulations never exceed the maximum observed warming trend. Furthermore, interdecadal GMST variability in the unforced piControl simulations is associated with regional variability in the high latitudes and the east Pacific, whereas interdecadal GMST variability in instrumental data and in historical simulations with external forcing is more globally coherent and is associated with variability in tropical deep convective regions. Plain Language Summary: Ongoing and future global and regional warming will progress as a combination of internal climate variability and forced climate change. Understanding the magnitude and spatial patterns associated with internal climate variability is an important aspect of being able to predict when, where, and how climate change will be felt around the globe. Here, we show that the latest climate model simulations, which will be used in the Intergovernmental Panel on Climate Change (IPCC) Assessment Report 6 (AR6), simulate a large range in magnitudes of internal global mean temperature variability. Although there are large unforced global temperature trends in some models, we find that even the most variable models never generate unforced global temperature trends equal to the recently observed global warming trends forced by greenhouse gas emissions. We examine the regions associated with internal climate variability and forced climate change in climate model simulations and find that only forced simulations show a pattern of warming consistent with instrumental data. Key Points: Several CMIP6 control simulations show more interdecadal global mean temperature variability than previous model generationsEven the most variable CMIP6 models never show century‐length global mean temperature trends that exceed observed warming trendsUnlike in control simulations, observed global mean temperature variability is coherent with variability in tropical convective regions [ABSTRACT FROM AUTHOR]

This study quantifies future changes in the polar cold air mass (PCAM) below a threshold potential temperature of 280 K and its horizontal fluxes, which are an indicator for the intensity of cold air outbreaks, by analyzing the outputs from five climate models from the Fifth Coupled Model Intercomparison Project. Under the business‐as‐usual scenario, the multimodel means of the wintertime total hemispheric PCAM decrease by 37% in the Northern Hemisphere and by 34% in the Southern Hemisphere by the end of the 21st century. The midlatitude equatorward PCAM fluxes are projected to decrease significantly by around 2050. This is primarily due to the reduction of the PCAM and to a lesser extent due to the weakening of the extratropical meridional circulation. Finally, intermodel differences in the total hemispheric PCAM reduction are shown to be linearly related to the climate sensitivity to the global mean surface air temperature. Plain Language Summary: In winter, the polar regions are reservoirs of extremely cold air masses. Sometimes, the polar cold air outflows to the midlatitudes and results in very cold weather. This study quantitatively evaluated the future changes in the polar cold air mass and its outflow to the midlatitudes in the winter hemispheres. We found that both the polar cold air mass and its outflow are projected to decrease significantly by the end of the 21st century in the Northern and Southern Hemispheres due to the increase in the global mean surface temperature. These findings provide a possible scenario of future polar climate change. Key Points: Under the RCP8.5 scenario, the polar cold air mass is projected to decrease by 37% in the boreal winter by the end of 21st centuryThe equatorward polar cold air mass fluxes in the midlatitudes are projected to decrease significantly by around 2050Thermodynamical (dynamical) change is the main (secondary) contributor to the projected reduction in the polar cold air mass fluxes [ABSTRACT FROM AUTHOR]

This study investigates the relative contribution of internal variability and external forcings on summer (June–August) surface air temperature (SAT) over the central Indian landmass. Here we use Community Earth System Model Large Ensemble (CESM‐LE) data to assess the historical (1966–2005) and future (2010–2060) climate change in presence of internal climate variability. The summer SAT trend during the historical period exhibits an amplified cooling (<−3°C), whereas a warming trend (≥4°C) is projected in all the ensemble members under RCP8.5 scenario. The total trend is then partitioned into contributions from the externally forced response and internal climatic variability. Over the Indian region, the external forcing displays a strong cooling trend during the historical period and warming trend under RCP8.5 scenario. On the other hand, natural variability displays mainly cooling trends and it introduces a wide range of uncertainty to the future projection in climate models. In historical period, the signal‐to‐noise ratio (SNR), that is, ratio of external forcings and internal variability, is less than 1, which indicates that the internal climatic variability dominates over the forced response. But in future decades the SNR is much higher than 1, that is, external forcing overrides the internal variability. However, to a greater extent natural variability will mask the warming trend over the Indian region, even under RCP8.5 scenario. [ABSTRACT FROM AUTHOR]

We present an analysis of the global annual mean surface temperature anomaly in 2014, 2015, and 2016 based on five datasets of historical observational records of surface temperature. These three years are the three warmest on record in all but one of the datasets. The largest warming occurred over land, especially at high latitudes. Since the strong El Niño event that occurred in 2015/2016 was similar to the 1997/1998 El Niño, we compared the 2014–2016 period with 1998, the warmest year in the 20th century. The contribution to the annual mean surface temperature anomaly of climate variations at different time scales was assessed using ensemble empirical mode decomposition. Results based on the HadCRUT4 dataset suggest that the interannual component may have contributed an anomaly of −0.01°C in 2014, 0.12°C in 2015, and 0.06°C in 2016. These values are substantially lower than the contribution in 1998 (0.18°C). In comparison, the combined contribution of the decadal‐to‐multidecadal (DM) component and the long‐term warming trend was 0.64°C in 2014, 0.70°C in 2015, and 0.77°C in 2016, which are substantially greater than that in 1998 (0.41°C). Similar results were obtained using the other four datasets. The larger contribution from the DM component and the long‐term warming trend implies that warmer years like 2014–2016 may occur more frequently in the near future. We conclude that the so‐called warming hiatus has faded away. [ABSTRACT FROM AUTHOR]

Neutral temperature is the temperature at which people feel neither cool nor warm. Previous regional studies have found that neutral temperature can change with season, which can be attributed to the psychological adaptation effect of people. However, few studies have examined whether the adaptation pattern can be observed across wide geographical and climatic regions. In some cases, outdoor comfort surveys cannot determine neutral temperatures because thermal sensation votes obtained do not reach neutrality. It reflects that thermal adaptation has a temperature limit, and thermal neutrality can hardly be achieved outside the limit. This study aims to establish linear models between neutral temperature and background temperature (survey‐period and climate normal) using neutral temperature data reported in international studies. The limit of thermal neutrality will also be determined in the best‐fit model. The neutral temperature data in 19 empirical outdoor thermal comfort studies published between 2009 and 2017 were used in the linear regression models. Survey‐period mean air temperature was found to be the best predictor of neutral temperature (R2 = 0.539, p = 0.007). The effect of long‐term acclimatization may be less important than it was previously thought because survey‐period temperature can explain over a half of the variability in neutral temperature. The temperature limit of thermal neutrality was defined by the lowest and the highest neutral temperatures in the best‐fit model; the limit was between 12.9 and 32.3 °C in air temperature. The findings indicate that the adaptation effect has a worldwide pattern as neutral temperature always lies closely to background air temperature. The outdoor neutral temperatures reported in international studies show a linear pattern with survey‐period mean air temperature. This indicates human ability to adapt to changing weather condition. The adaptation ability, however, has a limit. Neutral temperature could not be determined when mean air temperature is below 12.9 °C (case 26) or above 32.3 °C (case 29) because most respondents would feel cooler or warmer than neutral. Increasing thermal discomfort will be observed beyond the limits. [ABSTRACT FROM AUTHOR]

ABSTRACT Climate change has focused attention on the effects of changing temperature, particularly the effect on human health. Thus, robust and accurate spatially and temporally resolved air temperature ( Ta) data are of particular importance in the field of epidemiology and public health. However, most health studies to date have matched people to the nearest monitor. In this study, we aimed to develop a robust satellite-based spatio-temporally resolved Ta estimation model across the complex geo-climatic regions of France resulting in daily high-resolution 1 km predicted air temperature ( Tap) estimations. We use a daily calibration approach using a series of processes to generate daily Tap for every day across the entire study area and period. First, we start by calibrating MODIS (Moderate Resolution Imaging Spectroradiometer) satellite-gridded surface temperature ( Ts) data against Ta collected within 1 km of the Ts centroid. The calibration stage adjusted for spatio-temporal predictors, as done in environmental exposure assessment methods such as land use regressions. Second, to estimate Tap when no Ts data are available we fit a second model which uses the association of predicted grid cells Tap values (based on satellite Ts) with surrounding Ta monitors and the association with values in neighbouring grid cells. Out-of-sample tenfold cross-validation was used to quantify the accuracy of our predictions. Our model performance was excellent for both days with available Ts and days without Ts observations (overall mean out-of-sample R2 = 0.95 for both stages). In conclusion, we demonstrate how Ts can be used reliably to predict daily Tap at high-resolution across France for use in studies looking at the effects of fine resolution Ta exposure on various health outcomes. [ABSTRACT FROM AUTHOR]

Elevated CO2 and temperature strongly affect crop production, but understanding of the crop response to combined CO2 and temperature increases under field conditions is still limited while data are scarce. We grew wheat ( Triticum aestivum L.) and rice ( Oryza sativa L.) under two levels of CO2 (ambient and enriched up to 500 μmol mol−1) and two levels of canopy temperature (ambient and increased by 1.5-2.0 °C) in free-air CO2 enrichment (FACE) systems and carried out a detailed growth and yield component analysis during two growing seasons for both crops. An increase in CO2 resulted in higher grain yield, whereas an increase in temperature reduced grain yield, in both crops. An increase in CO2 was unable to compensate for the negative impact of an increase in temperature on biomass and yield of wheat and rice. Yields of wheat and rice were decreased by 10-12% and 17-35%, respectively, under the combination of elevated CO2 and temperature. The number of filled grains per unit area was the most important yield component accounting for the effects of elevated CO2 and temperature in wheat and rice. Our data showed complex treatment effects on the interplay between preheading duration, nitrogen uptake, tillering, leaf area index, and radiation-use efficiency, and thus on yield components and yield. Nitrogen uptake before heading was crucial in minimizing yield loss due to climate change in both crops. For rice, however, a breeding strategy to increase grain number per m2 and % filled grains (or to reduce spikelet sterility) at high temperature is also required to prevent yield reduction under conditions of global change. [ABSTRACT FROM AUTHOR]

ABSTRACT The surface and tropospheric temperature trends have been evaluated over Armenia. A very clear signal of warming is detected at most stations of Armenia for spring, summer and autumn seasons for the period 1979-2012 compared to 1961-1994. Overall, the observed data and the reanalyses products ( ERA-40 and ERA-Interim) provide a generally consistent picture for tropospheric temperature trends over Armenia. The trends obtained from the three sources of data show significantly faster warming at the near-surface troposphere (at 850 hPa) for the period 1979-2012 compared to 1961-1994. However, temperature trends at 850 hPa are underestimated by the reanalysis products relative to the observed data. The warming rates decrease with height, and negative and nearly zero trends are observed in the middle and upper troposphere in most cases. [ABSTRACT FROM AUTHOR]

This article explores the response of convective-scale atmospheric characteristics to surface temperature through the lens of large-domain, cloud-system-resolving model experiments run at radiative convective equilibrium. We note several features reminiscent of the response to surface warming in atmospheric general circulation models. These include an increase in the rain rate that is smaller than the modelled increase in precipitable water, a systematic decrease in sensible heating and an increase in clear-sky cooling. However, in contrast to climate models, we note that tropospheric relative humidity increases and column-integrated water vapour increases at the rate anticipated from the Clausius-Clapeyron relationship, but only when compared with the troposphere mean temperature rather than surface temperature. Also shown are results elucidating the changes in the vertically integrated water budget and the distribution of high precipitation rates shifting toward higher rates. Moist static energy distributions are analyzed and, from these, clouds are implicated in effecting the final equilibrium state of the atmosphere. The results indicate that, while there are aspects of the tropical equilibrium that are represented realistically in current general circulation model climate-change experiments, there are potentially influential local interactions that are sufficiently important as to alter the mean response of the tropical water and energy balance to changes in sea-surface temperature. Convection is shown to dictate the equilibrium state across all scales, including those unresolved in climate models, rather than only responding to surface-induced changes. [ABSTRACT FROM AUTHOR]

ABSTRACT The NASA announcement of record surface melting of the Greenland ice sheet in July 2012 led us to examine the atmospheric and oceanic climatic anomalies that are likely to have contributed to these exceptional conditions and also to ask the question of how unusual these anomalies were compared to available records. Our analysis allows us to assess the relative contributions of these two key influences to both the extreme melt event and ongoing climate change. In 2012, as in recent warm summers since 2007, a blocking high pressure feature, associated with negative NAO conditions, was present in the mid-troposphere over Greenland for much of the summer. This circulation pattern advected relatively warm southerly winds over the western flank of the ice sheet, forming a 'heat dome' over Greenland that led to the widespread surface melting. Both sea-surface temperature and sea-ice cover anomalies seem to have played a minimal role in this record melt, relative to atmospheric circulation. Two representative coastal climatological station averages and several individual stations in south, west and north-west Greenland set new surface air temperature records for May, June, July and the whole (JJA) summer. The unusually warm summer 2012 conditions extended to the top of the ice sheet at Summit, where our reanalysed (1994-2012) DMI Summit weather station summer (JJA) temperature series set new record high mean and extreme temperatures in 2012; 3-hourly instantaneous 2-m temperatures reached an exceptional value of 2.2°C at Summit on 11 July 2012. These conditions translated into the record observed ice-sheet wide melt during summer 2012. However, 2012 seems not to be climatically representative of future 'average' summers projected this century. [ABSTRACT FROM AUTHOR]